1
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Hermosilla AM, Berner C, Ovchinnikov S, Vorobieva AA. Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting. Protein Sci 2024; 33:e5033. [PMID: 38864690 PMCID: PMC11168064 DOI: 10.1002/pro.5033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/14/2024] [Accepted: 05/08/2024] [Indexed: 06/13/2024]
Abstract
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.
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Affiliation(s)
- Alvaro Martin Hermosilla
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Carolin Berner
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship ProgramHarvard UniversityCambridgeMassachusettsUSA
- Present address:
Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Anastassia A. Vorobieva
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
- VIB Center for AI and Computational BiologyBelgium
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2
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble and functional membrane protein analogues. Nature 2024:10.1038/s41586-024-07601-y. [PMID: 38898281 DOI: 10.1038/s41586-024-07601-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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Affiliation(s)
- Casper A Goverde
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martin Pacesa
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Goldbach
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lars J Dornfeld
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra E M Balbi
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sandrine Georgeon
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stéphane Rosset
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Srajan Kapoor
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Jagrity Choudhury
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Christian Schellhaas
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Simon Kozlov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alex J Vecchio
- Department of Structural Biology, University at Buffalo, Buffalo, NY, USA
| | - Bruno E Correia
- Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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3
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Dai Y, Kretz CA, Kim PY, Gross PL. A specific fluorescence resonance energy quenching-based biosensor for measuring thrombin activity in whole blood. J Thromb Haemost 2024; 22:1627-1639. [PMID: 38382740 DOI: 10.1016/j.jtha.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND At sites of vessel injury, thrombin acts as the central mediator of coagulation by catalyzing fibrin clot formation and platelet activation. Thrombin generation is most frequently measured in plasma samples using small-molecule substrates; however, these have low specificity for thrombin and limited utility in whole blood. Plasma assays are limited because they ignore the hemostatic contributions of blood cells and require anticoagulation and the addition of supraphysiological concentrations of calcium. OBJECTIVES To overcome these limitations, we designed and characterized a fluorescence resonance energy quenching-based thrombin sensor (FTS) protein. METHODS The fluorescence resonance energy quenching pair of mAmetrine and tTomato, separated by a thrombin recognition sequence, was developed. The protein was expressed using Escherichia coli, and purity was assessed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The cleavage of FTS was monitored by fluorescence using excitation at 406 nm and emission at 526 nm and 581 nm. RESULTS Compared with small-molecule substrates, the FTS demonstrated high specificity for thrombin; it is not cleaved by thrombin or inhibited by α2-macroglobulin and interacts with thrombin's anion-binding exosite I. The FTS can effectively measure thrombin generation in plasma and in finger-prick whole blood, which allows it to be developed into a point-of-care test of thrombin generation. The FTS does not inhibit standard thrombin-generation assays. Lastly, FTS-based thrombin generation in nonanticoagulated finger-prick blood was delayed but enhanced compared with that in citrated plasma. CONCLUSION The FTS will broaden our understanding of thrombin generation in ways that are not attainable with current methods.
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Affiliation(s)
- Ying Dai
- Hamilton Health Sciences and Departments of Medicine and Medical Sciences, Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Colin A Kretz
- Hamilton Health Sciences and Departments of Medicine and Medical Sciences, Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Paul Y Kim
- Hamilton Health Sciences and Departments of Medicine and Medical Sciences, Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, Ontario, Canada
| | - Peter L Gross
- Hamilton Health Sciences and Departments of Medicine and Medical Sciences, Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton, Ontario, Canada.
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4
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Beck J, Shanmugaratnam S, Höcker B. Diversifying de novo TIM barrels by hallucination. Protein Sci 2024; 33:e5001. [PMID: 38723111 PMCID: PMC11081422 DOI: 10.1002/pro.5001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/26/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024]
Abstract
De novo protein design expands the protein universe by creating new sequences to accomplish tailor-made enzymes in the future. A promising topology to implement diverse enzyme functions is the ubiquitous TIM-barrel fold. Since the initial de novo design of an idealized four-fold symmetric TIM barrel, the family of de novo TIM barrels is expanding rapidly. Despite this and in contrast to natural TIM barrels, these novel proteins lack cavities and structural elements essential for the incorporation of binding sites or enzymatic functions. In this work, we diversified a de novo TIM barrel by extending multiple βα-loops using constrained hallucination. Experimentally tested designs were found to be soluble upon expression in Escherichia coli and well-behaved. Biochemical characterization and crystal structures revealed successful extensions with defined α-helical structures. These diversified de novo TIM barrels provide a framework to explore a broad spectrum of functions based on the potential of natural TIM barrels.
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Affiliation(s)
- Julian Beck
- Department of BiochemistryUniversity of BayreuthBayreuthGermany
| | | | - Birte Höcker
- Department of BiochemistryUniversity of BayreuthBayreuthGermany
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5
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Dudley JA, Park S, Cho O, Wells NGM, MacDonald ME, Blejec KM, Fetene E, Zanderigo E, Houliston S, Liddle JC, Dashnaw CM, Sabo TM, Shaw BF, Balsbaugh JL, Rocklin GJ, Smith CA. Heat-induced structural and chemical changes to a computationally designed miniprotein. Protein Sci 2024; 33:e4991. [PMID: 38757381 PMCID: PMC11099715 DOI: 10.1002/pro.4991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 05/18/2024]
Abstract
The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.
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Affiliation(s)
- Joshua A. Dudley
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
| | - Sojeong Park
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
| | - Oliver Cho
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
| | | | | | | | - Emmanuel Fetene
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
| | - Eric Zanderigo
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
| | - Scott Houliston
- Structural Genomics ConsortiumUniversity of TorontoTorontoOntarioCanada
| | - Jennifer C. Liddle
- Proteomics and Metabolomics FacilityUniversity of ConnecticutStorrsConnecticutUSA
| | - Chad M. Dashnaw
- Department of Chemistry and BiochemistryBaylor UniversityWacoTexasUSA
| | - T. Michael Sabo
- Department of Medicine and Brown Cancer CenterUniversity of LouisvilleLouisvilleKentuckyUSA
| | - Bryan F. Shaw
- Department of Chemistry and BiochemistryBaylor UniversityWacoTexasUSA
| | - Jeremy L. Balsbaugh
- Proteomics and Metabolomics FacilityUniversity of ConnecticutStorrsConnecticutUSA
| | - Gabriel J. Rocklin
- Department of Pharmacology and Center for Synthetic BiologyNorthwestern UniversityEvanstonIllinoisUSA
| | - Colin A. Smith
- Department of ChemistryWesleyan UniversityMiddletownConnecticutUSA
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6
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Minoshima M, Reja SI, Hashimoto R, Iijima K, Kikuchi K. Hybrid Small-Molecule/Protein Fluorescent Probes. Chem Rev 2024; 124:6198-6270. [PMID: 38717865 DOI: 10.1021/acs.chemrev.3c00549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
Hybrid small-molecule/protein fluorescent probes are powerful tools for visualizing protein localization and function in living cells. These hybrid probes are constructed by diverse site-specific chemical protein labeling approaches through chemical reactions to exogenous peptide/small protein tags, enzymatic post-translational modifications, bioorthogonal reactions for genetically incorporated unnatural amino acids, and ligand-directed chemical reactions. The hybrid small-molecule/protein fluorescent probes are employed for imaging protein trafficking, conformational changes, and bioanalytes surrounding proteins. In addition, fluorescent hybrid probes facilitate visualization of protein dynamics at the single-molecule level and the defined structure with super-resolution imaging. In this review, we discuss development and the bioimaging applications of fluorescent probes based on small-molecule/protein hybrids.
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Affiliation(s)
- Masafumi Minoshima
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka 5650871, Japan
| | - Shahi Imam Reja
- Immunology Frontier Research Center, Osaka University, 2-1, Yamadaoka, Suita, Osaka 5650871, Japan
| | - Ryu Hashimoto
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka 5650871, Japan
| | - Kohei Iijima
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka 5650871, Japan
| | - Kazuya Kikuchi
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka 5650871, Japan
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7
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Zhang Z, Shen W, Liu Q, Zitnik M. PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581968. [PMID: 38464121 PMCID: PMC10925136 DOI: 10.1101/2024.02.25.581968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing small-molecule-binding proteins, such as enzymes and biosensors, is essential in protein biology and bioengineering. Generating high-fidelity protein pockets-areas where proteins interact with ligand molecules-is challenging due to the complex interactions between ligand molecules and proteins, the flexibility of ligand molecules and amino acid side chains, and intricate sequence-structure dependencies. We introduce PocketGen, a deep generative method that produces the residue sequence and the full-atom structure within the protein pocket region, leveraging sequence-structure consistency. PocketGen comprises a bilevel graph transformer for structural encoding and a sequence refinement module utilizing a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) through bilevel attention mechanisms. A structural adapter employing cross-attention is integrated into the pLM for sequence refinement to ensure consistency between structure-based and sequence-based prediction. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments demonstrate that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with an amino acid recovery rate exceeding 64%.
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8
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Adams CS, Kim H, Burtner AE, Lee DS, Dobbins C, Criswell C, Coventry B, Kim HM, King NP. De novo design of protein minibinder agonists of TLR3. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.17.589973. [PMID: 38659926 PMCID: PMC11042314 DOI: 10.1101/2024.04.17.589973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Toll-like Receptor 3 (TLR3) is a pattern recognition receptor that initiates antiviral immune responses upon binding double-stranded RNA (dsRNA). Several nucleic acid-based TLR3 agonists have been explored clinically as vaccine adjuvants in cancer and infectious disease, but present substantial manufacturing and formulation challenges. Here, we use computational protein design to create novel miniproteins that bind to human TLR3 with nanomolar affinities. Cryo-EM structures of two minibinders in complex with TLR3 reveal that they bind the target as designed, although one partially unfolds due to steric competition with a nearby N-linked glycan. Multimeric forms of both minibinders induce NF-κB signaling in TLR3-expressing cell lines, demonstrating that they may have therapeutically relevant biological activity. Our work provides a foundation for the development of specific, stable, and easy-to-formulate protein-based agonists of TLRs and other pattern recognition receptors.
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Affiliation(s)
- Chloe S. Adams
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
| | - Hyojin Kim
- Center for Biomolecular & Cellular Structure, Institute for Basic Science (IBS), Daejeon 34126, South Korea
| | - Abigail E. Burtner
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
| | - Dong Sun Lee
- Center for Biomolecular & Cellular Structure, Institute for Basic Science (IBS), Daejeon 34126, South Korea
| | - Craig Dobbins
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
| | - Cameron Criswell
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
| | - Brian Coventry
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
| | - Ho Min Kim
- Center for Biomolecular & Cellular Structure, Institute for Basic Science (IBS), Daejeon 34126, South Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea
| | - Neil P. King
- Institute for Protein Design, University of Washington, Seattle, WA, 98195 USA
- Department of Biochemistry, University of Washington, Seattle, WA, 98195 USA
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9
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Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 2024; 384:106-112. [PMID: 38574125 DOI: 10.1126/science.adl5364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/28/2024] [Indexed: 04/06/2024]
Abstract
The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.
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Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Samuel I Mann
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
- Department of Chemistry, University of California, Riverside, CA 92521, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xiaofang Zhong
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
| | - Dimitrios Gazgalis
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Morgan E Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Nicholas F Polizzi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
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10
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Xu Y, Hu X, Wang C, Liu Y, Chen Q, Liu H. De novo design of cavity-containing proteins with a backbone-centered neural network energy function. Structure 2024; 32:424-432.e4. [PMID: 38325370 DOI: 10.1016/j.str.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 10/04/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.
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Affiliation(s)
- Yang Xu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Xiuhong Hu
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Chenchen Wang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yongrui Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Quan Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Centre for Advanced Interdisciplinary Science and Biomedicine of IHM, Hefei National Center for Interdisciplinary Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China; Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China.
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China; Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China; School of Data Science, University of Science and Technology of China, Hefei, Anhui 230027, China.
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11
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Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00718-y. [PMID: 38565617 DOI: 10.1038/s41580-024-00718-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
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Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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12
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Pham TL, Thomas F. Design of Functional Globular β-Sheet Miniproteins. Chembiochem 2024; 25:e202300745. [PMID: 38275210 DOI: 10.1002/cbic.202300745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 01/27/2024]
Abstract
The design of discrete β-sheet peptides is far less advanced than e. g. the design of α-helical peptides. The reputation of β-sheet peptides as being poorly soluble and aggregation-prone often hinders active design efforts. Here, we show that this reputation is unfounded. We demonstrate this by looking at the β-hairpin and WW domain. Their structure and folding have been extensively studied and they have long served as model systems to investigate protein folding and folding kinetics. The resulting fundamental understanding has led to the development of hyperstable β-sheet scaffolds that fold at temperatures of 100 °C or high concentrations of denaturants. These have been used to design functional miniproteins with protein or nucleic acid binding properties, in some cases with such success that medical applications are conceivable. The β-sheet scaffolds are not always completely rigid, but can be specifically designed to respond to changes in pH, redox potential or presence of metal ions. Some engineered β-sheet peptides also exhibit catalytic properties, although not comparable to those of natural proteins. Previous reviews have focused on the design of stably folded and non-aggregating β-sheet sequences. In our review, we now also address design strategies to obtain functional miniproteins from β-sheet folding motifs.
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Affiliation(s)
- Truc Lam Pham
- Truc Lam Pham, Prof. Dr. Franziska Thomas, Institute of Organic Chemistry, Heidelberg University, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
| | - Franziska Thomas
- Truc Lam Pham, Prof. Dr. Franziska Thomas, Institute of Organic Chemistry, Heidelberg University, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
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13
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Goverde CA, Pacesa M, Goldbach N, Dornfeld LJ, Balbi PEM, Georgeon S, Rosset S, Kapoor S, Choudhury J, Dauparas J, Schellhaas C, Kozlov S, Baker D, Ovchinnikov S, Vecchio AJ, Correia BE. Computational design of soluble functional analogues of integral membrane proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.09.540044. [PMID: 38496615 PMCID: PMC10942269 DOI: 10.1101/2023.05.09.540044] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.
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14
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Peng Z, Iwabuchi S, Izumi K, Takiguchi S, Yamaji M, Fujita S, Suzuki H, Kambara F, Fukasawa G, Cooney A, Di Michele L, Elani Y, Matsuura T, Kawano R. Lipid vesicle-based molecular robots. LAB ON A CHIP 2024; 24:996-1029. [PMID: 38239102 PMCID: PMC10898420 DOI: 10.1039/d3lc00860f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
A molecular robot, which is a system comprised of one or more molecular machines and computers, can execute sophisticated tasks in many fields that span from nanomedicine to green nanotechnology. The core parts of molecular robots are fairly consistent from system to system and always include (i) a body to encapsulate molecular machines, (ii) sensors to capture signals, (iii) computers to make decisions, and (iv) actuators to perform tasks. This review aims to provide an overview of approaches and considerations to develop molecular robots. We first introduce the basic technologies required for constructing the core parts of molecular robots, describe the recent progress towards achieving higher functionality, and subsequently discuss the current challenges and outlook. We also highlight the applications of molecular robots in sensing biomarkers, signal communications with living cells, and conversion of energy. Although molecular robots are still in their infancy, they will unquestionably initiate massive change in biomedical and environmental technology in the not too distant future.
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Affiliation(s)
- Zugui Peng
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Shoji Iwabuchi
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Kayano Izumi
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Sotaro Takiguchi
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Misa Yamaji
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Shoko Fujita
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Harune Suzuki
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Fumika Kambara
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
| | - Genki Fukasawa
- School of Life Science and Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-Ku, Tokyo 152-8550, Japan
| | - Aileen Cooney
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK
| | - Lorenzo Di Michele
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK
- FabriCELL, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK
| | - Yuval Elani
- Department of Chemical Engineering, Imperial College London, South Kensington, London SW7 2AZ, UK
- FabriCELL, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK
| | - Tomoaki Matsuura
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-Ku, Tokyo 152-8550, Japan
| | - Ryuji Kawano
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo185-8588, Japan.
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15
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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16
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Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.
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Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
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17
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Sakuma K, Kobayashi N, Sugiki T, Nagashima T, Fujiwara T, Suzuki K, Kobayashi N, Murata T, Kosugi T, Tatsumi-Koga R, Koga N. Design of complicated all-α protein structures. Nat Struct Mol Biol 2024; 31:275-282. [PMID: 38177681 DOI: 10.1038/s41594-023-01147-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/04/2023] [Indexed: 01/06/2024]
Abstract
A wide range of de novo protein structure designs have been achieved, but the complexity of naturally occurring protein structures is still far beyond these designs. Here, to expand the diversity and complexity of de novo designed protein structures, we sought to develop a method for designing 'difficult-to-describe' α-helical protein structures composed of irregularly aligned α-helices like globins. Backbone structure libraries consisting of a myriad of α-helical structures with five or six helices were generated by combining 18 helix-loop-helix motifs and canonical α-helices, and five distinct topologies were selected for de novo design. The designs were found to be monomeric with high thermal stability in solution and fold into the target topologies with atomic accuracy. This study demonstrated that complicated α-helical proteins are created using typical building blocks. The method we developed will enable us to explore the universe of protein structures for designing novel functional proteins.
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Affiliation(s)
- Koya Sakuma
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Naohiro Kobayashi
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
- Institute for Protein Research, Osaka University, Suita, Japan
| | | | - Toshio Nagashima
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | | | - Kano Suzuki
- Department of Chemistry, Graduate School of Science, Chiba University, Chiba, Japan
| | - Naoya Kobayashi
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of National Sciences, Okazaki, Japan
| | - Takeshi Murata
- Department of Chemistry, Graduate School of Science, Chiba University, Chiba, Japan
- Membrane Protein Research Center, Chiba University, Chiba, Japan
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Japan
| | - Takahiro Kosugi
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of National Sciences, Okazaki, Japan
- Research Center of Integrative Molecular Systems, Institute for Molecular Science, National Institutes of National Sciences, Okazaki, Japan
| | - Rie Tatsumi-Koga
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of National Sciences, Okazaki, Japan
| | - Nobuyasu Koga
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of National Sciences, Okazaki, Japan.
- Research Center of Integrative Molecular Systems, Institute for Molecular Science, National Institutes of National Sciences, Okazaki, Japan.
- Institute for Protein Research, Osaka University, Suita, Japan.
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18
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Satalkar V, Degaga GD, Li W, Pang YT, McShan AC, Gumbart JC, Mitchell JC, Torres MP. Generative β-hairpin design using a residue-based physicochemical property landscape. Biophys J 2024:S0006-3495(24)00070-5. [PMID: 38297834 DOI: 10.1016/j.bpj.2024.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/20/2023] [Accepted: 01/25/2024] [Indexed: 02/02/2024] Open
Abstract
De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.
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Affiliation(s)
- Vardhan Satalkar
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Gemechis D Degaga
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Wei Li
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Yui Tik Pang
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - James C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
| | - Matthew P Torres
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia.
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19
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Dolorfino M, Samanta R, Vorobieva A. ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575764. [PMID: 38352434 PMCID: PMC10862708 DOI: 10.1101/2024.01.16.575764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.
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Affiliation(s)
- Marissa Dolorfino
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
| | | | - Anastassia Vorobieva
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
- VIB Center for AI and Computational Biology, Belgium
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20
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Kortemme T. De novo protein design-From new structures to programmable functions. Cell 2024; 187:526-544. [PMID: 38306980 PMCID: PMC10990048 DOI: 10.1016/j.cell.2023.12.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/03/2023] [Accepted: 12/19/2023] [Indexed: 02/04/2024]
Abstract
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
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Affiliation(s)
- Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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21
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Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573178. [PMID: 38187746 PMCID: PMC10769398 DOI: 10.1101/2023.12.23.573178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.
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Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Samuel I. Mann
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Dimitrios Gazgalis
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Morgan E. Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | | | - William F. DeGrado
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
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22
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An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, Juergens D, Dauparas J, Anishchenko I, Coventry B, Bera AK, Kang A, Levine PM, Alvarez V, Pillai A, Norn C, Feldman D, Zorine D, Hicks DR, Li X, Sanchez MG, Vafeados DK, Salveson PJ, Vorobieva AA, Baker D. De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572602. [PMID: 38187589 PMCID: PMC10769206 DOI: 10.1101/2023.12.20.572602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.
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Affiliation(s)
- Linna An
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Meerit Said
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Long Tran
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Asim K. Bera
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M. Levine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Valentina Alvarez
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind Pillai
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Feldman
- BioInnovation Institute, DK2200 Copenhagen N, Denmark
| | - Dmitri Zorine
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R. Hicks
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Dionne K. Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Patrick J. Salveson
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - David Baker
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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23
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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24
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Berhanu S, Majumder S, Müntener T, Whitehouse J, Berner C, Bera AK, Kang A, Liang B, Khan GN, Sankaran B, Tamm LK, Brockwell DJ, Hiller S, Radford SE, Baker D, Vorobieva AA. Sculpting conducting nanopore size and shape through de novo protein design. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572500. [PMID: 38187764 PMCID: PMC10769293 DOI: 10.1101/2023.12.20.572500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Transmembrane β-barrels (TMBs) are widely used for single molecule DNA and RNA sequencing and have considerable potential for a broad range of sensing and sequencing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels such as CsgG, which have evolved to carry out functions very different from sensing, and hence provide sub-optimal starting points. In contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to the design of transmembrane β-barrel pores with different diameter and pore geometry. NMR and crystallographic characterization shows that the designs are stably folded with structures close to the design models. We report the first examples of de novo designed TMBs with 10, 12 and 14 stranded β-barrels. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 pS (~0.5 nm pore diameter) to 430 pS (~1.1 nm pore diameter), and can be converted into sensitive small-molecule sensors with high signal to noise ratio. The capability to generate on demand β-barrel pores of defined geometry opens up fundamentally new opportunities for custom engineering of sequencing and sensing technologies.
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Affiliation(s)
- Samuel Berhanu
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Sagardip Majumder
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - James Whitehouse
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT
| | - Carolin Berner
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
| | - Asim K. Bera
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Binyong Liang
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - G Nasir Khan
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT
| | - Banumathi Sankaran
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Lukas K. Tamm
- Department of Molecular Physiology and Biological Physics and Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, VA 22903, USA
| | - David J. Brockwell
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT
| | | | - Sheena E. Radford
- Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT
| | - David Baker
- Department of Biochemistry, The University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Anastassia A. Vorobieva
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
- VIB Center for AI and Computational Biology, Belgium
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25
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Imamura H, Otsubo S, Nishida M, Takekawa N, Imada K. Red fluorescent proteins engineered from green fluorescent proteins. Proc Natl Acad Sci U S A 2023; 120:e2307687120. [PMID: 37871160 PMCID: PMC10636333 DOI: 10.1073/pnas.2307687120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 08/31/2023] [Indexed: 10/25/2023] Open
Abstract
Fluorescent proteins (FPs) form a fluorophore through autocatalysis from three consecutive amino acid residues within a polypeptide chain. The two major groups, green FPs (GFPs) and red FPs (RFPs), have distinct fluorophore structures; RFPs have an extended π-conjugation system with an additional double bond. However, due to the low sequence homology between the two groups, amino acid residues essential for determining the different fluorophore structures were unclear. Therefore, engineering a GFP into an RFP has been challenging, and the exact mechanism of how GFPs and RFPs achieve different autocatalytic reactions remained elucidated. Here, we show the conversion of two coral GFPs, AzamiGreen (AG) and mcavGFP, into RFPs by defined mutations. Structural comparison of AG and AzamiRed1.0, an AG-derived RFP, revealed that the mutations triggered drastic rearrangements in the interaction networks between amino acid residues around the fluorophore, suggesting that coordinated multisite mutations are required for the green-to-red conversion. As a result of the structural rearrangements, a cavity suitable for the entry of an oxygen molecule, which is necessary for the double bond formation of the red fluorophores, is created in the proximity of the fluorophore. We also show that a monomeric variant of AzamiRed1.0 can be used for labeling organelles and proteins in mammalian cells. Our results provide a structural basis for understanding the red fluorophore formation mechanism and demonstrate that protein engineering of GFPs is a promising way to create RFPs suitable for fluorescent tags.
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Affiliation(s)
- Hiromi Imamura
- Division of Systemic Life Science, Graduate School of Biostudies, Kyoto University, Kyoto606-8501, Japan
| | - Shiho Otsubo
- Department of Macromolecular Science, Graduate School of Science, Osaka University, Toyonaka, Osaka560-0043, Japan
| | - Mizuho Nishida
- Division of Systemic Life Science, Graduate School of Biostudies, Kyoto University, Kyoto606-8501, Japan
| | - Norihiro Takekawa
- Department of Macromolecular Science, Graduate School of Science, Osaka University, Toyonaka, Osaka560-0043, Japan
| | - Katsumi Imada
- Department of Macromolecular Science, Graduate School of Science, Osaka University, Toyonaka, Osaka560-0043, Japan
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26
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Lee GR, Pellock SJ, Norn C, Tischer D, Dauparas J, Anischenko I, Mercer JAM, Kang A, Bera A, Nguyen H, Goreshnik I, Vafeados D, Roullier N, Han HL, Coventry B, Haddox HK, Liu DR, Yeh AHW, Baker D. Small-molecule binding and sensing with a designed protein family. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565201. [PMID: 37961294 PMCID: PMC10635051 DOI: 10.1101/2023.11.01.565201] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
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27
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An L, Hicks DR, Zorine D, Dauparas J, Wicky BIM, Milles LF, Courbet A, Bera AK, Nguyen H, Kang A, Carter L, Baker D. Hallucination of closed repeat proteins containing central pockets. Nat Struct Mol Biol 2023; 30:1755-1760. [PMID: 37770718 PMCID: PMC10643118 DOI: 10.1038/s41594-023-01112-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/28/2023] [Indexed: 09/30/2023]
Abstract
In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes.
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Affiliation(s)
- Linna An
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
| | - Derrick R Hicks
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Dmitri Zorine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Basile I M Wicky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lukas F Milles
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alexis Courbet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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28
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Kosugi T, Iida T, Tanabe M, Iino R, Koga N. Design of allosteric sites into rotary motor V 1-ATPase by restoring lost function of pseudo-active sites. Nat Chem 2023; 15:1591-1598. [PMID: 37414880 PMCID: PMC10624635 DOI: 10.1038/s41557-023-01256-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/26/2023] [Indexed: 07/08/2023]
Abstract
Allostery produces concerted functions of protein complexes by orchestrating the cooperative work between the constituent subunits. Here we describe an approach to create artificial allosteric sites in protein complexes. Certain protein complexes contain subunits with pseudo-active sites, which are believed to have lost functions during evolution. Our hypothesis is that allosteric sites in such protein complexes can be created by restoring the lost functions of pseudo-active sites. We used computational design to restore the lost ATP-binding ability of the pseudo-active site in the B subunit of a rotary molecular motor, V1-ATPase. Single-molecule experiments with X-ray crystallography analyses revealed that binding of ATP to the designed allosteric site boosts this V1's activity compared with the wild-type, and the rotation rate can be tuned by modulating ATP's binding affinity. Pseudo-active sites are widespread in nature, and our approach shows promise as a means of programming allosteric control over concerted functions of protein complexes.
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Affiliation(s)
- Takahiro Kosugi
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan.
| | - Tatsuya Iida
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Mikio Tanabe
- Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Japan
| | - Ryota Iino
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
- Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Nobuyasu Koga
- Research Center of Integrative Molecular Systems (CIMoS), Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Department of Structural Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan.
- Institute for Protein Research (IPR), Osaka University, Suita, Japan.
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29
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Zhang L, Liu H. Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function. J Comput Aided Mol Des 2023; 37:463-478. [PMID: 37498491 DOI: 10.1007/s10822-023-00518-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023]
Abstract
When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.
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Affiliation(s)
- Lu Zhang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, Anhui, China
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, Anhui, China.
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, 230027, Anhui, China.
- School of Data Science, University of Science and Technology of China, Hefei, 230027, Anhui, China.
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30
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Roel-Touris J, Nadal M, Marcos E. Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops. Nat Commun 2023; 14:5939. [PMID: 37741853 PMCID: PMC10517939 DOI: 10.1038/s41467-023-41717-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023] Open
Abstract
Antibody derivatives have sought to recapitulate the antigen binding properties of antibodies, but with improved biophysical attributes convenient for therapeutic, diagnostic and research applications. However, their success has been limited by the naturally occurring structure of the immunoglobulin dimer displaying hypervariable binding loops, which is hard to modify by traditional engineering approaches. Here, we devise geometrical principles for de novo designing single-chain immunoglobulin dimers, as a tunable two-domain architecture that optimizes biophysical properties through more favorable dimer interfaces. Guided by these principles, we computationally designed protein scaffolds that were hyperstable, structurally accurate and robust for accommodating multiple functional loops, both individually and in combination, as confirmed through biochemical assays and X-ray crystallography. We showcase the modularity of this architecture by deep-learning-based diversification, opening up the possibility for tailoring the number, positioning, and relative orientation of ligand-binding loops targeting one or two distal epitopes. Our results provide a route to custom-design robust protein scaffolds for harboring multiple functional loops.
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Affiliation(s)
- Jorge Roel-Touris
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB), CSIC, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Marta Nadal
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB), CSIC, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Enrique Marcos
- Protein Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB), CSIC, Baldiri Reixac 10, 08028, Barcelona, Spain.
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31
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Huang B, Abedi M, Ahn G, Coventry B, Sappington I, Wang R, Schlichthaerle T, Zhang JZ, Wang Y, Goreshnik I, Chiu CW, Chazin-Gray A, Chan S, Gerben S, Murray A, Wang S, O'Neill J, Yeh R, Misquith A, Wolf A, Tomasovic LM, Piraner DI, Gonzalez MJD, Bennett NR, Venkatesh P, Satoe D, Ahlrichs M, Dobbins C, Yang W, Wang X, Vafeados D, Mout R, Shivaei S, Cao L, Carter L, Stewart L, Spangler JB, Bernardes GJL, Roybal KT, Greisen P, Li X, Bertozzi C, Baker D. Designed Endocytosis-Triggering Proteins mediate Targeted Degradation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.19.553321. [PMID: 37781607 PMCID: PMC10541094 DOI: 10.1101/2023.08.19.553321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.
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Affiliation(s)
- Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Mohamad Abedi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Green Ahn
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Isaac Sappington
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Rong Wang
- Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thomas Schlichthaerle
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jason Z Zhang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yujia Wang
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Ching Wen Chiu
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Adam Chazin-Gray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Sidney Chan
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey Gerben
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Shunzhi Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | | | | | | | - Luke M Tomasovic
- Departments of Biomedical Engineering and Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dan I Piraner
- Department of Microbiology and Immunology, University of California San Francisco
| | | | - Nathaniel R Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Danny Satoe
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Maggie Ahlrichs
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Craig Dobbins
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinru Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Dionne Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Rubul Mout
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Shirin Shivaei
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Longxing Cao
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Lauren Carter
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jamie B Spangler
- Departments of Biomedical Engineering and Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Gonçalo J L Bernardes
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Kole T Roybal
- Department of Microbiology and Immunology, University of California San Francisco
| | | | - Xiaochun Li
- Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Carolyn Bertozzi
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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32
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Minami S, Kobayashi N, Sugiki T, Nagashima T, Fujiwara T, Tatsumi-Koga R, Chikenji G, Koga N. Exploration of novel αβ-protein folds through de novo design. Nat Struct Mol Biol 2023; 30:1132-1140. [PMID: 37400653 PMCID: PMC10442233 DOI: 10.1038/s41594-023-01029-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
A fundamental question in protein evolution is whether nature has exhaustively sampled nearly all possible protein folds throughout evolution, or whether a large fraction of the possible folds remains unexplored. To address this question, we defined a set of rules for β-sheet topology to predict novel αβ-folds and carried out a systematic de novo protein design exploration of the novel αβ-folds predicted by the rules. The designs for all eight of the predicted novel αβ-folds with a four-stranded β-sheet, including a knot-forming one, folded into structures close to the design models. Further, the rules predicted more than 10,000 novel αβ-folds with five- to eight-stranded β-sheets; this number far exceeds the number of αβ-folds observed in nature so far. This result suggests that a vast number of αβ-folds are possible, but have not emerged or have become extinct due to evolutionary bias.
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Affiliation(s)
- Shintaro Minami
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
| | - Naohiro Kobayashi
- Institute for Protein Research (IPR), Osaka University, Osaka, Japan
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Toshihiko Sugiki
- Institute for Protein Research (IPR), Osaka University, Osaka, Japan
| | - Toshio Nagashima
- RIKEN Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | | | - Rie Tatsumi-Koga
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan
| | - George Chikenji
- Department of Applied Physics, Graduate School of Engineering, Nagoya University, Nagoya, Japan
| | - Nobuyasu Koga
- Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- SOKENDAI, The Graduate University for Advanced Studies, Hayama, Japan.
- Research Center of Integrative Molecular Systems, Institute for Molecular Science (IMS), National Institutes of Natural Sciences (NINS), Okazaki, Japan.
- Laboratory for Protein Design, Institute for Protein Research (IPR), Osaka University, Osaka, Japan.
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33
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Tsuboyama K, Dauparas J, Chen J, Laine E, Mohseni Behbahani Y, Weinstein JJ, Mangan NM, Ovchinnikov S, Rocklin GJ. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 2023; 620:434-444. [PMID: 37468638 PMCID: PMC10412457 DOI: 10.1038/s41586-023-06328-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Abstract
Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.
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Affiliation(s)
- Kotaro Tsuboyama
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- PRESTO, Japan Science and Technology Agency, Tokyo, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jonathan Chen
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Yasser Mohseni Behbahani
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Jonathan J Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Niall M Mangan
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Gabriel J Rocklin
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.
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34
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Hutchins GH, Noble CEM, Bunzel HA, Williams C, Dubiel P, Yadav SKN, Molinaro PM, Barringer R, Blackburn H, Hardy BJ, Parnell AE, Landau C, Race PR, Oliver TAA, Koder RL, Crump MP, Schaffitzel C, Oliveira ASF, Mulholland AJ, Anderson JLR. An expandable, modular de novo protein platform for precision redox engineering. Proc Natl Acad Sci U S A 2023; 120:e2306046120. [PMID: 37487099 PMCID: PMC10400981 DOI: 10.1073/pnas.2306046120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/20/2023] [Indexed: 07/26/2023] Open
Abstract
The electron-conducting circuitry of life represents an as-yet untapped resource of exquisite, nanoscale biomolecular engineering. Here, we report the characterization and structure of a de novo diheme "maquette" protein, 4D2, which we subsequently use to create an expanded, modular platform for heme protein design. A well-folded monoheme variant was created by computational redesign, which was then utilized for the experimental validation of continuum electrostatic redox potential calculations. This demonstrates how fundamental biophysical properties can be predicted and fine-tuned. 4D2 was then extended into a tetraheme helical bundle, representing a 7 nm molecular wire. Despite a molecular weight of only 24 kDa, electron cryomicroscopy illustrated a remarkable level of detail, indicating the positioning of the secondary structure and the heme cofactors. This robust, expressible, highly thermostable and readily designable modular platform presents a valuable resource for redox protein design and the future construction of artificial electron-conducting circuitry.
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Affiliation(s)
- George H. Hutchins
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Claire E. M. Noble
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
- BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, BristolBS8 1TQ, United Kingdom
| | - H. Adrian Bunzel
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | | | - Paulina Dubiel
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Sathish K. N. Yadav
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Paul M. Molinaro
- Department of Physics, The City College of New York, New York, NY10031
- Graduate Programs of Physics, Biology, Chemistry and Biochemistry, The Graduate Center of The City University of New York, New York, NY10016
| | - Rob Barringer
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Hector Blackburn
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Benjamin J. Hardy
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Alice E. Parnell
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
- BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, BristolBS8 1TQ, United Kingdom
| | - Charles Landau
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - Paul R. Race
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
- BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, BristolBS8 1TQ, United Kingdom
| | | | - Ronald L. Koder
- Department of Physics, The City College of New York, New York, NY10031
- Graduate Programs of Physics, Biology, Chemistry and Biochemistry, The Graduate Center of The City University of New York, New York, NY10016
| | - Matthew P. Crump
- School of Chemistry, University of Bristol, BristolBS8 1TS, United Kingdom
| | - Christiane Schaffitzel
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
| | - A. Sofia F. Oliveira
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
- School of Chemistry, University of Bristol, BristolBS8 1TS, United Kingdom
| | - Adrian J. Mulholland
- BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, BristolBS8 1TQ, United Kingdom
- School of Chemistry, University of Bristol, BristolBS8 1TS, United Kingdom
| | - J. L. Ross Anderson
- School of Biochemistry, University of Bristol, University Walk, BristolBS8 1TD, United Kingdom
- BrisSynBio Synthetic Biology Research Centre, Life Sciences Building, University of Bristol, BristolBS8 1TQ, United Kingdom
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35
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Zhang D, Chen Z, Du Z, Bao B, Su N, Chen X, Ge Y, Lin Q, Yang L, Hua Y, Wang S, Hua X, Zuo F, Li N, Liu R, Jiang L, Bao C, Zhao Y, Loscalzo J, Yang Y, Zhu L. Design of a palette of SNAP-tag mimics of fluorescent proteins and their use as cell reporters. Cell Discov 2023; 9:56. [PMID: 37311750 DOI: 10.1038/s41421-023-00546-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 03/18/2023] [Indexed: 06/15/2023] Open
Abstract
Naturally occurring fluorescent proteins (FPs) are the most widely used tools for tracking cellular proteins and sensing cellular events. Here, we chemically evolved the self-labeling SNAP-tag into a palette of SNAP-tag mimics of fluorescent proteins (SmFPs) that possess bright, rapidly inducible fluorescence ranging from cyan to infrared. SmFPs are integral chemical-genetic entities based on the same fluorogenic principle as FPs, i.e., induction of fluorescence of non-emitting molecular rotors by conformational locking. We demonstrate the usefulness of these SmFPs in real-time tracking of protein expression, degradation, binding interactions, trafficking, and assembly, and show that these optimally designed SmFPs outperform FPs like GFP in many important ways. We further show that the fluorescence of circularly permuted SmFPs is sensitive to the conformational changes of their fusion partners, and that these fusion partners can be used for the development of single SmFP-based genetically encoded calcium sensors for live cell imaging.
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Affiliation(s)
- Dasheng Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zhengda Chen
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zengmin Du
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Bingkun Bao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ni Su
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xianjun Chen
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
| | - Yihui Ge
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Qiuning Lin
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lipeng Yang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yujie Hua
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuo Wang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xin Hua
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Fangting Zuo
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Ningfeng Li
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Renmei Liu
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Li Jiang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chunyan Bao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yi Yang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
| | - Linyong Zhu
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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36
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Perfilov MM, Zaitseva ER, Baleeva NS, Kublitski VS, Smirnov AY, Bogdanova YA, Krasnova SA, Myasnyanko IN, Mishin AS, Baranov MS. Meta-CF 3-Substituted Analogues of the GFP Chromophore with Remarkable Solvatochromism. Int J Mol Sci 2023; 24:9923. [PMID: 37373071 DOI: 10.3390/ijms24129923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
In this work, we have shown that the introduction of a trifluoromethyl group into the me-ta-position of arylidene imidazolones (GFP chromophore core) leads to a dramatic increase in their fluorescence in nonpolar and aprotic media. The presence of a pronounced solvent-dependent gradation of fluorescence intensity makes it possible to use these substances as fluorescent polarity sensors. In particular, we showed that one of the created compounds could be used for selective labeling of the endoplasmic reticulum of living cells.
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Affiliation(s)
- Maxim M Perfilov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Elvira R Zaitseva
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Nadezhda S Baleeva
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow 117997, Russia
| | - Vadim S Kublitski
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Alexander Yu Smirnov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow 117997, Russia
- Center of Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoi Blvd. 30, Bld. 1, Moscow 121205, Russia
| | - Yulia A Bogdanova
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Svetlana A Krasnova
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Ivan N Myasnyanko
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow 117997, Russia
| | - Alexander S Mishin
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
| | - Mikhail S Baranov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow 117997, Russia
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37
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Bennett NR, Coventry B, Goreshnik I, Huang B, Allen A, Vafeados D, Peng YP, Dauparas J, Baek M, Stewart L, DiMaio F, De Munck S, Savvides SN, Baker D. Improving de novo protein binder design with deep learning. Nat Commun 2023; 14:2625. [PMID: 37149653 PMCID: PMC10163288 DOI: 10.1038/s41467-023-38328-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/24/2023] [Indexed: 05/08/2023] Open
Abstract
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
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Affiliation(s)
- Nathaniel R Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Aza Allen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ying Po Peng
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Steven De Munck
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - Savvas N Savvides
- VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Unit for Structural Biology, Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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38
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Tan Z, Li J, Hou J, Gonzalez R. Designing artificial pathways for improving chemical production. Biotechnol Adv 2023; 64:108119. [PMID: 36764336 DOI: 10.1016/j.biotechadv.2023.108119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.
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Affiliation(s)
- Zaigao Tan
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jian Li
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jin Hou
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China
| | - Ramon Gonzalez
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA.
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39
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Hederman AP, Ackerman ME. Leveraging deep learning to improve vaccine design. Trends Immunol 2023; 44:333-344. [PMID: 37003949 PMCID: PMC10485910 DOI: 10.1016/j.it.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/05/2023] [Accepted: 03/05/2023] [Indexed: 04/03/2023]
Abstract
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.
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Affiliation(s)
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA; Department of Microbiology and Immunology, Geisel School of Medicine, Hanover, NH, USA.
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40
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Bogdanova YA, Zaitseva ER, Smirnov AY, Baleeva NS, Gavrikov AS, Myasnyanko IN, Goncharuk SA, Kot EF, Mineev KS, Mishin AS, Baranov MS. NanoLuc Luciferase as a Fluorogen-Activating Protein for GFP Chromophore Based Fluorogens. Int J Mol Sci 2023; 24:ijms24097958. [PMID: 37175667 PMCID: PMC10178166 DOI: 10.3390/ijms24097958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
In this work, we showed that the well-known NanoLuc luciferase can act as a fluorogen activating protein for various arylidene-imidazolones structurally similar to the Kaede protein chromophore. We showed that such compounds can be used as fluorescent sensors for this protein and can also be used in pairs with it in fluorescent microscopy as a genetically encoded tag.
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Affiliation(s)
- Yulia A Bogdanova
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
| | - Elvira R Zaitseva
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
| | - Alexander Yu Smirnov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, 117997 Moscow, Russia
| | - Nadezhda S Baleeva
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, 117997 Moscow, Russia
| | - Alexey S Gavrikov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
| | - Ivan N Myasnyanko
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, 117997 Moscow, Russia
| | - Sergey A Goncharuk
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
| | - Erik F Kot
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
| | - Konstantin S Mineev
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
| | - Alexander S Mishin
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
| | - Mikhail S Baranov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Laboratory of Medicinal Substances Chemistry, Institute of Translational Medicine, Pirogov Russian National Research Medical University, Ostrovitianov 1, 117997 Moscow, Russia
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41
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Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
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Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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42
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Zhang W, Liu M, Wang Y, Wang X, Wang R, Li S, Yu L, Zhang F, Wang C. β-Sheet Assembly Translates Conservative Single-Site Mutation into a Perturbation in Macroscopic Structure. NANO LETTERS 2023; 23:2370-2378. [PMID: 36897606 DOI: 10.1021/acs.nanolett.3c00311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Transferring structural information from amino acid sequence to macroscale assembly is a challenging approach for designing protein quaternary structure. However, the pathway by which the slight variations in sequence result in a global perturbation effect on the assembled structure is unknown. Herein, we design two synthetic peptides, QNL-His and QNL-Arg, with one amino acid substitution and use scanning tunneling microscopy (STM) to image individual peptides in the assembled state. The submolecular resolution of STM enables us to determine the folding structure and β-sheet supramolecular organization of peptides. QNL-His and QNL-Arg differ in their β-strand length distribution in pleated β-sheet association. These structural variations lead to distinguishable outcomes in their β-sheet assembled fibrils and phase transitions. The comparison of QNL-His versus QNL-Arg structures and macroscopic properties unveils the role of assembly to amplify the structural variations associated with a single-site mutation from a single-molecule scale to a macroscopic scale.
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Affiliation(s)
- Wenbo Zhang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Mingwei Liu
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Yang Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Xin Wang
- Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14850, United States
| | - Ruonan Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Shuyuan Li
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Lanlan Yu
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
| | - Feiyi Zhang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
- Institute for Advanced Materials, Jiangsu University, Zhenjiang, Jiangsu 212013, P. R. China
| | - Chenxuan Wang
- State Key Laboratory of Medical Molecular Biology, Haihe Laboratory of Cell Ecosystem, Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, P. R. China
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43
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Wang JY(J, Khmelinskaia A, Sheffler W, Miranda MC, Antanasijevic A, Borst AJ, Torres SV, Shu C, Hsia Y, Nattermann U, Ellis D, Walkey C, Ahlrichs M, Chan S, Kang A, Nguyen H, Sydeman C, Sankaran B, Wu M, Bera AK, Carter L, Fiala B, Murphy M, Baker D, Ward AB, King NP. Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains. Proc Natl Acad Sci U S A 2023; 120:e2214556120. [PMID: 36888664 PMCID: PMC10089191 DOI: 10.1073/pnas.2214556120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/03/2023] [Indexed: 03/09/2023] Open
Abstract
Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.
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Affiliation(s)
- Jing Yang (John) Wang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA98195
| | - Alena Khmelinskaia
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions”, University of Bonn, 53113Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, 53121Bonn, Germany
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Marcos C. Miranda
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Aleksandar Antanasijevic
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- Scripps Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, CA92037
| | - Andrew J. Borst
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Susana V. Torres
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Chelsea Shu
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Yang Hsia
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Una Nattermann
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA98195
| | - Daniel Ellis
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA98195
| | - Carl Walkey
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Maggie Ahlrichs
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Sidney Chan
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Claire Sydeman
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Banumathi Sankaran
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, Berkeley, CA94720
- Berkeley Center for Structural Biology, Lawrence Berkeley Laboratory, Berkeley, CA94720
| | - Mengyu Wu
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Brooke Fiala
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Michael Murphy
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Andrew B. Ward
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037
- Scripps Consortium for HIV/AIDS Vaccine Development, The Scripps Research Institute, La Jolla, CA92037
| | - Neil P. King
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
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44
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Kim DE, Jensen DR, Feldman D, Tischer D, Saleem A, Chow CM, Li X, Carter L, Milles L, Nguyen H, Kang A, Bera AK, Peterson FC, Volkman BF, Ovchinnikov S, Baker D. De novo design of small beta barrel proteins. Proc Natl Acad Sci U S A 2023; 120:e2207974120. [PMID: 36897987 PMCID: PMC10089152 DOI: 10.1073/pnas.2207974120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 01/27/2023] [Indexed: 03/12/2023] Open
Abstract
Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.
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Affiliation(s)
- David E. Kim
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- HHMI, University of Washington, Seattle, WA98195
| | - Davin R. Jensen
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - David Feldman
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Doug Tischer
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Ayesha Saleem
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Cameron M. Chow
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Lukas Milles
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Francis C. Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - Brian F. Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI53226
| | - Sergey Ovchinnikov
- Division of Science, Faculty of Arts and Sciences, Harvard University, Cambridge, MA02138
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA02138
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- HHMI, University of Washington, Seattle, WA98195
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45
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Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
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46
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Wang X, Xu K, Tan Y, Liu S, Zhou J. Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes. Int J Mol Sci 2023; 24:3827. [PMID: 36835238 PMCID: PMC9964944 DOI: 10.3390/ijms24043827] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 02/17/2023] Open
Abstract
Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.
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Affiliation(s)
- Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Song Liu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, School of Biotechnology, Jiangnan University, Wuxi 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China
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47
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Menke FS, Mazzier D, Wicher B, Allmendinger L, Kauffmann B, Maurizot V, Huc I. Molecular torsion springs: alteration of helix curvature in frustrated tertiary folds. Org Biomol Chem 2023; 21:1275-1283. [PMID: 36645374 DOI: 10.1039/d2ob02109a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The first abiotic foldamer tertiary structures have been recently reported in the form of aromatic helix-turn-helix motifs based on oligo-quinolinecarboxamides held together by intramolecular hydrogen bonds. Tertiary folds were predicted by computational modelling of the hydrogen-bonding interfaces between helices and later verified by X-ray crystallography. However, the prognosis of how the conformational preference inherent to each helix influences the tertiary structure warranted further investigation. Several new helix-turn-helix sequences were synthesised in which some hydrogen bonds have been removed. Contrary to expectations, this change did not strongly destabilise the tertiary folds. On closer inspection, a new crystal structure revealed that helices adopt their natural curvature when some hydrogen bonds are missing and undergo some spring torsion upon forming the said hydrogen bonds, thus potentially giving rise to a conformational frustration. This phenomenon sheds light on the aggregation behaviour of the helices when they are not linked by a turn unit.
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Affiliation(s)
- Friedericke S Menke
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Daniela Mazzier
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Barbara Wicher
- Department of Chemical Technology of Drugs, Poznan University of Medical Sciences, Grunwaldzka 6, 60-780 Poznan, Poland
| | - Lars Allmendinger
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
| | - Brice Kauffmann
- Institut Européen de Chimie et Biologie (UMS3011/US001), CNRS, Inserm, Université de Bordeaux, 2 rue Robert Escarpit, F-33600 Pessac, France
| | - Victor Maurizot
- CBMN (UMR 5248), Univ. Bordeaux, CNRS, Bordeaux INP, 2 rue Robert Escarpit, 33600 Pessac, France
| | - Ivan Huc
- Department of Pharmacy, Ludwig-Maximilians-University, Butenandstraße 5-13, 81377 Munich, Germany.
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Yeh AHW, Norn C, Kipnis Y, Tischer D, Pellock SJ, Evans D, Ma P, Lee GR, Zhang JZ, Anishchenko I, Coventry B, Cao L, Dauparas J, Halabiya S, DeWitt M, Carter L, Houk KN, Baker D. De novo design of luciferases using deep learning. Nature 2023; 614:774-780. [PMID: 36813896 PMCID: PMC9946828 DOI: 10.1038/s41586-023-05696-3] [Citation(s) in RCA: 89] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 01/03/2023] [Indexed: 02/24/2023]
Abstract
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.
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Affiliation(s)
- Andy Hsien-Wei Yeh
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
| | - Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yakov Kipnis
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Doug Tischer
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samuel J Pellock
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Declan Evans
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Pengchen Ma
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
- School of Chemistry, Xi'an Key Laboratory of Sustainable Energy Materials Chemistry, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi'an Jiaotong University, Xi'an, China
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jason Z Zhang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Samer Halabiya
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Michelle DeWitt
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lauren Carter
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - K N Houk
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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Niitsu A, Sugita Y. Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation. Phys Chem Chem Phys 2023; 25:3595-3606. [PMID: 36647771 DOI: 10.1039/d2cp03972a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.
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Affiliation(s)
- Ai Niitsu
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan. .,Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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50
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De novo protein fold design through sequence-independent fragment assembly simulations. Proc Natl Acad Sci U S A 2023; 120:e2208275120. [PMID: 36656852 PMCID: PMC9942881 DOI: 10.1073/pnas.2208275120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.
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