151
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Koehler Leman J, Szczerbiak P, Renfrew PD, Gligorijevic V, Berenberg D, Vatanen T, Taylor BC, Chandler C, Janssen S, Pataki A, Carriero N, Fisk I, Xavier RJ, Knight R, Bonneau R, Kosciolek T. Sequence-structure-function relationships in the microbial protein universe. Nat Commun 2023; 14:2351. [PMID: 37100781 PMCID: PMC10133388 DOI: 10.1038/s41467-023-37896-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, NY, USA.
| | - Pawel Szczerbiak
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Vladimir Gligorijevic
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
| | - Daniel Berenberg
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA
| | - Tommi Vatanen
- Broad Institute, Cambridge, MA, USA
- Liggins Institute, University of Auckland, Auckland, New Zealand
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, 00014 University of Helsinki, Helsinki, Finland
| | - Bryn C Taylor
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- In Silico Discovery and External Innovation, Janssen Research and Development, San Diego, CA, 92122, USA
| | - Chris Chandler
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Stefan Janssen
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA
- Algorithmic Bioinformatics, Justus Liebig University Giessen, Giessen, Germany
| | - Andras Pataki
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Nick Carriero
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Ian Fisk
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Ramnik J Xavier
- Broad Institute, Cambridge, MA, USA
- Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, 02139, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
- Center for Data Science, New York University, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY, USA
- Prescient Design, a Genentech accelerator, New York, NY, 10010, USA
| | - Tomasz Kosciolek
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.
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152
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Chino M, Di Costanzo LF, Leone L, La Gatta S, Famulari A, Chiesa M, Lombardi A, Pavone V. Designed Rubredoxin miniature in a fully artificial electron chain triggered by visible light. Nat Commun 2023; 14:2368. [PMID: 37185349 PMCID: PMC10130062 DOI: 10.1038/s41467-023-37941-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
Designing metal sites into de novo proteins has significantly improved, recently. However, identifying the minimal coordination spheres, able to encompass the necessary information for metal binding and activity, still represents a great challenge, today. Here, we test our understanding with a benchmark, nevertheless difficult, case. We assemble into a miniature 28-residue protein, the quintessential elements required to fold properly around a FeCys4 redox center, and to function efficiently in electron-transfer. This study addresses a challenge in de novo protein design, as it reports the crystal structure of a designed tetra-thiolate metal-binding protein in sub-Å agreement with the intended design. This allows us to well correlate structure to spectroscopic and electrochemical properties. Given its high reduction potential compared to natural and designed FeCys4-containing proteins, we exploit it as terminal electron acceptor of a fully artificial chain triggered by visible light.
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Affiliation(s)
- Marco Chino
- Department of Chemical Sciences, University of Naples Federico II, Via Cintia 21, 80126, Napoli, Italy
| | - Luigi Franklin Di Costanzo
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Italy
| | - Linda Leone
- Department of Chemical Sciences, University of Naples Federico II, Via Cintia 21, 80126, Napoli, Italy
| | - Salvatore La Gatta
- Department of Chemical Sciences, University of Naples Federico II, Via Cintia 21, 80126, Napoli, Italy
| | - Antonino Famulari
- Department of Chemistry, University of Torino, Via Giuria 9, 10125, Torino, Italy
- Department of Condensed Matter Physics, University of Zaragoza, Calle Pedro Cerbuna 12, 50009, Zaragoza, Spain
| | - Mario Chiesa
- Department of Chemistry, University of Torino, Via Giuria 9, 10125, Torino, Italy
| | - Angela Lombardi
- Department of Chemical Sciences, University of Naples Federico II, Via Cintia 21, 80126, Napoli, Italy.
| | - Vincenzo Pavone
- Department of Chemical Sciences, University of Naples Federico II, Via Cintia 21, 80126, Napoli, Italy.
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153
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Koehler Leman J, Künze G. Recent Advances in NMR Protein Structure Prediction with ROSETTA. Int J Mol Sci 2023; 24:ijms24097835. [PMID: 37175539 PMCID: PMC10178863 DOI: 10.3390/ijms24097835] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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154
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Liessmann F, Künze G, Meiler J. Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection. Int J Mol Sci 2023; 24:7788. [PMID: 37175495 PMCID: PMC10178219 DOI: 10.3390/ijms24097788] [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: 04/03/2023] [Revised: 04/19/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.
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Affiliation(s)
- Fabian Liessmann
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
| | - Jens Meiler
- Institute for Drug Discovery, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (F.L.)
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, 04105 Leipzig, Germany
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155
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Sato N, Suetaka S, Hayashi Y, Arai M. Rational peptide design for inhibition of the KIX-MLL interaction. Sci Rep 2023; 13:6330. [PMID: 37072438 PMCID: PMC10113271 DOI: 10.1038/s41598-023-32848-2] [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: 10/25/2022] [Accepted: 04/03/2023] [Indexed: 05/03/2023] Open
Abstract
The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX-MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein-protein interactions implicated in the progression of various diseases.
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Affiliation(s)
- Nao Sato
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
| | - Shunji Suetaka
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
| | - Yuuki Hayashi
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
- Environmental Science Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-0033, Japan
| | - Munehito Arai
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan.
- Department of Physics, Graduate School of Science, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan.
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156
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Yang EC, Divine R, Miranda MC, Borst AJ, Sheffler W, Zhang JZ, Decarreau J, Saragovi A, Abedi M, Goldbach N, Ahlrichs M, Dobbins C, Hand A, Cheng S, Lamb M, Levine PM, Chan S, Skotheim R, Fallas J, Ueda G, Lubner J, Somiya M, Khmelinskaia A, King NP, Baker D. Computational design of non-porous, pH-responsive antibody nanoparticles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.537263. [PMID: 37131615 PMCID: PMC10153164 DOI: 10.1101/2023.04.17.537263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and important for targeted delivery of biologics. We describe the design of octahedral non-porous nanoparticles with the three symmetry axes (four-fold, three-fold, and two-fold) occupied by three distinct protein homooligomers: a de novo designed tetramer, an antibody of interest, and a designed trimer programmed to disassemble below a tunable pH transition point. The nanoparticles assemble cooperatively from independently purified components, and a cryo-EM density map reveals that the structure is very close to the computational design model. The designed nanoparticles can package a variety of molecular payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between to 5.9-6.7. To our knowledge, these are the first designed nanoparticles with more than two structural components and with finely tunable environmental sensitivity, and they provide new routes to antibody-directed targeted delivery.
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Affiliation(s)
- Erin C Yang
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure & Design, University of Washington, Seattle, WA, USA
| | - Robby Divine
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Graduate Program in Biochemistry, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Marcos C Miranda
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Medicine Solna, Division of Immunology and Allergy, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Andrew J Borst
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Will Sheffler
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jason Z Zhang
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Justin Decarreau
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Amijai Saragovi
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Mohamad Abedi
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Nicolas Goldbach
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Technical University of Munich, Munich, Germany
| | - Maggie Ahlrichs
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Craig Dobbins
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Alexis Hand
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Suna Cheng
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Mila Lamb
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Paul M Levine
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Sidney Chan
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rebecca Skotheim
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Jorge Fallas
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - George Ueda
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Joshua Lubner
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Masaharu Somiya
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- SANKEN, Osaka University, Osaka, Japan
| | - Alena Khmelinskaia
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Transdisciplinary Research Area "Building Blocks of Matter and Fundamental Interactions (TRA Matter)", University of Bonn, Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Neil P King
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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157
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Kumar A, Matsuoka M, Matsuyama A, Yoshida M, Zhang KYJ. Identification of Fungal and Bacterial Denitrification Inhibitors Targeting Copper Nitrite Reductase. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:5172-5184. [PMID: 36967599 DOI: 10.1021/acs.jafc.3c00896] [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
The usage of nitrification inhibitors is one of the strategies that reduce or slow down the denitrification process to prevent nitrogen loss to the atmosphere in the form of N2O. Directly targeting microbial denitrification could be one of the mitigation strategies; however, until now little efforts have been devoted toward the development of denitrification inhibitors. Here, we have identified small-molecule inhibitors of one of the proteins involved in the fungal denitrification pathway. Specifically, virtual screening was employed to identify the inhibitors of copper-containing nitrite reductase (FoNirK) of the filamentous fungus Fusarium oxysporum. Three series of chemical compounds were identified, out of which compounds belonging to two chemical scaffolds inhibited FoNirK enzymatic activity in low micromolar ranges. Several compounds also displayed moderate inhibition of fungal denitrification activity in vivo. Evaluation of in vitro activity against NirK from denitrifying bacterium Achromobacter xylosoxidans (AxNirK) and in vivo bacterial denitrification revealed a similar inhibitory profile.
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Affiliation(s)
- Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Masaki Matsuoka
- Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Akihisa Matsuyama
- Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, and Collaboerative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Minoru Yoshida
- Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, and Collaboerative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
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158
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Wu K, Bai H, Chang YT, Redler R, McNally KE, Sheffler W, Brunette TJ, Hicks DR, Morgan TE, Stevens TJ, Broerman A, Goreshnik I, DeWitt M, Chow CM, Shen Y, Stewart L, Derivery E, Silva DA, Bhabha G, Ekiert DC, Baker D. De novo design of modular peptide-binding proteins by superhelical matching. Nature 2023; 616:581-589. [PMID: 37020023 PMCID: PMC10115654 DOI: 10.1038/s41586-023-05909-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 03/01/2023] [Indexed: 04/07/2023]
Abstract
General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.
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Affiliation(s)
- Kejia Wu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Biological Physics, Structure and Design Graduate Program, University of Washington, Seattle, WA, USA
| | - Hua Bai
- 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
| | - Ya-Ting Chang
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Rachel Redler
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | | | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - T J Brunette
- 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
| | | | | | - Adam Broerman
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, 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
| | - Michelle DeWitt
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Cameron M Chow
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Yihang Shen
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | | | - Daniel Adriano Silva
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Division of Life Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong.
- Monod Bio, Seattle, WA, USA.
| | - Gira Bhabha
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Damian C Ekiert
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, New York University School of Medicine, New York, NY, 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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159
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Naveed M, Waseem M, Aziz T, Hassan JU, Makhdoom SI, Ali U, Alharbi M, Alsahammari A. Identification of Bacterial Strains and Development of anmRNA-Based Vaccine to Combat Antibiotic Resistance in Staphylococcus aureus via In Vitro and In Silico Approaches. Biomedicines 2023; 11:biomedicines11041039. [PMID: 37189657 DOI: 10.3390/biomedicines11041039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
The emergence of antibiotic-resistant microorganisms is a significant concern in global health. Antibiotic resistance is attributed to various virulent factors and genetic elements. This study investigated the virulence factors of Staphylococcus aureus to create an mRNA-based vaccine that could help prevent antibiotic resistance. Distinct strains of the bacteria were selected for molecular identification of virulence genes, such as spa, fmhA, lukD, and hla-D, which were performed utilizing PCR techniques. DNA extraction from samples of Staphylococcus aureus was conducted using the Cetyl Trimethyl Ammonium Bromide (CTAB) method, which was confirmed and visualized using a gel doc; 16S rRNA was utilized to identify the bacterial strains, and primers of spa, lukD, fmhA, and hla-D genes were employed to identify the specific genes. Sequencing was carried out at Applied Bioscience International (ABI) in Malaysia. Phylogenetic analysis and alignment of the strains were subsequently constructed. We also performed an in silico analysis of the spa, fmhA, lukD, and hla-D genes to generate an antigen-specific vaccine. The virulence genes were translated into proteins, and a chimera was created using various linkers. The mRNA vaccine candidate was produced utilizing 18 epitopes, linkers, and an adjuvant, known as RpfE, to target the immune system. Testing determined that this design covered 90% of the population conservancy. An in silico immunological vaccine simulation was conducted to verify the hypothesis, including validating and predicting secondary and tertiary structures and molecular dynamics simulations to evaluate the vaccine’s long-term viability. This vaccine design may be further evaluated through in vivo and in vitro testing to assess its efficacy.
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160
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Malbranke C, Bikard D, Cocco S, Monasson R, Tubiana J. Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies. Curr Opin Struct Biol 2023; 80:102571. [PMID: 36947951 DOI: 10.1016/j.sbi.2023.102571] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 01/29/2023] [Accepted: 02/07/2023] [Indexed: 03/24/2023]
Abstract
Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The former infer characteristic sequence features shared by sets of evolutionary-related proteins, such as conserved or coevolving positions, and recombine them to generate candidates with similar structure and function. The latter approaches estimate key biochemical properties, such as structure free energy, conformational entropy, or binding affinities using machine learning surrogates, and optimize them to yield improved designs. Here, we review recent progress along both tracks, discuss their strengths and weaknesses, and highlight opportunities for synergistic approaches.
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Affiliation(s)
- Cyril Malbranke
- Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France.
| | - David Bikard
- Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France
| | - Simona Cocco
- Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France
| | - Rémi Monasson
- Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France
| | - Jérôme Tubiana
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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161
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Kimball IH, Nguyen PT, Olivera BM, Sack JT, Yarov-Yarovoy V. Molecular determinants of μ-conotoxin KIIIA interaction with the human voltage-gated sodium channel Na V1.7. Front Pharmacol 2023; 14:1156855. [PMID: 37007002 PMCID: PMC10060530 DOI: 10.3389/fphar.2023.1156855] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/03/2023] [Indexed: 03/18/2023] Open
Abstract
The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between μ-Conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels.
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Affiliation(s)
- Ian H. Kimball
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
| | - Phuong T. Nguyen
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
| | | | - Jon T. Sack
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
- Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA, United States
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA, United States
- Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA, United States
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162
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Edman NI, Redler RL, Phal A, Schlichthaerle T, Srivatsan SR, Etemadi A, An SJ, Favor A, Ehnes D, Li Z, Praetorius F, Gordon M, Yang W, Coventry B, Hicks DR, Cao L, Bethel N, Heine P, Murray A, Gerben S, Carter L, Miranda M, Negahdari B, Lee S, Trapnell C, Stewart L, Ekiert DC, Schlessinger J, Shendure J, Bhabha G, Ruohola-Baker H, Baker D. Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532666. [PMID: 36993355 PMCID: PMC10055045 DOI: 10.1101/2023.03.14.532666] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Growth factors and cytokines signal by binding to the extracellular domains of their receptors and drive association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affects signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo designed fibroblast growth-factor receptor (FGFR) binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and MAPK pathway activation. The high specificity of the designed agonists reveal distinct roles for two FGFR splice variants in driving endothelial and mesenchymal cell fates during early vascular development. The ability to incorporate receptor binding domains and repeat extensions in a modular fashion makes our designed scaffolds broadly useful for probing and manipulating cellular signaling pathways.
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Affiliation(s)
- Natasha I Edman
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA 98195, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA
| | - Rachel L Redler
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Ashish Phal
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Thomas Schlichthaerle
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Sanjay R Srivatsan
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Ali Etemadi
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Seong J An
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Andrew Favor
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Devon Ehnes
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Zhe Li
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Florian Praetorius
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Max Gordon
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Derrick R. Hicks
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Neville Bethel
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Piper Heine
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Stacey Gerben
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Marcos Miranda
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Babak Negahdari
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Sangwon Lee
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Damian C. Ekiert
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, New York University School of Medicine, New York, United States
| | - Joseph Schlessinger
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Gira Bhabha
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Hannele Ruohola-Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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163
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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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164
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Eastwood JRB, Weisberg EI, Katz D, Zuckermann RN, Kirshenbaum K. Guidelines for designing peptoid structures: Insights from the
Peptoid Data Bank. Pept Sci (Hoboken) 2023. [DOI: 10.1002/pep2.24307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Affiliation(s)
| | | | - Dana Katz
- Department of Chemistry New York University New York New York USA
| | | | - Kent Kirshenbaum
- Department of Chemistry New York University New York New York USA
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165
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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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166
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Remesh SG, Merz GE, Brilot AF, Chio US, Rizo AN, Pospiech TH, Lui I, Laurie MT, Glasgow J, Le CQ, Zhang Y, Diwanji D, Hernandez E, Lopez J, Mehmood H, Pawar KI, Pourmal S, Smith AM, Zhou F, DeRisi J, Kortemme T, Rosenberg OS, Glasgow A, Leung KK, Wells JA, Verba KA. Computational pipeline provides mechanistic understanding of Omicron variant of concern neutralizing engineered ACE2 receptor traps. Structure 2023; 31:253-264.e6. [PMID: 36805129 PMCID: PMC9936628 DOI: 10.1016/j.str.2023.01.009] [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: 08/02/2022] [Revised: 11/23/2022] [Accepted: 01/25/2023] [Indexed: 02/19/2023]
Abstract
The SARS-CoV-2 Omicron variant, with 15 mutations in Spike receptor-binding domain (Spike-RBD), renders virtually all clinical monoclonal antibodies against WT SARS-CoV-2 ineffective. We recently engineered the SARS-CoV-2 host entry receptor, ACE2, to tightly bind WT-RBD and prevent viral entry into host cells ("receptor traps"). Here we determine cryo-EM structures of our receptor traps in complex with stabilized Spike ectodomain. We develop a multi-model pipeline combining Rosetta protein modeling software and cryo-EM to allow interface energy calculations even at limited resolution and identify interface side chains that allow for high-affinity interactions between our ACE2 receptor traps and Spike-RBD. Our structural analysis provides a mechanistic rationale for the high-affinity (0.53-4.2 nM) binding of our ACE2 receptor traps to Omicron-RBD confirmed with biolayer interferometry measurements. Finally, we show that ACE2 receptor traps potently neutralize Omicron and Delta pseudotyped viruses, providing alternative therapeutic routes to combat this evolving virus.
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Affiliation(s)
- Soumya G Remesh
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gregory E Merz
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Axel F Brilot
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Un Seng Chio
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Alexandrea N Rizo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Thomas H Pospiech
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Irene Lui
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Mathew T Laurie
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jeff Glasgow
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chau Q Le
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yun Zhang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Devan Diwanji
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Evelyn Hernandez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jocelyne Lopez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Hevatib Mehmood
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Komal Ishwar Pawar
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sergei Pourmal
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Amber M Smith
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Fengbo Zhou
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joseph DeRisi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA; QBI, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; The University of California, Berkeley-University of California, San Francisco Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Oren S Rosenberg
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Anum Glasgow
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
| | - Kevin K Leung
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - James A Wells
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA.
| | - Kliment A Verba
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, San Francisco, CA 94158, USA; QBI, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA.
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167
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Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol Sci 2023; 44:175-189. [PMID: 36669976 DOI: 10.1016/j.tips.2022.12.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023]
Abstract
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.
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168
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Ma W, You S, Regnier M, McCammon JA. Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation. Proc Natl Acad Sci U S A 2023; 120:e2215836120. [PMID: 36802417 PMCID: PMC9992861 DOI: 10.1073/pnas.2215836120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/15/2023] [Indexed: 02/23/2023] Open
Abstract
Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.
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Affiliation(s)
- Wen Ma
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA92093
| | - Shengjun You
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD21205
| | - Michael Regnier
- Department of Bioengineering, University of Washington, Seattle, WA98109
| | - J. Andrew McCammon
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA92093
- Department of Pharmacology, University of California, San Diego, La Jolla, CA92093
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169
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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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170
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Li M, Zhi Y, Liu B, Yao Q. Advancing Strategies for Proteolysis-Targeting Chimera Design. J Med Chem 2023; 66:2308-2329. [PMID: 36788245 DOI: 10.1021/acs.jmedchem.2c01555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.
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Affiliation(s)
- Minglei Li
- School of Pharmacy and Pharmaceutical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117 Shandong, P. R. China
| | - Ying Zhi
- School of Pharmacy and Pharmaceutical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117 Shandong, P. R. China
| | - Bo Liu
- School of Pharmacy and Pharmaceutical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117 Shandong, P. R. China
| | - Qingqiang Yao
- School of Pharmacy and Pharmaceutical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117 Shandong, P. R. China
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171
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Belyaeva J, Zlobin A, Maslova V, Golovin A. Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease. Phys Chem Chem Phys 2023; 25:6352-6361. [PMID: 36779321 DOI: 10.1039/d2cp05502c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.
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Affiliation(s)
- Julia Belyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991, Moscow, Russia. .,Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997, Moscow, Russia
| | - Alexander Zlobin
- Sirius University of Science and Technology, 354340, Sochi, Russia.,Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Valentina Maslova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991, Moscow, Russia. .,Sirius University of Science and Technology, 354340, Sochi, Russia
| | - Andrey Golovin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119991, Moscow, Russia. .,Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997, Moscow, Russia.,Sirius University of Science and Technology, 354340, Sochi, Russia
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172
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Guffy SL, Pulavarti SVSRK, Harrison J, Fleming D, Szyperski T, Kuhlman B. Inside-Out Design of Zinc-Binding Proteins with Non-Native Backbones. Biochemistry 2023; 62:770-781. [PMID: 36634348 PMCID: PMC9939277 DOI: 10.1021/acs.biochem.2c00595] [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] [Indexed: 01/14/2023]
Abstract
The de novo design of functional proteins requires specification of tertiary structure and incorporation of molecular binding sites. Here, we develop an inside-out design strategy in the molecular modeling program Rosetta that begins with amino acid side chains from one or two α-helices making well-defined contacts with a ligand. A full-sized protein is then built around the ligand by adding additional helices that promote the formation of a protein core and allow additional contacts with the ligand. The protocol was tested by designing 12 zinc-binding proteins, each with 4-5 helices. Four of the designs were folded and bound to zinc with equilibrium dissociation constants varying between 95 nM and 1.1 μM. The design with the tightest affinity for zinc, N12, adopts a unique conformation in the folded state as assessed with nuclear magnetic resonance (NMR) and the design model closely matches (backbone root-mean-square deviation (RMSD) < 1 Å) an AlphaFold model of the sequence. Retrospective analysis with AlphaFold suggests that the sequences of many of the failed designs did not encode the desired tertiary packing.
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Affiliation(s)
- Sharon L. Guffy
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Joseph Harrison
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Drew Fleming
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York, Buffalo, NY, 14260, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
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173
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Reddi RN, Rogel A, Gabizon R, Rawale DG, Harish B, Marom S, Tivon B, Arbel YS, Gurwicz N, Oren R, David K, Liu J, Duberstein S, Itkin M, Malitsky S, Barr H, Katz BZ, Herishanu Y, Shachar I, Shulman Z, London N. Sulfamate Acetamides as Self-Immolative Electrophiles for Covalent Ligand-Directed Release Chemistry. J Am Chem Soc 2023; 145:3346-3360. [PMID: 36738297 PMCID: PMC9936582 DOI: 10.1021/jacs.2c08853] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Electrophiles for covalent inhibitors that are suitable for in vivo administration are rare. While acrylamides are prevalent in FDA-approved covalent drugs, chloroacetamides are considered too reactive for such purposes. We report sulfamate-based electrophiles that maintain chloroacetamide-like geometry with tunable reactivity. In the context of the BTK inhibitor ibrutinib, sulfamate analogues showed low reactivity with comparable potency in protein labeling, in vitro, and cellular kinase activity assays and were effective in a mouse model of CLL. In a second example, we converted a chloroacetamide Pin1 inhibitor to a potent and selective sulfamate acetamide with improved buffer stability. Finally, we show that sulfamate acetamides can be used for covalent ligand-directed release (CoLDR) chemistry, both for the generation of "turn-on" probes as well as for traceless ligand-directed site-specific labeling of proteins. Taken together, this chemistry represents a promising addition to the list of electrophiles suitable for in vivo covalent targeting.
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Affiliation(s)
- Rambabu N. Reddi
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel,
| | - Adi Rogel
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ronen Gabizon
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Dattatraya Gautam Rawale
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Battu Harish
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Shir Marom
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Barr Tivon
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yamit Shorer Arbel
- Sackler
Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel
| | - Neta Gurwicz
- Dept.
of Systems Immunology, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Roni Oren
- Department
of Veterinary Resources, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Keren David
- Dept.
of Systems Immunology, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Jingjing Liu
- Dept.
of Systems Immunology, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Shirly Duberstein
- Wohl
Institute for Drug Discovery of the Nancy and Stephen Grand Israel
National Center for Personalized Medicine, The Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Maxim Itkin
- Life Sciences
Core Facilities, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Sergey Malitsky
- Life Sciences
Core Facilities, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Haim Barr
- Wohl
Institute for Drug Discovery of the Nancy and Stephen Grand Israel
National Center for Personalized Medicine, The Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ben-Zion Katz
- Sackler
Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel,Department
of Hematology, Tel Aviv Sourasky Medical
Center, Tel Aviv 6423906, Israel
| | - Yair Herishanu
- Sackler
Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel,Department
of Hematology, Tel Aviv Sourasky Medical
Center, Tel Aviv 6423906, Israel
| | - Idit Shachar
- Dept.
of Systems Immunology, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Ziv Shulman
- Dept.
of Systems Immunology, The Weizmann Institute
of Science, Rehovot 7610001, Israel
| | - Nir London
- Dept.
of Chemical and Structural Biology, The
Weizmann Institute of Science, Rehovot 7610001, Israel,
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174
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Melo MCR, Bernardi RC. Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike. Biophys J 2023:S0006-3495(23)00091-7. [PMID: 36738105 PMCID: PMC10398237 DOI: 10.1016/j.bpj.2023.01.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/24/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.
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Affiliation(s)
- Marcelo C R Melo
- Auburn University, Department of Physics, Auburn University, Auburn, Alabama
| | - Rafael C Bernardi
- Auburn University, Department of Physics, Auburn University, Auburn, Alabama.
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175
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Jiang F, Liu Y, Xue Y, Cheng P, Wang J, Lian J, Gong W. Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis. Int Immunopharmacol 2023; 115:109728. [PMID: 36652758 PMCID: PMC9832108 DOI: 10.1016/j.intimp.2023.109728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/01/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses. METHODS Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed. RESULTS A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies. CONCLUSION S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.
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Affiliation(s)
- Fan Jiang
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China,The Second Brigade of Cadet, Basic Medical Science Academy of Air Force Medical University, Xi’an, China,Department of Infectious Diseases, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Yinping Liu
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Yong Xue
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Peng Cheng
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Jie Wang
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Jianqi Lian
- Department of Infectious Diseases, Tangdu Hospital, Air Force Medical University, Xi'an, China.
| | - Wenping Gong
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China.
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176
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Ceppi I, Cannavo E, Bret H, Camarillo R, Vivalda F, Thakur RS, Romero-Franco A, Sartori AA, Huertas P, Guérois R, Cejka P. PLK1 regulates CtIP and DNA2 interplay in long-range DNA end resection. Genes Dev 2023; 37:119-135. [PMID: 36746606 PMCID: PMC10069449 DOI: 10.1101/gad.349981.122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 01/12/2023] [Indexed: 02/08/2023]
Abstract
DNA double-strand break (DSB) repair is initiated by DNA end resection. CtIP acts in short-range resection to stimulate MRE11-RAD50-NBS1 (MRN) to endonucleolytically cleave 5'-terminated DNA to bypass protein blocks. CtIP also promotes the DNA2 helicase-nuclease to accelerate long-range resection downstream from MRN. Here, using AlphaFold2, we identified CtIP-F728E-Y736E as a separation-of-function mutant that is still proficient in conjunction with MRN but is not able to stimulate ssDNA degradation by DNA2. Accordingly, CtIP-F728E-Y736E impairs physical interaction with DNA2. Cellular assays revealed that CtIP-F728E-Y736E cells exhibit reduced DSB-dependent chromatin-bound RPA, impaired long-range resection, and increased sensitivity to DSB-inducing drugs. Previously, CtIP was shown to be targeted by PLK1 to inhibit long-range resection, yet the underlying mechanism was unclear. We show that the DNA2-interacting region in CtIP includes the PLK1 target site at S723. The integrity of S723 in CtIP is necessary for the stimulation of DNA2, and phosphorylation of CtIP by PLK1 in vitro is consequently inhibitory, explaining why PLK1 restricts long-range resection. Our data support a model in which CDK-dependent phosphorylation of CtIP activates resection by MRN in S phase, and PLK1-mediated phosphorylation of CtIP disrupts CtIP stimulation of DNA2 to attenuate long-range resection later at G2/M.
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Affiliation(s)
- Ilaria Ceppi
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera italiana (USI), Bellinzona 6500, Switzerland
- Department of Biology, Institute of Biochemistry, Eidgenössische Technische Hochschule (ETH), Zürich 8093, Switzerland
| | - Elda Cannavo
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera italiana (USI), Bellinzona 6500, Switzerland
| | - Hélène Bret
- Institute for Integrative Biology of the Cell (I2BC), Commissariat à l'Energie Atomique, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Rosa Camarillo
- Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Sevilla 41080, Spain
- Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), Universidad de Sevilla-Consejo Superior de Investigaciones Científicas-Universidad Pablo de Olavide, Sevilla 41092, Spain
| | - Francesca Vivalda
- Institute of Molecular Cancer Research, University of Zürich, Zürich 8057, Switzerland
| | - Roshan Singh Thakur
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera italiana (USI), Bellinzona 6500, Switzerland
| | - Amador Romero-Franco
- Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Sevilla 41080, Spain
- Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), Universidad de Sevilla-Consejo Superior de Investigaciones Científicas-Universidad Pablo de Olavide, Sevilla 41092, Spain
| | - Alessandro A Sartori
- Institute of Molecular Cancer Research, University of Zürich, Zürich 8057, Switzerland
| | - Pablo Huertas
- Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Sevilla 41080, Spain
- Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), Universidad de Sevilla-Consejo Superior de Investigaciones Científicas-Universidad Pablo de Olavide, Sevilla 41092, Spain
| | - Raphaël Guérois
- Institute for Integrative Biology of the Cell (I2BC), Commissariat à l'Energie Atomique, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Petr Cejka
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera italiana (USI), Bellinzona 6500, Switzerland;
- Department of Biology, Institute of Biochemistry, Eidgenössische Technische Hochschule (ETH), Zürich 8093, Switzerland
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177
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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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178
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Qiu Y, Wei GW. Persistent spectral theory-guided protein engineering. NATURE COMPUTATIONAL SCIENCE 2023; 3:149-163. [PMID: 37637776 PMCID: PMC10456983 DOI: 10.1038/s43588-022-00394-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/22/2022] [Indexed: 08/29/2023]
Abstract
While protein engineering, which iteratively optimizes protein fitness by screening the gigantic mutational space, is constrained by experimental capacity, various machine learning models have substantially expedited protein engineering. Three-dimensional protein structures promise further advantages, but their intricate geometric complexity hinders their applications in deep mutational screening. Persistent homology, an established algebraic topology tool for protein structural complexity reduction, fails to capture the homotopic shape evolution during the filtration of a given data. This work introduces a Topology-offered protein Fitness (TopFit) framework to complement protein sequence and structure embeddings. Equipped with an ensemble regression strategy, TopFit integrates the persistent spectral theory, a new topological Laplacian, and two auxiliary sequence embeddings to capture mutation-induced topological invariant, shape evolution, and sequence disparity in the protein fitness landscape. The performance of TopFit is assessed by 34 benchmark datasets with 128,634 variants, involving a vast variety of protein structure acquisition modalities and training set size variations.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI, 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
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179
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Li AJ, Lu M, Desta I, Sundar V, Grigoryan G, Keating AE. Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs. Protein Sci 2023; 32:e4554. [PMID: 36564857 PMCID: PMC9854172 DOI: 10.1002/pro.4554] [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: 08/18/2022] [Revised: 11/15/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022]
Abstract
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.
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Affiliation(s)
- Alex J. Li
- Department of ChemistryMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Mindren Lu
- Department of Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Israel Desta
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Vikram Sundar
- Computational and Systems Biology ProgramMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Gevorg Grigoryan
- Department of Computer ScienceDartmouth CollegeHanoverNew HampshireUSA
| | - Amy E. Keating
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
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180
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Kretschmer S, Perry N, Zhang Y, Kortemme T. Multi-input drug-controlled switches of mammalian gene expression based on engineered nuclear hormone receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.01.526549. [PMID: 36778233 PMCID: PMC9915577 DOI: 10.1101/2023.02.01.526549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Protein-based switches that respond to different inputs to regulate cellular outputs, such as gene expression, are central to synthetic biology. For increased controllability, multi-input switches that integrate several cooperating and competing signals for the regulation of a shared output are of particular interest. The nuclear hormone receptor (NHR) superfamily offers promising starting points for engineering multi-input-controlled responses to clinically approved drugs. Starting from the VgEcR/RXR pair, we demonstrate that novel (multi-)drug regulation can be achieved by exchange of the ecdysone receptor (EcR) ligand binding domain (LBD) for other human NHR-derived LBDs. For responses activated to saturation by an agonist for the first LBD, we show that outputs can be boosted by an agonist targeting the second LBD. In combination with an antagonist, output levels are tunable by up to three simultaneously present small-molecule drugs. Such high-level control validates NHRs as a versatile, engineerable platform for programming multi-drug-controlled responses.
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Affiliation(s)
- Simon Kretschmer
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA,California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158, USA,Corresponding author:
| | - Nicholas Perry
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA,California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158, USA,University of California, Berkeley—University of California, San Francisco Joint Graduate Program in Bioengineering, San Francisco, CA, USA
| | - Yang Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA,California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA,California Quantitative Biosciences Institute (QBI) at UCSF, San Francisco, CA 94158, USA,University of California, Berkeley—University of California, San Francisco Joint Graduate Program in Bioengineering, San Francisco, CA, USA
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181
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Gomari MM, Tarighi P, Choupani E, Abkhiz S, Mohamadzadeh M, Rostami N, Sadroddiny E, Baammi S, Uversky VN, Dokholyan NV. Structural evolution of Delta lineage of SARS-CoV-2. Int J Biol Macromol 2023; 226:1116-1140. [PMID: 36435470 PMCID: PMC9683856 DOI: 10.1016/j.ijbiomac.2022.11.227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.
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Affiliation(s)
- Mohammad Mahmoudi Gomari
- Student Research Committee, Iran University of Medical Sciences, Tehran 1449614535, Iran,Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Parastoo Tarighi
- Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Edris Choupani
- Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Shadi Abkhiz
- Department of Medical Biotechnology, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Masoud Mohamadzadeh
- Department of Chemistry, Faculty of Sciences, University of Hormozgan, Bandar Abbas 7916193145, Iran
| | - Neda Rostami
- Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak 3848177584, Iran
| | - Esmaeil Sadroddiny
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran 1417613151, Iran
| | - Soukayna Baammi
- African Genome Centre (AGC), Mohammed VI Polytechnic University, Benguerir 43150, Morocco
| | - Vladimir N. Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33620, USA,Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia,Correspondence to: V.N. Uversky, Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33620, USA
| | - Nikolay V. Dokholyan
- Department of Pharmacology, Department of Biochemistry & Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 16802, USA,Corresponding author
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182
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Papadaki GF, Ani O, Florio TJ, Young MC, Danon JN, Sun Y, Dersh D, Sgourakis NG. Decoupling peptide binding from T cell receptor recognition with engineered chimeric MHC-I molecules. Front Immunol 2023; 14:1116906. [PMID: 36761745 PMCID: PMC9905809 DOI: 10.3389/fimmu.2023.1116906] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/10/2023] [Indexed: 01/26/2023] Open
Abstract
Major Histocompatibility Complex class I (MHC-I) molecules display self, viral or aberrant epitopic peptides to T cell receptors (TCRs), which employ interactions between complementarity-determining regions with both peptide and MHC-I heavy chain 'framework' residues to recognize specific Human Leucocyte Antigens (HLAs). The highly polymorphic nature of the HLA peptide-binding groove suggests a malleability of interactions within a common structural scaffold. Here, using structural data from peptide:MHC-I and pMHC:TCR structures, we first identify residues important for peptide and/or TCR binding. We then outline a fixed-backbone computational design approach for engineering synthetic molecules that combine peptide binding and TCR recognition surfaces from existing HLA allotypes. X-ray crystallography demonstrates that chimeric molecules bridging divergent HLA alleles can bind selected peptide antigens in a specified backbone conformation. Finally, in vitro tetramer staining and biophysical binding experiments using chimeric pMHC-I molecules presenting established antigens further demonstrate the requirement of TCR recognition on interactions with HLA framework residues, as opposed to interactions with peptide-centric Chimeric Antigen Receptors (CARs). Our results underscore a novel, structure-guided platform for developing synthetic HLA molecules with desired properties as screening probes for peptide-centric interactions with TCRs and other therapeutic modalities.
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Affiliation(s)
- Georgia F. Papadaki
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Omar Ani
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Tyler J. Florio
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael C. Young
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Julia N. Danon
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Yi Sun
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Devin Dersh
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nikolaos G. Sgourakis
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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183
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Opuu V, Simonson T. Enzyme redesign and genetic code expansion. Protein Eng Des Sel 2023; 36:gzad017. [PMID: 37879093 DOI: 10.1093/protein/gzad017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/27/2023] Open
Abstract
Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.
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Affiliation(s)
- Vaitea Opuu
- Institut Chimie Biologie Innovation (CNRS UMR8231), Ecole Supérieure de Physique et Chimie de Paris (ESPCI), 75005 Paris, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
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184
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Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics. PLoS Comput Biol 2023; 19:e1010457. [PMID: 36668672 PMCID: PMC9891523 DOI: 10.1371/journal.pcbi.1010457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/01/2023] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.
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185
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Singh DK, Gamboa RS, Singh AK, Walkemeier B, Van Leene J, De Jaeger G, Siddiqi I, Guerois R, Crismani W, Mercier R. The FANCC-FANCE-FANCF complex is evolutionarily conserved and regulates meiotic recombination. Nucleic Acids Res 2023; 51:2516-2528. [PMID: 36652992 PMCID: PMC10085685 DOI: 10.1093/nar/gkac1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/29/2022] [Accepted: 12/14/2022] [Indexed: 01/20/2023] Open
Abstract
At meiosis, programmed meiotic DNA double-strand breaks are repaired via homologous recombination, resulting in crossovers (COs). From a large excess of DNA double-strand breaks that are formed, only a small proportion gets converted into COs because of active mechanisms that restrict CO formation. The Fanconi anemia (FA) complex proteins AtFANCM, MHF1 and MHF2 were previously identified in a genetic screen as anti-CO factors that function during meiosis in Arabidopsis thaliana. Here, pursuing the same screen, we identify FANCC as a new anti-CO gene. FANCC was previously only identified in mammals because of low primary sequence conservation. We show that FANCC, and its physical interaction with FANCE-FANCF, is conserved from vertebrates to plants. Further, we show that FANCC, together with its subcomplex partners FANCE and FANCF, regulates meiotic recombination. Mutations of any of these three genes partially rescues CO-defective mutants, which is particularly marked in female meiosis. Functional loss of FANCC, FANCE, or FANCF results in synthetic meiotic catastrophe with the pro-CO factor MUS81. This work reveals that FANCC is conserved outside mammals and has an anti-CO role during meiosis together with FANCE and FANCF.
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Affiliation(s)
- Dipesh Kumar Singh
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Rigel Salinas Gamboa
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Avinash Kumar Singh
- CSIR-Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500007, India
| | - Birgit Walkemeier
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
| | - Jelle Van Leene
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent B-9052, Belgium.,Center for Plant Systems Biology, VIB, Ghent B-9052, Belgium
| | - Geert De Jaeger
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent B-9052, Belgium.,Center for Plant Systems Biology, VIB, Ghent B-9052, Belgium
| | - Imran Siddiqi
- CSIR-Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500007, India
| | - Raphael Guerois
- Institute for Integrative Biology of the Cell (I2BC), Commissariat à l'Energie Atomique, CNRS, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Wayne Crismani
- The DNA Repair and Recombination Laboratory, St Vincent's Institute of Medical Research, Melbourne 3065, Australia.,The Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Parkville, Victoria, Australia
| | - Raphael Mercier
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Cologne, Germany
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186
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Lee J, Campillo B, Hamidian S, Liu Z, Shorey M, St-Pierre F. Automating the High-Throughput Screening of Protein-Based Optical Indicators and Actuators. Biochemistry 2023; 62:169-177. [PMID: 36315460 PMCID: PMC9852035 DOI: 10.1021/acs.biochem.2c00357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Over the last 25 years, protein engineers have developed an impressive collection of optical tools to interface with biological systems: indicators to eavesdrop on cellular activity and actuators to poke and prod native processes. To reach the performance level required for their downstream applications, protein-based tools are usually sculpted by iterative rounds of mutagenesis. In each round, libraries of variants are made and evaluated, and the most promising hits are then retrieved, sequenced, and further characterized. Early efforts to engineer protein-based optical tools were largely manual, suffering from low throughput, human error, and tedium. Here, we describe approaches to automating the screening of libraries generated as colonies on agar, multiwell plates, and pooled populations of single-cell variants. We also briefly discuss emerging approaches for screening, including cell-free systems and machine learning.
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Affiliation(s)
- Jihwan Lee
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Beatriz Campillo
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shaminta Hamidian
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhuohe Liu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Matthew Shorey
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - François St-Pierre
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX 77005, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
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187
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Michel E, Cucuzza S, Mittl PRE, Zerbe O, Plückthun A. Improved Repeat Protein Stability by Combined Consensus and Computational Protein Design. Biochemistry 2023; 62:318-329. [PMID: 35657362 DOI: 10.1021/acs.biochem.2c00083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
High protein stability is an important feature for proteins used as therapeutics, as diagnostics, and in basic research. We have previously employed consensus design to engineer optimized Armadillo repeat proteins (ArmRPs) for sequence-specific recognition of linear epitopes with a modular binding mode. These designed ArmRPs (dArmRPs) feature high stability and are composed of M-type internal repeats that are flanked by N- and C-terminal capping repeats that protect the hydrophobic core from solvent exposure. While the overall stability of the designed ArmRPs is remarkably high, subsequent biochemical and biophysical experiments revealed that the N-capping repeat assumes a partially unfolded, solvent-accessible conformation for a small fraction of time that renders it vulnerable to proteolysis and aggregation. To overcome this problem, we have designed new N-caps starting from an M-type internal repeat using the Rosetta software. The superior stability of the computationally refined models was experimentally verified by circular dichroism and nuclear magnetic resonance spectroscopy. A crystal structure of a dArmRP containing the novel N-cap revealed that the enhanced stability correlates with an improved packing of this N-cap onto the hydrophobic core of the dArmRP. Hydrogen exchange experiments further show that the level of local unfolding of the N-cap is reduced by several orders of magnitude, resulting in increased resistance to proteolysis and weakened aggregation. As a first application of the novel N-cap, we determined the solution structure of a dArmRP with four internal repeats, which was previously impeded by the instability of the original N-cap.
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Affiliation(s)
- Erich Michel
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Stefano Cucuzza
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Peer R E Mittl
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Oliver Zerbe
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Andreas Plückthun
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
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188
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Wang X, Qin X, Tong L, Zheng J, Dong T, Wang X, Wang Y, Huang H, Yao B, Zhang H, Luo H. Improving the catalytic activity of a detergent-compatible serine protease by rational design. Microb Biotechnol 2023; 16:947-960. [PMID: 36636777 PMCID: PMC10128134 DOI: 10.1111/1751-7915.14218] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/14/2023] Open
Abstract
Serine proteases are among the most important biological additives in various industries such as detergents, leather, animal feed and food. A serine protease gene, Fgapt4, from Fusarium graminearum 2697 was identified, cloned and expressed in Pichia pastoris. The optimal pH and temperature of FgAPT4 were 8.5 and 40°C, respectively. The relative activity was >30% even at 10°C. It had a wide range of pH stability (4.0-12.0) and detergent compatibility. To improve the catalytic activity, a strategy combining molecular docking and evolutionary analysis was adopted. Twelve amino acid residue sites and three loops (A, B and C) were selected as potential hot spots that might play critical roles in the enzyme's functional properties. Twenty-eight mutants targeting changes in individual sites or loops were designed, and mutations with good performance were combined. The best mutant was FgAPT4-M3 (Q70N/D142S/A143S/loop C). The specific activity and catalytic efficiency of FgAPT4-M3 increased by 1.6 (1008.5 vs. 385.9 U/mg) and 2.2-fold (3565.1 vs. 1106.3/s/mM), respectively. Computational analyses showed that the greater flexibility of the substrate pocket may be responsible for the increased catalytic activity. In addition, its application in detergents indicated that FgAPT4-M3 has great potential in washing.
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Affiliation(s)
- Xiao Wang
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xing Qin
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lige Tong
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jie Zheng
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tao Dong
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolu Wang
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuan Wang
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huoqing Huang
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bin Yao
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Honglian Zhang
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huiying Luo
- State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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189
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Michael E, Saint-Jalme R, Mignon D, Simonson T. Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance. Front Mol Biosci 2023; 9:905588. [PMID: 36699702 PMCID: PMC9868620 DOI: 10.3389/fmolb.2022.905588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.
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190
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Pedersen GB, Blaschek L, Frandsen KEH, Noack LC, Persson S. Cellulose synthesis in land plants. MOLECULAR PLANT 2023; 16:206-231. [PMID: 36564945 DOI: 10.1016/j.molp.2022.12.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
All plant cells are surrounded by a cell wall that provides cohesion, protection, and a means of directional growth to plants. Cellulose microfibrils contribute the main biomechanical scaffold for most of these walls. The biosynthesis of cellulose, which typically is the most prominent constituent of the cell wall and therefore Earth's most abundant biopolymer, is finely attuned to developmental and environmental cues. Our understanding of the machinery that catalyzes and regulates cellulose biosynthesis has substantially improved due to recent technological advances in, for example, structural biology and microscopy. Here, we provide a comprehensive overview of the structure, function, and regulation of the cellulose synthesis machinery and its regulatory interactors. We aim to highlight important knowledge gaps in the field, and outline emerging approaches that promise a means to close those gaps.
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Affiliation(s)
- Gustav B Pedersen
- Copenhagen Plant Science Center (CPSC), Department of Plant & Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark
| | - Leonard Blaschek
- Copenhagen Plant Science Center (CPSC), Department of Plant & Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark
| | - Kristian E H Frandsen
- Copenhagen Plant Science Center (CPSC), Department of Plant & Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark
| | - Lise C Noack
- Copenhagen Plant Science Center (CPSC), Department of Plant & Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark
| | - Staffan Persson
- Copenhagen Plant Science Center (CPSC), Department of Plant & Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark; Joint International Research Laboratory of Metabolic & Developmental Sciences, State Key Laboratory of Hybrid Rice, SJTU-University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
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191
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Deckers J, Anbergen T, Hokke AM, de Dreu A, Schrijver DP, de Bruin K, Toner YC, Beldman TJ, Spangler JB, de Greef TFA, Grisoni F, van der Meel R, Joosten LAB, Merkx M, Netea MG, Mulder WJM. Engineering cytokine therapeutics. NATURE REVIEWS BIOENGINEERING 2023; 1:286-303. [PMID: 37064653 PMCID: PMC9933837 DOI: 10.1038/s44222-023-00030-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Cytokines have pivotal roles in immunity, making them attractive as therapeutics for a variety of immune-related disorders. However, the widespread clinical use of cytokines has been limited by their short blood half-lives and severe side effects caused by low specificity and unfavourable biodistribution. Innovations in bioengineering have aided in advancing our knowledge of cytokine biology and yielded new technologies for cytokine engineering. In this Review, we discuss how the development of bioanalytical methods, such as sequencing and high-resolution imaging combined with genetic techniques, have facilitated a better understanding of cytokine biology. We then present an overview of therapeutics arising from cytokine re-engineering, targeting and delivery, mRNA therapeutics and cell therapy. We also highlight the application of these strategies to adjust the immunological imbalance in different immune-mediated disorders, including cancer, infection and autoimmune diseases. Finally, we look ahead to the hurdles that must be overcome before cytokine therapeutics can live up to their full potential.
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Affiliation(s)
- Jeroen Deckers
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
| | - Tom Anbergen
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
| | - Ayla M. Hokke
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Anne de Dreu
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - David P. Schrijver
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Koen de Bruin
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Yohana C. Toner
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
| | - Thijs J. Beldman
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
| | - Jamie B. Spangler
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD USA
- Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Tom F. A. de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
- Centre for Living Technologies, Alliance Eindhoven University of Technology, Wageningen University & Research, Utrecht University and University Medical Center Utrecht (EWUU), Utrecht, Netherlands
| | - Francesca Grisoni
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
- Centre for Living Technologies, Alliance Eindhoven University of Technology, Wageningen University & Research, Utrecht University and University Medical Center Utrecht (EWUU), Utrecht, Netherlands
| | - Roy van der Meel
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Present Address: Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Leo A. B. Joosten
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
- Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Medical Genetics, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Maarten Merkx
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Present Address: Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
- Radboud Institute for Molecular Life Sciences (RIMLS), Radboud University Medical Centre, Nijmegen, Netherlands
- Department for Genomics and Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Willem J. M. Mulder
- Department of Internal Medicine and Radboud Center for Infectious diseases (RCI), Radboud University Medical Centre, Nijmegen, Netherlands
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Present Address: Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
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192
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Dandekar T, Kunz M. Design Principles of a Cell. Bioinformatics 2023. [DOI: 10.1007/978-3-662-65036-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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193
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Dodd-O J, Acevedo-Jake AM, Azizogli AR, Mulligan VK, Kumar VA. How to Design Peptides. Methods Mol Biol 2023; 2597:187-216. [PMID: 36374423 DOI: 10.1007/978-1-0716-2835-5_15] [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] [Indexed: 11/16/2022]
Abstract
Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.
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Affiliation(s)
- Joseph Dodd-O
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Amanda M Acevedo-Jake
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | | | | | - Vivek A Kumar
- York Center for Environmental Engineering and Science, New Jersey Institute of Technology, Newark, NJ, USA.
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194
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Integrated in silico-in vitro molecular modeling and design of halogenated phenylalanine-containing antihypertensive peptide inhibitors with halogen bonds to target human angiotensin-I-converting enzyme. Chem Phys 2023. [DOI: 10.1016/j.chemphys.2022.111732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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195
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Koehler Leman J, Bonneau R. Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling. Methods Mol Biol 2023; 2627:141-166. [PMID: 36959446 DOI: 10.1007/978-1-0716-2974-1_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely, RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
- Department of Computer Science, New York University, New York, NY, USA
- Center for Data Science, New York University, New York, NY, USA
- Prescient Design, a Genentech Accelerator, New York, NY, USA
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196
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Watkins AM, Das R. RNA 3D Modeling with FARFAR2, Online. Methods Mol Biol 2023; 2586:233-249. [PMID: 36705908 DOI: 10.1007/978-1-0716-2768-6_14] [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] [Indexed: 01/28/2023]
Abstract
Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA-Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2 . We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the "basic interface" to the webserver and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the "advanced interface." We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low-resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.
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Affiliation(s)
- Andrew M Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Prescient Design, Genentech, South San Francisco, CA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.
- Biophysics Program, Stanford University, Stanford, CA, USA.
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197
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Lugmayr W, Kotov V, Goessweiner-Mohr N, Wald J, DiMaio F, Marlovits TC. StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement. Nat Protoc 2023; 18:239-264. [PMID: 36323866 DOI: 10.1038/s41596-022-00757-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/08/2022] [Indexed: 01/13/2023]
Abstract
Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.
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Affiliation(s)
- Wolfgang Lugmayr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Vadim Kotov
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Evotec SE, Hamburg, Germany
| | - Nikolaus Goessweiner-Mohr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Johannes Kepler University, Institute of Biophysics, Linz, Austria
| | - Jiri Wald
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Frank DiMaio
- University of Washington, Department of Biochemistry, Seattle, WA, USA
| | - Thomas C Marlovits
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany. .,CSSB Centre for Structural Systems Biology, Hamburg, Germany. .,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany. .,Research Institute of Molecular Pathology (IMP), Vienna, Austria. .,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.
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198
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Zhang L, Xu W, Ma X, Sun X, Fan J, Wang Y. Virus-like Particles as Antiviral Vaccine: Mechanism, Design, and Application. BIOTECHNOL BIOPROC E 2023; 28:1-16. [PMID: 36627930 PMCID: PMC9817464 DOI: 10.1007/s12257-022-0107-8] [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: 04/01/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 01/09/2023]
Abstract
Virus-like particles (VLPs) are viral structural protein that are noninfectious as they do not contain viral genetic materials. They are safe and effective immune stimulators and play important roles in vaccine development because of their intrinsic immunogenicity to induce cellular and humoral immune responses. In the design of antiviral vaccine, VLPs based vaccines are appealing multifunctional candidates with the advantages such as self-assembling nanoscaled structures, repetitive surface epitopes, ease of genetic and chemical modifications, versatility as antigen presenting platforms, intrinsic immunogenicity, higher safety profile in comparison with live-attenuated vaccines and inactivated vaccines. In this review, we discuss the mechanism of VLPs vaccine inducing cellular and humoral immune responses. We outline the impact of size, shape, surface charge, antigen presentation, genetic and chemical modification, and expression systems when constructing effective VLPs based vaccines. Recent applications of antiviral VLPs vaccines and their clinical trials are summarized.
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Affiliation(s)
- Lei Zhang
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
| | - Wen Xu
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
| | - Xi Ma
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
| | - XiaoJing Sun
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
| | - JinBo Fan
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
| | - Yang Wang
- Xi'an Key Laboratory of Pathogenic Microorganism and Tumor Immunity, Department of Basic Medicine, Xi'an Medical University, Xi'an, 710021, Shaanxi China
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199
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Scietti L, Forneris F. Modeling of Protein Complexes. Methods Mol Biol 2023; 2627:349-371. [PMID: 36959458 DOI: 10.1007/978-1-0716-2974-1_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.
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Affiliation(s)
- Luigi Scietti
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
| | - Federico Forneris
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
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200
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Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. FRONTIERS IN BIOINFORMATICS 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [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: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
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