1
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Che X, Tao X, Chen J, Feng Y, Cui Z, Feng T, Fang Y, Wen H, Xue S. Mining Highly Active Oleate Hydratases by Structure Clustering, Sequence Clustering, and Ancestral Sequence Reconstruction. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2025; 73:7335-7346. [PMID: 40088169 DOI: 10.1021/acs.jafc.4c10815] [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: 03/17/2025]
Abstract
Oleate hydratases (Ohys) catalyze the conversion of oleic acid (OA) to 10-(R)-hydroxystearic acid (10-HSA), a compound widely used in the chemical industry. However, the limited activity of Ohys has hindered their broader applications. To address this limitation, we propose a novel strategy for mining highly active Ohys through structure clustering, sequence clustering, and ancestral sequence reconstruction (SSA strategy). Structure clustering via AI-driven protein structure prediction followed by classification enhanced the ability to mine target Ohys. Ancestral enzyme reconstruction was carried out based on mining results from both structure and sequence clustering. This strategy significantly reduces the time and cost of the discovery process. Among the 1304 Ohys screened via SSA, 13 candidates were selected. Seven candidates demonstrated high activity. Ohy 64, identified through structure clustering, exhibited the highest activity. Ancestral enzymes that were reconstructed from structure clustering targets were 3 times more likely to exhibit high catalytic activity than those identified through sequence clustering. Four critical, hydrophobic residues were identified through structure and sequence comparisons between StOhy and targets mined by SSA. Site-directed mutagenesis revealed that these hydrophobic residues conferred varying levels of enzyme activity, confirming that increased hydrophobicity at these positions enhances cofactor FAD binding, thus improving enzyme catalytic efficiency.
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Affiliation(s)
- Xinyu Che
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xiangyu Tao
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, Liaoning, China
| | | | - Yanbin Feng
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Ziheng Cui
- National Energy R&D Center of Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Ting Feng
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Yunming Fang
- National Energy R&D Center of Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Han Wen
- DP Technology, Beijing 100089, China
- AI for Science Institute, Beijing 100000, China
- Institute for Advanced Algorithms Research, Shanghai 200233, China
- Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing 100871, China
- State Key Laboratory of Medical Proteomics, Beijing 102206, China
| | - Song Xue
- MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, Liaoning, China
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2
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Islam F, Basilone N, Yoo V, Ball E, Shilton B. Evolutionary analysis of Quinone Reductases 1 and 2 suggests that NQO2 evolved to function as a pseudoenzyme. Protein Sci 2024; 33:e5234. [PMID: 39584664 PMCID: PMC11586865 DOI: 10.1002/pro.5234] [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: 07/10/2024] [Revised: 09/30/2024] [Accepted: 11/09/2024] [Indexed: 11/26/2024]
Abstract
Quinone reductases 1 and 2 (NQO1 and NQO2) are paralogous FAD-linked enzymes found in all amniotes. NQO1 and NQO2 have similar structures, and both catalyze the reduction of quinones and other electrophiles; however, the two enzymes differ in their cosubstrate preference. While NQO1 can use both redox couples NADH and NADPH, NQO2 is almost inactive with these cosubstrates and instead must use dihydronicotinamide riboside (NRH) and small synthetic cosubstrates such as N-benzyl-dihydronicotinamide (BNAH) for efficient catalysis. We used ancestral sequence reconstruction to investigate the catalytic properties of a predicted common ancestor and two additional ancestors from each of the evolutionary pathways to extant NQO1 and NQO2. In all cases, the small nicotinamide cosubstrates NRH and BNAH were good cosubstrates for the common ancestor and the enzymes along both the NQO1 and NQO2 lineages. In contrast, with NADH as cosubstrate, extant NQO1 evolved to a catalytic efficiency 100 times higher than the common ancestor, while NQO2 has evolved to a catalytic efficiency 3000 times lower than the common ancestor. The evolutionary analysis combined with site-directed mutagenesis revealed a potential site of interaction for the ADP portion of NAD(P)H in NQO1 that is altered in charge and structure in NQO2. The results indicate that while NQO1 evolved to have greater efficiency with NAD(P)H, befitting an enzymatic function in cells, NQO2 was under selective pressure to acquire extremely low catalytic efficiency with NAD(P)H. These divergent trajectories have implications for the functions of both enzymes.
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Affiliation(s)
- Faiza Islam
- Department of BiochemistryUniversity of Western OntarioLondonOntarioCanada
| | - Nicoletta Basilone
- Department of BiochemistryUniversity of Western OntarioLondonOntarioCanada
| | - Vania Yoo
- Department of BiochemistryUniversity of Western OntarioLondonOntarioCanada
| | - Eric Ball
- Department of BiochemistryUniversity of Western OntarioLondonOntarioCanada
| | - Brian Shilton
- Department of BiochemistryUniversity of Western OntarioLondonOntarioCanada
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3
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Zhu XX, Zheng WQ, Xia ZW, Chen XR, Jin T, Ding XW, Chen FF, Chen Q, Xu JH, Kong XD, Zheng GW. Evolutionary insights into the stereoselectivity of imine reductases based on ancestral sequence reconstruction. Nat Commun 2024; 15:10330. [PMID: 39609402 PMCID: PMC11605051 DOI: 10.1038/s41467-024-54613-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 11/14/2024] [Indexed: 11/30/2024] Open
Abstract
The stereoselectivity of enzymes plays a central role in asymmetric biocatalytic reactions, but there remains a dearth of evolution-driven biochemistry studies investigating the evolutionary trajectory of this vital property. Imine reductases (IREDs) are one such enzyme that possesses excellent stereoselectivity, and stereocomplementary members are pervasive in the family. However, the regulatory mechanism behind stereocomplementarity remains cryptic. Herein, we reconstruct a panel of active ancestral IREDs and trace the evolution of stereoselectivity from ancestors to extant IREDs. Combined with coevolution analysis, we reveal six historical mutations capable of recapitulating stereoselectivity evolution. An investigation of the mechanism with X-ray crystallography shows that they collectively reshape the substrate-binding pocket to regulate stereoselectivity inversion. In addition, we construct an empirical fitness landscape and discover that epistasis is prevalent in stereoselectivity evolution. Our findings emphasize the power of ASR in circumventing the time-consuming large-scale mutagenesis library screening for identifying mutations that change functions and support a Darwinian premise from a molecular perspective that the evolution of biological functions is a stepwise process.
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Affiliation(s)
- Xin-Xin Zhu
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Wen-Qing Zheng
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Zi-Wei Xia
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Xin-Ru Chen
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Tian Jin
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Xu-Wei Ding
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Fei-Fei Chen
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Qi Chen
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Jian-He Xu
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China
| | - Xu-Dong Kong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China.
| | - Gao-Wei Zheng
- State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, China.
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4
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Choi S, Seo S, Kim BJ, Park C, Park S. PIDiff: Physics informed diffusion model for protein pocket-specific 3D molecular generation. Comput Biol Med 2024; 180:108865. [PMID: 39067153 DOI: 10.1016/j.compbiomed.2024.108865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 07/02/2024] [Accepted: 07/07/2024] [Indexed: 07/30/2024]
Abstract
Designing drugs capable of binding to the structure of target proteins for treating diseases is essential in drug development. Recent remarkable advancements in geometric deep learning have led to unprecedented progress in three-dimensional (3D) generation of ligands that can bind to the protein pocket. However, most existing methods primarily focus on modeling the geometric information of ligands in 3D space. Consequently, these methods fail to consider that the binding of proteins and ligands is a phenomenon driven by intrinsic physicochemical principles. Motivated by this understanding, we propose PIDiff, a model for generating molecules by accounting in the physicochemical principles of protein-ligand binding. Our model learns not only the structural information of proteins and ligands but also to minimize the binding free energy between them. To evaluate the proposed model, we introduce an experimental framework that surpasses traditional assessment methods by encompassing various essential aspects for the practical application of generative models to actual drug development. The results confirm that our model outperforms baseline models on the CrossDocked2020 benchmark dataset, demonstrating its superiority. Through diverse experiments, we have illustrated the promising potential of the proposed model in practical drug development.
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Affiliation(s)
- Seungyeon Choi
- Department of Computer Science, Yonsei University, Seoul, 03722, Republic of Korea
| | - Sangmin Seo
- Department of Computer Science, Yonsei University, Seoul, 03722, Republic of Korea
| | - Byung Ju Kim
- UBLBio Corporation, Suwon, 16679, Republic of Korea
| | - Chihyun Park
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, 24341, Republic of Korea
| | - Sanghyun Park
- Department of Computer Science, Yonsei University, Seoul, 03722, Republic of Korea.
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5
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Wang J, Miao Y. Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. J Chem Theory Comput 2024; 20:5829-5841. [PMID: 39002136 DOI: 10.1021/acs.jctc.4c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.
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Affiliation(s)
- Jinan Wang
- Computational Medicine Program and Department of Pharmacology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Yinglong Miao
- Computational Medicine Program and Department of Pharmacology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, United States
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6
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Chisholm LO, Orlandi KN, Phillips SR, Shavlik MJ, Harms MJ. Ancestral Reconstruction and the Evolution of Protein Energy Landscapes. Annu Rev Biophys 2024; 53:127-146. [PMID: 38134334 PMCID: PMC11192866 DOI: 10.1146/annurev-biophys-030722-125440] [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: 12/24/2023]
Abstract
A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.
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Affiliation(s)
- Lauren O Chisholm
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon, USA;
- Institute of Molecular Biology, University of Oregon, Eugene, Oregon, USA
| | - Kona N Orlandi
- Institute of Molecular Biology, University of Oregon, Eugene, Oregon, USA
- Department of Biology, University of Oregon, Eugene, Oregon, USA
| | - Sophia R Phillips
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon, USA;
- Institute of Molecular Biology, University of Oregon, Eugene, Oregon, USA
| | - Michael J Shavlik
- Institute of Molecular Biology, University of Oregon, Eugene, Oregon, USA
- Department of Biology, University of Oregon, Eugene, Oregon, USA
| | - Michael J Harms
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon, USA;
- Institute of Molecular Biology, University of Oregon, Eugene, Oregon, USA
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7
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Spassov DS. Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models. Int J Mol Sci 2024; 25:7124. [PMID: 39000229 PMCID: PMC11240957 DOI: 10.3390/ijms25137124] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.
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Affiliation(s)
- Danislav S Spassov
- Drug Design and Bioinformatics Lab, Department of Chemistry, Faculty of Pharmacy, Medical University of Sofia, 1000 Sofia, Bulgaria
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8
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Schmitt L, Hoppe J, Cea-Medina P, Bruch PM, Krings KS, Lechtenberg I, Drießen D, Peter C, Bhatia S, Dietrich S, Stork B, Fritz G, Gohlke H, Müller TJJ, Wesselborg S. Novel meriolin derivatives potently inhibit cell cycle progression and transcription in leukemia and lymphoma cells via inhibition of cyclin-dependent kinases (CDKs). Cell Death Discov 2024; 10:279. [PMID: 38862521 PMCID: PMC11167047 DOI: 10.1038/s41420-024-02056-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/13/2024] Open
Abstract
A key feature of cancer is the disruption of cell cycle regulation, which is characterized by the selective and abnormal activation of cyclin-dependent kinases (CDKs). Consequently, targeting CDKs via meriolins represents an attractive therapeutic approach for cancer therapy. Meriolins represent a semisynthetic compound class derived from meridianins and variolins with a known CDK inhibitory potential. Here, we analyzed the two novel derivatives meriolin 16 and meriolin 36 in comparison to other potent CDK inhibitors and could show that they displayed a high cytotoxic potential in different lymphoma and leukemia cell lines as well as in primary patient-derived lymphoma and leukemia cells. In a kinome screen, we showed that meriolin 16 and 36 prevalently inhibited most of the CDKs (such as CDK1, 2, 3, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 20). In drug-to-target modeling studies, we predicted a common binding mode of meriolin 16 and 36 to the ATP-pocket of CDK2 and an additional flipped binding for meriolin 36. We could show that cell cycle progression and proliferation were blocked by abolishing phosphorylation of retinoblastoma protein (a major target of CDK2) at Ser612 and Thr82. Moreover, meriolin 16 prevented the CDK9-mediated phosphorylation of RNA polymerase II at Ser2 which is crucial for transcription initiation. This renders both meriolin derivatives as valuable anticancer drugs as they target three different Achilles' heels of the tumor: (1) inhibition of cell cycle progression and proliferation, (2) prevention of transcription, and (3) induction of cell death.
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Affiliation(s)
- Laura Schmitt
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Julia Hoppe
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Pablo Cea-Medina
- Institute for Pharmaceutical and Medicinal Chemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Peter-Martin Bruch
- Department of Hematology, Oncology and Clinical Immunology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany
- Department of Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Düsseldorf, Germany
| | - Karina S Krings
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Ilka Lechtenberg
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Daniel Drießen
- Institute of Organic Chemistry and Macromolecular Chemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Christoph Peter
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Sanil Bhatia
- Department of Pediatric Oncology, Hematology and Clinical Immunology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Sascha Dietrich
- Department of Hematology, Oncology and Clinical Immunology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany
- Department of Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Düsseldorf, Germany
| | - Björn Stork
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Gerhard Fritz
- Institute of Toxicology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Center (JSC) and Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Thomas J J Müller
- Institute of Organic Chemistry and Macromolecular Chemistry, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Sebastian Wesselborg
- Institute for Molecular Medicine I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Düsseldorf, Germany.
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9
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Paul JW, Muratcioğlu S, Kuriyan J. A fluorescence-based sensor for calibrated measurement of protein kinase stability in live cells. Protein Sci 2024; 33:e5023. [PMID: 38801214 PMCID: PMC11129626 DOI: 10.1002/pro.5023] [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: 01/04/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024]
Abstract
Oncogenic mutations can destabilize signaling proteins, resulting in increased or unregulated activity. Thus, there is considerable interest in mapping the relationship between mutations and the stability of signaling proteins, to better understand the consequences of oncogenic mutations and potentially inform the development of new therapeutics. Here, we develop a tool to study protein-kinase stability in live mammalian cells and the effects of the HSP90 chaperone system on the stability of these kinases. We determine the expression levels of protein kinases by monitoring the fluorescence of fluorescent proteins fused to those kinases, normalized to that of co-expressed reference fluorescent proteins. We used this tool to study the dependence of Src- and Raf-family kinases on the HSP90 system. We demonstrate that this sensor reports on destabilization induced by oncogenic mutations in these kinases. We also show that Src-homology 2 and Src-homology 3 domains, which are required for autoinhibition of Src-family kinases, stabilize these kinase domains in the cell. Our expression-calibrated sensor enables the facile characterization of the effects of mutations and small-molecule drugs on protein-kinase stability.
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Affiliation(s)
- Joseph W. Paul
- Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- California Institute for Quantitative Bioscience (QB3)University of CaliforniaBerkeleyCaliforniaUSA
| | - Serena Muratcioğlu
- Department of BiochemistryVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - John Kuriyan
- Department of BiochemistryVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of ChemistryVanderbilt UniversityNashvilleTennesseeUSA
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10
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Nixon C, Lim SA, Sternke M, Barrick D, Harms MJ, Marqusee S. The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins. Protein Sci 2024; 33:e5011. [PMID: 38747388 PMCID: PMC11094778 DOI: 10.1002/pro.5011] [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: 06/29/2023] [Revised: 04/02/2024] [Accepted: 04/23/2024] [Indexed: 05/19/2024]
Abstract
A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.
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Affiliation(s)
- Charlotte Nixon
- Department of Molecular and Cell BiologyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Shion A. Lim
- Department of Molecular and Cell BiologyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Matt Sternke
- The T.C. Jenkins Department of BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Doug Barrick
- The T.C. Jenkins Department of BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Michael J. Harms
- Department of Chemistry and BiochemistryUniversity of OregonEugeneOregonUSA
| | - Susan Marqusee
- Department of Molecular and Cell BiologyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Department of ChemistryUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- California Institute for Quantitative Biosciences (QB3)BerkeleyCaliforniaUSA
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11
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Wang J, Miao Y. Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592668. [PMID: 38766067 PMCID: PMC11100592 DOI: 10.1101/2024.05.06.592668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.
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Affiliation(s)
- Jinan Wang
- Computational Medicine Program and Department of Pharmacology, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina, USA 27599
| | - Yinglong Miao
- Computational Medicine Program and Department of Pharmacology, University of North Carolina – Chapel Hill, Chapel Hill, North Carolina, USA 27599
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12
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Reddy KD, Rasool B, Akher FB, Kutlešić N, Pant S, Boudker O. Evolutionary analysis reveals the origin of sodium coupling in glutamate transporters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.03.569786. [PMID: 38106174 PMCID: PMC10723334 DOI: 10.1101/2023.12.03.569786] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Secondary active membrane transporters harness the energy of ion gradients to concentrate their substrates. Homologous transporters evolved to couple transport to different ions in response to changing environments and needs. The bases of such diversification, and thus principles of ion coupling, are unexplored. Employing phylogenetics and ancestral protein reconstruction, we investigated sodium-coupled transport in prokaryotic glutamate transporters, a mechanism ubiquitous across life domains and critical to neurotransmitter recycling in humans. We found that the evolutionary transition from sodium-dependent to independent substrate binding to the transporter preceded changes in the coupling mechanism. Structural and functional experiments suggest that the transition entailed allosteric mutations, making sodium binding dispensable without affecting ion-binding sites. Allosteric tuning of transporters' energy landscapes might be a widespread route of their functional diversification.
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Affiliation(s)
- Krishna D. Reddy
- Dept. of Physiology & Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
| | - Burha Rasool
- Dept. of Physiology & Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
| | - Farideh Badichi Akher
- Dept. of Physiology & Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
| | - Nemanja Kutlešić
- Dept. of Physiology & Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
| | - Swati Pant
- Dept. of Biochemistry, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
| | - Olga Boudker
- Dept. of Physiology & Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
- Howard Hughes Medical Institute, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA
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13
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Jones BS, Ross CM, Foley G, Pozhydaieva N, Sharratt JW, Kress N, Seibt LS, Thomson RES, Gumulya Y, Hayes MA, Gillam EMJ, Flitsch SL. Engineering Biocatalysts for the C-H Activation of Fatty Acids by Ancestral Sequence Reconstruction. Angew Chem Int Ed Engl 2024; 63:e202314869. [PMID: 38163289 DOI: 10.1002/anie.202314869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/03/2024]
Abstract
Selective, one-step C-H activation of fatty acids from biomass is an attractive concept in sustainable chemistry. Biocatalysis has shown promise for generating high-value hydroxy acids, but to date enzyme discovery has relied on laborious screening and produced limited hits, which predominantly oxidise the subterminal positions of fatty acids. Herein we show that ancestral sequence reconstruction (ASR) is an effective tool to explore the sequence-activity landscape of a family of multidomain, self-sufficient P450 monooxygenases. We resurrected 11 catalytically active CYP116B ancestors, each with a unique regioselectivity fingerprint that varied from subterminal in the older ancestors to mid-chain in the lineage leading to the extant, P450-TT. In lineages leading to extant enzymes in thermophiles, thermostability increased from ancestral to extant forms, as expected if thermophily had arisen de novo. Our studies show that ASR can be applied to multidomain enzymes to develop active, self-sufficient monooxygenases as regioselective biocatalysts for fatty acid hydroxylation.
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Affiliation(s)
- Bethan S Jones
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
| | - Connie M Ross
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, 4072, Australia
| | - Gabriel Foley
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, 4072, Australia
| | - Nadiia Pozhydaieva
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
| | - Joseph W Sharratt
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
| | - Nico Kress
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
| | - Lisa S Seibt
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
| | - Raine E S Thomson
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, 4072, Australia
| | - Yosephine Gumulya
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, 4072, Australia
| | - Martin A Hayes
- Compound Synthesis and Management, Discovery Sciences, R&D, AstraZeneca, Gothenburg, SE
| | - Elizabeth M J Gillam
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, 4072, Australia
| | - Sabine L Flitsch
- School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, UK
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14
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Sennett MA, Theobald DL. Extant Sequence Reconstruction: The Accuracy of Ancestral Sequence Reconstructions Evaluated by Extant Sequence Cross-Validation. J Mol Evol 2024; 92:181-206. [PMID: 38502220 PMCID: PMC10978691 DOI: 10.1007/s00239-024-10162-3] [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: 07/12/2023] [Accepted: 02/20/2024] [Indexed: 03/21/2024]
Abstract
Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term "extant sequence reconstruction" (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein.
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Affiliation(s)
- Michael A Sennett
- Department of Biochemistry, Brandeis University, Waltham, MA, 02453, USA
| | - Douglas L Theobald
- Department of Biochemistry, Brandeis University, Waltham, MA, 02453, USA.
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15
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Monteiro da Silva G, Cui JY, Dalgarno DC, Lisi GP, Rubenstein BM. High-throughput prediction of protein conformational distributions with subsampled AlphaFold2. Nat Commun 2024; 15:2464. [PMID: 38538622 PMCID: PMC10973385 DOI: 10.1038/s41467-024-46715-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/28/2024] [Indexed: 04/12/2024] Open
Abstract
This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.
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Affiliation(s)
| | - Jennifer Y Cui
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA
| | | | - George P Lisi
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA
- Brown University Department of Chemistry, Providence, RI, USA
| | - Brenda M Rubenstein
- Brown University Department of Molecular and Cell Biology and Biochemistry, Providence, RI, USA.
- Brown University Department of Chemistry, Providence, RI, USA.
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16
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Brown BP, Stein RA, Meiler J, Mchaourab HS. Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations. J Chem Theory Comput 2024; 20:1434-1447. [PMID: 38215214 PMCID: PMC10867840 DOI: 10.1021/acs.jctc.3c01081] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024]
Abstract
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.
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Affiliation(s)
- Benjamin P. Brown
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
| | - Richard A. Stein
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig, SAC 04103, Germany
| | - Hassane S. Mchaourab
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
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17
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da Silva GM, Cui JY, Dalgarno DC, Lisi GP, Rubenstein BM. Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550545. [PMID: 37546747 PMCID: PMC10402055 DOI: 10.1101/2023.07.25.550545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, NMR analysis, and evolution.
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Affiliation(s)
- Gabriel Monteiro da Silva
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Providence, RI, USA
| | - Jennifer Y Cui
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Providence, RI, USA
| | | | - George P Lisi
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University Department of Chemistry, Providence, RI, USA
| | - Brenda M Rubenstein
- Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University Department of Chemistry, Providence, RI, USA
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18
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Paul JW, Muratcioğlu S, Kuriyan J. A Fluorescence-Based Sensor for Calibrated Measurement of Protein Kinase Stability in Live Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570636. [PMID: 38106090 PMCID: PMC10723428 DOI: 10.1101/2023.12.07.570636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Oncogenic mutations can destabilize signaling proteins, resulting in increased or unregulated activity. Thus, there is considerable interest in mapping the relationship between mutations and the stability of proteins, to better understand the consequences of oncogenic mutations and potentially inform the development of new therapeutics. Here, we develop a tool to study protein-kinase stability in live mammalian cells and the effects of the HSP90 chaperone system on the stability of these kinases. We monitor the fluorescence of kinases fused to a fluorescent protein relative to that of a co-expressed reference fluorescent protein. We used this tool to study the dependence of Src- and Raf-family kinases on the HSP90 system. We demonstrate that this sensor reports on destabilization induced by oncogenic mutations in these kinases. We also show that Src-homology 2 (SH2) and Src-homology 3 (SH3) domains, which are required for autoinhibition of Src-family kinases, stabilize these kinase domains in the cell. Our expression-calibrated sensor enables the facile characterization of the effects of mutations and small-molecule drugs on protein-kinase stability.
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Affiliation(s)
- Joseph W. Paul
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720 USA
- California Institute for Quantitative Bioscience (QB3), University of California, Berkeley, CA, 94720 USA
| | - Serena Muratcioğlu
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, 37232 USA
| | - John Kuriyan
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, 37232 USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37240 USA
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19
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Brown BP, Stein RA, Meiler J, Mchaourab H. Approximating conformational Boltzmann distributions with AlphaFold2 predictions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.06.552168. [PMID: 37609301 PMCID: PMC10441281 DOI: 10.1101/2023.08.06.552168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Protein dynamics are intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria are evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, the computational and experimental methods capable of quantifying them are limited to systems of modest size. Here, we demonstrate that AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions, which we evaluate through examination of the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Further, we show that contact distance probability distributions generated by AlphaFold2 are sensitive to points mutations thus AF2 can predict the structural effects of mutations in some systems. We anticipate that our approach will be a valuable tool to model the thermodynamics of conformational changes in large biomolecular systems.
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Affiliation(s)
- Benjamin P. Brown
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
| | - Richard A. Stein
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC 04103, Germany
| | - Hassane Mchaourab
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. Nashville, TN 37232, USA
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20
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Nixon C, Lim SA, Sternke M, Barrick D, Harms M, Marqusee S. The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.29.547063. [PMID: 37425932 PMCID: PMC10327145 DOI: 10.1101/2023.06.29.547063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
A protein sequence encodes its energy landscape - all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs - questioning the differences and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the overall consensus protein is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted region is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order couplings using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.
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Affiliation(s)
- Charlotte Nixon
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Shion A Lim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Matt Sternke
- The T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218
| | - Doug Barrick
- The T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218
| | - Mike Harms
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR 97403
| | - Susan Marqusee
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720
- California Institute for Quantitative Biosciences (QB3), Berkeley
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21
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Bajaj T, Kuriyan J, Gee CL. Crystal structure of the kinase domain of a receptor tyrosine kinase from a choanoflagellate, Monosiga brevicollis. PLoS One 2023; 18:e0276413. [PMID: 37310965 DOI: 10.1371/journal.pone.0276413] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/28/2023] [Indexed: 06/15/2023] Open
Abstract
Genomic analysis of the unicellular choanoflagellate, Monosiga brevicollis (MB), revealed the remarkable presence of cell signaling and adhesion protein domains that are characteristically associated with metazoans. Strikingly, receptor tyrosine kinases, one of the most critical elements of signal transduction and communication in metazoans, are present in choanoflagellates. We determined the crystal structure at 1.95 Å resolution of the kinase domain of the M. brevicollis receptor tyrosine kinase C8 (RTKC8, a member of the choanoflagellate receptor tyrosine kinase C family) bound to the kinase inhibitor staurospaurine. The chonanoflagellate kinase domain is closely related in sequence to mammalian tyrosine kinases (~ 40% sequence identity to the human Ephrin kinase domain EphA3) and, as expected, has the canonical protein kinase fold. The kinase is structurally most similar to human Ephrin (EphA5), even though the extracellular sensor domain is completely different from that of Ephrin. The RTKC8 kinase domain is in an active conformation, with two staurosporine molecules bound to the kinase, one at the active site and another at the peptide-substrate binding site. To our knowledge this is the first example of staurospaurine binding in the Aurora A activation segment (AAS). We also show that the RTKC8 kinase domain can phosphorylate tyrosine residues in peptides from its C-terminal tail segment which is presumably the mechanism by which it transmits the extracellular stimuli to alter cellular function.
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Affiliation(s)
- Teena Bajaj
- Graduate Program in Comparative Biochemistry, University of California, Berkeley, Berkeley, California, United States of America
| | - John Kuriyan
- Graduate Program in Comparative Biochemistry, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
- Department of Chemistry, University of California, Berkeley, Berkeley, California, United States of America
- Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, California, United States of America
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, California, United States of America
| | - Christine L Gee
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
- Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, California, United States of America
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, California, United States of America
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22
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Ahmed F, Yao XQ, Hamelberg D. Conserved Conformational Dynamics Reveal a Key Dynamic Residue in the Gatekeeper Loop of Human Cyclophilins. J Phys Chem B 2023; 127:3139-3150. [PMID: 36989346 PMCID: PMC10108351 DOI: 10.1021/acs.jpcb.2c08650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Cyclophilins are ubiquitous human enzymes that catalyze peptidyl-prolyl cis-trans isomerization in protein substrates. Of the 17 unique isoforms, five closely related isoforms (CypA-E) are found in various environments and participate in diverse cellular processes, yet all have similar structures and the same core catalytic function. The question is what key residues are behind the conserved function of these enzymes. Here, conformational dynamics are compared across these isoforms to detect conserved dynamics essential for the catalytic activity of cyclophilins. A set of key dynamic residues, defined by the most dynamically conserved positions, are identified in the gatekeeper 2 region. The highly conserved glycine (Gly80) in this region is predicted to underlie the local flexibility, which is further tested by molecular dynamics simulations performed on mutants (G80A) of CypE and CypA. The mutation leads to decreased flexibility of CypE and CypA during substrate binding but increased flexibility during catalysis. Dynamical changes occur in the mutated region and a distal loop downstream of the mutation site in sequence. Examinations of the mutational effect on catalysis show that both mutated CypE and CypA exhibit shifted binding free energies of the substrate under distinct isomer conformations. The results suggest a loss of function in the mutated CypE and CypA. These catalytic changes by the mutation are likely independent of the substrate sequence, at least in CypA. Our work presents a method to identify function-related key residues in proteins.
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Affiliation(s)
- Furyal Ahmed
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
- Agnes Scott College, Decatur, Georgia 30030, United States
| | - Xin-Qiu Yao
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Donald Hamelberg
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
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23
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Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket. J Chem Theory Comput 2023; 19:733-745. [PMID: 36706316 DOI: 10.1021/acs.jctc.2c01194] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.
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24
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Nam K, Wolf-Watz M. Protein dynamics: The future is bright and complicated! STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2023; 10:014301. [PMID: 36865927 PMCID: PMC9974214 DOI: 10.1063/4.0000179] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Biological life depends on motion, and this manifests itself in proteins that display motion over a formidable range of time scales spanning from femtoseconds vibrations of atoms at enzymatic transition states, all the way to slow domain motions occurring on micro to milliseconds. An outstanding challenge in contemporary biophysics and structural biology is a quantitative understanding of the linkages among protein structure, dynamics, and function. These linkages are becoming increasingly explorable due to conceptual and methodological advances. In this Perspective article, we will point toward future directions of the field of protein dynamics with an emphasis on enzymes. Research questions in the field are becoming increasingly complex such as the mechanistic understanding of high-order interaction networks in allosteric signal propagation through a protein matrix, or the connection between local and collective motions. In analogy to the solution to the "protein folding problem," we argue that the way forward to understanding these and other important questions lies in the successful integration of experiment and computation, while utilizing the present rapid expansion of sequence and structure space. Looking forward, the future is bright, and we are in a period where we are on the doorstep to, at least in part, comprehend the importance of dynamics for biological function.
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Affiliation(s)
- Kwangho Nam
- Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, USA
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25
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Cortez LM, Morrison AJ, Garen CR, Patterson S, Uyesugi T, Petrosyan R, Sekar RV, Harms MJ, Woodside MT, Sim VL. Probing the origin of prion protein misfolding via reconstruction of ancestral proteins. Protein Sci 2022; 31:e4477. [PMID: 36254680 PMCID: PMC9667828 DOI: 10.1002/pro.4477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 12/13/2022]
Abstract
Prion diseases are fatal neurodegenerative diseases caused by pathogenic misfolding of the prion protein, PrP. They are transmissible between hosts, and sometimes between different species, as with transmission of bovine spongiform encephalopathy to humans. Although PrP is found in a wide range of vertebrates, prion diseases are seen only in certain mammals, suggesting that infectious misfolding was a recent evolutionary development. To explore when PrP acquired the ability to misfold infectiously, we reconstructed the sequences of ancestral versions of PrP from the last common primate, primate-rodent, artiodactyl, placental, bird, and amniote. Recombinant ancestral PrPs were then tested for their ability to form β-sheet aggregates, either spontaneously or when seeded with infectious prion strains from human, cervid, or rodent species. The ability to aggregate developed after the oldest ancestor (last common amniote), and aggregation capabilities diverged along evolutionary pathways consistent with modern-day susceptibilities. Ancestral bird PrP could not be seeded with modern-day prions, just as modern-day birds are resistant to prion disease. Computational modeling of structures suggested that differences in helix 2 could account for the resistance of ancestral bird PrP to seeding. Interestingly, ancestral primate PrP could be converted by all prion seeds, including both human and cervid prions, raising the possibility that species descended from an ancestral primate have retained the susceptibility to conversion by cervid prions. More generally, the results suggest that susceptibility to prion disease emerged prior to ~100 million years ago, with placental mammals possibly being generally susceptible to disease.
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Affiliation(s)
- Leonardo M. Cortez
- Centre for Prions and Protein Folding DiseasesUniversity of AlbertaEdmontonAlbertaCanada
- Division of Neurology, Department of MedicineUniversity of AlbertaEdmontonAlbertaCanada
- Neuroscience and Mental Health InstituteUniversity of AlbertaEdmontonAlbertaCanada
| | - Anneliese J. Morrison
- Institute of Molecular BiologyUniversity of OregonEugeneOregonUSA
- Department of Chemistry and BiochemistryUniversity of OregonEugeneOregonUSA
| | - Craig R. Garen
- Department of PhysicsUniversity of AlbertaEdmontonAlbertaCanada
| | - Sawyer Patterson
- Centre for Prions and Protein Folding DiseasesUniversity of AlbertaEdmontonAlbertaCanada
| | - Toshi Uyesugi
- Department of PhysicsUniversity of AlbertaEdmontonAlbertaCanada
| | - Rafayel Petrosyan
- Department of PhysicsUniversity of AlbertaEdmontonAlbertaCanada
- Present address:
Zaven & Sonia Akian College of Science and EngineeringAmerican University of ArmeniaYerevanArmenia
| | | | - Michael J. Harms
- Institute of Molecular BiologyUniversity of OregonEugeneOregonUSA
- Department of Chemistry and BiochemistryUniversity of OregonEugeneOregonUSA
| | - Michael T. Woodside
- Centre for Prions and Protein Folding DiseasesUniversity of AlbertaEdmontonAlbertaCanada
- Department of PhysicsUniversity of AlbertaEdmontonAlbertaCanada
- Li Ka Shing Institute of VirologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Valerie L. Sim
- Centre for Prions and Protein Folding DiseasesUniversity of AlbertaEdmontonAlbertaCanada
- Division of Neurology, Department of MedicineUniversity of AlbertaEdmontonAlbertaCanada
- Neuroscience and Mental Health InstituteUniversity of AlbertaEdmontonAlbertaCanada
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26
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Ayuso-Fernández I, Molpeceres G, Camarero S, Ruiz-Dueñas FJ, Martínez AT. Ancestral sequence reconstruction as a tool to study the evolution of wood decaying fungi. FRONTIERS IN FUNGAL BIOLOGY 2022; 3:1003489. [PMID: 37746217 PMCID: PMC10512382 DOI: 10.3389/ffunb.2022.1003489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/22/2022] [Indexed: 09/26/2023]
Abstract
The study of evolution is limited by the techniques available to do so. Aside from the use of the fossil record, molecular phylogenetics can provide a detailed characterization of evolutionary histories using genes, genomes and proteins. However, these tools provide scarce biochemical information of the organisms and systems of interest and are therefore very limited when they come to explain protein evolution. In the past decade, this limitation has been overcome by the development of ancestral sequence reconstruction (ASR) methods. ASR allows the subsequent resurrection in the laboratory of inferred proteins from now extinct organisms, becoming an outstanding tool to study enzyme evolution. Here we review the recent advances in ASR methods and their application to study fungal evolution, with special focus on wood-decay fungi as essential organisms in the global carbon cycling.
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Affiliation(s)
- Iván Ayuso-Fernández
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Gonzalo Molpeceres
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB), CSIC, Madrid, Spain
| | - Susana Camarero
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB), CSIC, Madrid, Spain
| | | | - Angel T. Martínez
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB), CSIC, Madrid, Spain
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27
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Hobbs HT, Shah NH, Shoemaker SR, Amacher JF, Marqusee S, Kuriyan J. Saturation mutagenesis of a predicted ancestral Syk-family kinase. Protein Sci 2022; 31:e4411. [PMID: 36173161 PMCID: PMC9601881 DOI: 10.1002/pro.4411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/27/2022] [Accepted: 07/25/2022] [Indexed: 11/08/2022]
Abstract
Many tyrosine kinases cannot be expressed readily in Escherichia coli, limiting facile production of these proteins for biochemical experiments. We used ancestral sequence reconstruction to generate a spleen tyrosine kinase (Syk) variant that can be expressed in bacteria and purified in soluble form, unlike the human members of this family (Syk and zeta-chain-associated protein kinase of 70 kDa [ZAP-70]). The catalytic activity, substrate specificity, and regulation by phosphorylation of this Syk variant are similar to the corresponding properties of human Syk and ZAP-70. Taking advantage of the ability to express this novel Syk-family kinase in bacteria, we developed a two-hybrid assay that couples the growth of E. coli in the presence of an antibiotic to successful phosphorylation of a bait peptide by the kinase. Using this assay, we screened a site-saturation mutagenesis library of the kinase domain of this reconstructed Syk-family kinase. Sites of loss-of-function mutations identified in the screen correlate well with residues established previously as critical to function and/or structure in protein kinases. We also identified activating mutations in the regulatory hydrophobic spine and activation loop, which are within key motifs involved in kinase regulation. Strikingly, one mutation in an ancestral Syk-family variant increases the soluble expression of the protein by 75-fold. Thus, through ancestral sequence reconstruction followed by deep mutational scanning, we have generated Syk-family kinase variants that can be expressed in bacteria with very high yield.
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Affiliation(s)
- Helen T. Hobbs
- Department of ChemistryUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Biomedical EngineeringUniversity of CaliforniaIrvineCaliforniaUSA
| | - Neel H. Shah
- Department of ChemistryColumbia UniversityNew YorkNew YorkUSA
| | - Sophie R. Shoemaker
- Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Jeanine F. Amacher
- Department of ChemistryWestern Washington UniversityBellinghamWashingtonUSA
| | - Susan Marqusee
- Department of ChemistryUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- California Institute for Quantitative BiosciencesUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - John Kuriyan
- Department of ChemistryUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Molecular and Cell BiologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- California Institute for Quantitative BiosciencesUniversity of CaliforniaBerkeleyCaliforniaUSA
- Howard Hughes Medical InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
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28
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Shi W, Singha M, Pu L, Srivastava G, Ramanujam J, Brylinski M. GraphSite: Ligand Binding Site Classification with Deep Graph Learning. Biomolecules 2022; 12:1053. [PMID: 36008947 PMCID: PMC9405584 DOI: 10.3390/biom12081053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/10/2022] Open
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Jagannathan Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
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29
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Shekhar M, Smith Z, Seeliger MA, Tiwary P. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases. Angew Chem Int Ed Engl 2022; 61:e202200983. [PMID: 35486370 DOI: 10.1002/anie.202200983] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Indexed: 12/14/2022]
Abstract
Understanding how mutations render a drug ineffective is a problem of immense relevance. Often the mechanism through which mutations cause drug resistance can be explained purely through thermodynamics. However, the more perplexing situation is when two proteins have the same drug binding affinities but different residence times. In this work, we demonstrate how all-atom molecular dynamics simulations using recent developments grounded in statistical mechanics can provide a detailed mechanistic rationale for such variances. We discover dissociation mechanisms for the anti-cancer drug Imatinib (Gleevec) against wild-type and the N368S mutant of Abl kinase. We show how this point mutation triggers far-reaching changes in the protein's flexibility and leads to a different, much faster, drug dissociation pathway. We believe that this work marks an efficient and scalable approach to obtain mechanistic insight into resistance mutations in biomolecular receptors that are hard to explain using a structural perspective.
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Affiliation(s)
- Mrinal Shekhar
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zachary Smith
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
| | - Markus A Seeliger
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
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30
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Otsuka FAM, Bjelic S. Evaluation of residue variability in a conformation-specific context and during evolutionary sequence reconstruction narrows drug resistance selection in Abl1 tyrosine kinase. Protein Sci 2022; 31:e4354. [PMID: 35762721 PMCID: PMC9202545 DOI: 10.1002/pro.4354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/31/2022] [Accepted: 05/10/2022] [Indexed: 11/12/2022]
Abstract
Diseases with readily available therapies may eventually prevail against the specific treatment by the acquisition of resistance. The constitutively active Abl1 tyrosine kinase known to cause chronic myeloid leukemia is an example, where patients may experience relapse after small inhibitor drug treatment. Mutations in the Abl1 tyrosine kinase domain (Abl1-KD) are a critical source of resistance and their emergence depends on the conformational states that have been observed experimentally: the inactive state, the active state, and the intermediate inactive state that resembles Src kinase. Understanding how resistant positions and amino acid identities are determined by selection pressure during drug treatment is necessary to improve future drug development or treatment decisions. We carry out in silico site-saturation mutagenesis over the Abl1-KD structure in a conformational context to evaluate the in situ and conformational stability energy upon mutation. Out of the 11 studied resistant positions, we determined that 7 of the resistant mutations favored the active conformation of Abl1-KD with respect to the inactive state. When, instead, the sequence optimization was modeled simultaneously at resistant positions, we recovered five known resistant mutations in the active conformation. These results suggested that the Abl1 resistance mechanism targeted substitutions that favored the active conformation. Further sequence variability, explored by ancestral reconstruction in Abl1-KD, showed that neutral genetic drift, with respect to amino acid variability, was specifically diminished in the resistant positions. Since resistant mutations are susceptible to chance with a certain probability of fixation, combining methodologies outlined here may narrow and limit the available sequence space for resistance to emerge, resulting in more robust therapeutic treatments over time.
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MESH Headings
- Amino Acids
- Drug Resistance, Neoplasm/genetics
- Humans
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myeloid, Chronic-Phase/drug therapy
- Leukemia, Myeloid, Chronic-Phase/genetics
- Protein Kinase Inhibitors/pharmacology
- Proto-Oncogene Proteins c-abl/genetics
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Affiliation(s)
- Felipe A. M. Otsuka
- Department of Chemistry and Biomedical SciencesLinnaeus UniversityKalmarSweden
- Departamento de Bioquímica, Instituto de QuímicaUniversidade de São PauloSão PauloBrazil
| | - Sinisa Bjelic
- Department of Chemistry and Biomedical SciencesLinnaeus UniversityKalmarSweden
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31
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Nocedal I, Laub MT. Ancestral reconstruction of duplicated signaling proteins reveals the evolution of signaling specificity. eLife 2022; 11:e77346. [PMID: 35686729 PMCID: PMC9208753 DOI: 10.7554/elife.77346] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/27/2022] [Indexed: 01/30/2023] Open
Abstract
Gene duplication is crucial to generating novel signaling pathways during evolution. However, it remains unclear how the redundant proteins produced by gene duplication ultimately acquire new interaction specificities to establish insulated paralogous signaling pathways. Here, we used ancestral sequence reconstruction to resurrect and characterize a bacterial two-component signaling system that duplicated in α-proteobacteria. We determined the interaction specificities of the signaling proteins that existed before and immediately after this duplication event and then identified key mutations responsible for establishing specificity in the two systems. Just three mutations, in only two of the four interacting proteins, were sufficient to establish specificity of the extant systems. Some of these mutations weakened interactions between paralogous systems to limit crosstalk. However, others strengthened interactions within a system, indicating that the ancestral interaction, although functional, had the potential to be strengthened. Our work suggests that protein-protein interactions with such latent potential may be highly amenable to duplication and divergence.
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Affiliation(s)
- Isabel Nocedal
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Michael T Laub
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
- Howard Hughes Medical Institute, Massachusetts Institute of TechnologyCambridgeUnited States
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32
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A topological data analytic approach for discovering biophysical signatures in protein dynamics. PLoS Comput Biol 2022; 18:e1010045. [PMID: 35500014 PMCID: PMC9098046 DOI: 10.1371/journal.pcbi.1010045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 05/12/2022] [Accepted: 03/22/2022] [Indexed: 12/02/2022] Open
Abstract
Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.
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33
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Shekhar M, Smith Z, Seeliger M, Tiwary P. Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset Of Resistance Mutations in Kinases. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202200983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Mrinal Shekhar
- Broad Institute Center for Development of Therapeutics UNITED STATES
| | - Zachary Smith
- University of Maryland at College Park Institute for Physical Science and Technology UNITED STATES
| | - Markus Seeliger
- Stony Brook University Department of Pharmacological Sciences UNITED STATES
| | - Pratyush Tiwary
- university of maryland chemistry and biochemistry university of maryland 20740 college park UNITED STATES
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34
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Cruz MA, Frederick TE, Mallimadugula UL, Singh S, Vithani N, Zimmerman MI, Porter JR, Moeder KE, Amarasinghe GK, Bowman GR. A cryptic pocket in Ebola VP35 allosterically controls RNA binding. Nat Commun 2022; 13:2269. [PMID: 35477718 PMCID: PMC9046395 DOI: 10.1038/s41467-022-29927-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/07/2022] [Indexed: 11/08/2022] Open
Abstract
Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.
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Affiliation(s)
- Matthew A Cruz
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Thomas E Frederick
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Upasana L Mallimadugula
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Sukrit Singh
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Neha Vithani
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Justin R Porter
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Katelyn E Moeder
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gaya K Amarasinghe
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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35
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Omar H, Hein A, Cole CA, Valafar H. Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT. Front Mol Biosci 2022; 9:806584. [PMID: 35187082 PMCID: PMC8856112 DOI: 10.3389/fmolb.2022.806584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of internal dynamics. In this simulation, DHFR was segmented into 7 regions where 4 of the fragments were fixed with respect to each other, two regions underwent rigid-body dynamics, and one region experienced uncorrelated and melting event. The two dynamical and rigid-body segments experienced an average orientational modification of 7° and 12° respectively. Observable RDC data for backbone C′-N, N-HN, and C′-HN were generated from 102 uniformly sampled frames that described the molecular trajectory. The structure calculation of DHFR with REDCRAFT by using traditional Ramachandran restraint produced a structure with 29 Å of structural difference measured over the backbone atoms (bb-rmsd) over the entire length of the protein and an average bb-rmsd of more than 4.7 Å over each of the dynamical fragments. The same exercise repeated with context-specific dihedral restraints generated by PDBMine produced a structure with bb-rmsd of 21 Å over the entire length of the protein but with bb-rmsd of less than 3 Å over each of the fragments. Finally, utilization of the Dynamic Profile generated by REDCRAFT allowed for the identification of different dynamical regions of the protein and the recovery of individual fragments with bb-rmsd of less than 1 Å. Following the recovery of the fragments, our assembly procedure of domains (larger segments consisting of multiple fragments with a common dynamical profile) correctly assembled the four fragments that are rigid with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure could be defined. In conclusion, our approach was successful in identifying the dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible.
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36
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Abstract
The reconstruction of genetic material of ancestral organisms constitutes a powerful application of evolutionary biology. A fundamental step in this inference is the ancestral sequence reconstruction (ASR), which can be performed with diverse methodologies implemented in computer frameworks. However, most of these methodologies ignore evolutionary properties frequently observed in microbes, such as genetic recombination and complex selection processes, that can bias the traditional ASR. From a practical perspective, here I review methodologies for the reconstruction of ancestral DNA and protein sequences, with particular focus on microbes, and including biases, recommendations, and software implementations. I conclude that microbial ASR is a complex analysis that should be carefully performed and that there is a need for methods to infer more realistic ancestral microbial sequences.
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Affiliation(s)
- Miguel Arenas
- Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain.
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.
- Galicia Sur Health Research Institute (IIS Galicia Sur), Vigo, Spain.
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37
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Hwang T, Parker SS, Hill SM, Ilunga MW, Grant RA, Mouneimne G, Keating AE. A distributed residue network permits conformational binding specificity in a conserved family of actin remodelers. eLife 2021; 10:e70601. [PMID: 34854809 PMCID: PMC8639148 DOI: 10.7554/elife.70601] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Abstract
Metazoan proteomes contain many paralogous proteins that have evolved distinct functions. The Ena/VASP family of actin regulators consists of three members that share an EVH1 interaction domain with a 100 % conserved binding site. A proteome-wide screen revealed photoreceptor cilium actin regulator (PCARE) as a high-affinity ligand for ENAH EVH1. Here, we report the surprising observation that PCARE is ~100-fold specific for ENAH over paralogs VASP and EVL and can selectively bind ENAH and inhibit ENAH-dependent adhesion in cells. Specificity arises from a mechanism whereby PCARE stabilizes a conformation of the ENAH EVH1 domain that is inaccessible to family members VASP and EVL. Structure-based modeling rapidly identified seven residues distributed throughout EVL that are sufficient to differentiate binding by ENAH vs. EVL. By exploiting the ENAH-specific conformation, we rationally designed the tightest and most selective ENAH binder to date. Our work uncovers a conformational mechanism of interaction specificity that distinguishes highly similar paralogs and establishes tools for dissecting specific Ena/VASP functions in processes including cancer cell invasion.
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Affiliation(s)
- Theresa Hwang
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Sara S Parker
- Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of ArizonaTucsonUnited States
| | - Samantha M Hill
- Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of ArizonaTucsonUnited States
| | - Meucci W Ilunga
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Robert A Grant
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ghassan Mouneimne
- Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of ArizonaTucsonUnited States
| | - Amy E Keating
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biological Engineering and Koch Institue for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
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38
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Thomas T, Roux B. TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:203. [PMID: 36524055 PMCID: PMC9749240 DOI: 10.1140/epjb/s10051-021-00207-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/14/2021] [Indexed: 05/28/2023]
Abstract
Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.
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39
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Huang YMM. Multiscale computational study of ligand binding pathways: Case of p38 MAP kinase and its inhibitors. Biophys J 2021; 120:3881-3892. [PMID: 34453922 PMCID: PMC8511166 DOI: 10.1016/j.bpj.2021.08.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 06/07/2021] [Accepted: 08/20/2021] [Indexed: 01/09/2023] Open
Abstract
Protein kinases are one of the most important drug targets in the past 10 years. Understanding the inhibitor association processes will profoundly impact new binder designs with preferred binding kinetics. However, after more than a decade of effort, a complete atomistic-level study of kinase inhibitor binding pathways is still lacking. As all kinases share a similar scaffold, we used p38 kinase as a model system to investigate the conformational dynamics and free energy transition of inhibitor binding toward kinases. Two major kinase conformations, Asp-Phe-Gly (DFG)-in and DFG-out, and three types of inhibitors, type I, II, and III, were thoroughly investigated in this work. We performed Brownian dynamics simulations and up to 340 μs Gaussian-accelerated molecular dynamics simulations to capture the inhibitor binding paths and a series of conformational transitions of the p38 kinase from its apo to inhibitor-bound form. Eighteen successful binding trajectories, including all types of inhibitors, are reported herein. Our simulations suggest a mechanism of inhibitor recruitment, a faster ligand association step to a pre-existing DFG-in/DFG-out p38 protein, followed by a slower molecular rearrangement step to adjust the protein-ligand conformation followed by a shift in the energy landscape to reach the final bound state. The ligand association processes also reflect the energetic favor of type I and type II/III inhibitor binding through ATP and allosteric channels, respectively. These different binding routes are directly responsible for the fast (type I binders) and slow (type II/III binders) kinetics of different types of p38 inhibitors. Our findings also echo the recent study of p38 inhibitor dissociation, implying that ligand unbinding could undergo a reverse path of binding, and both processes share similar metastates. This study deepens the understanding of molecular and energetic features of kinase inhibitor-binding processes and will inspire future drug development from a kinetic point of view.
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Affiliation(s)
- Yu-Ming M Huang
- Department of Physics and Astronomy, Wayne State University, Detroit, Michigan.
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40
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Epistasis shapes the fitness landscape of an allosteric specificity switch. Nat Commun 2021; 12:5562. [PMID: 34548494 PMCID: PMC8455584 DOI: 10.1038/s41467-021-25826-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 09/03/2021] [Indexed: 11/08/2022] Open
Abstract
Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the designed mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. The structure of the designed mutant shows that a striking change in inducer orientation still retains allosteric function. Comparing biophysical and functional properties suggests a nonlinear relationship between inducer binding affinity and allostery. Our results highlight the functional and evolutionary complexity of allosteric proteins. Epistasis plays an important role in the evolution of novel protein functions because it determines the mutational path a protein takes. Here, the authors combine functional, structural and biophysical analyses to characterize epistasis in a computationally redesigned ligand-inducible allosteric transcription factor and found that epistasis creates distinct biophysical and biological functional landscapes.
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41
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Morrison AJ, Wonderlick DR, Harms MJ. Ensemble epistasis: thermodynamic origins of nonadditivity between mutations. Genetics 2021; 219:iyab105. [PMID: 34849909 PMCID: PMC8633102 DOI: 10.1093/genetics/iyab105] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/19/2021] [Indexed: 01/02/2023] Open
Abstract
Epistasis-when mutations combine nonadditively-is a profoundly important aspect of biology. It is often difficult to understand its mechanistic origins. Here, we show that epistasis can arise from the thermodynamic ensemble, or the set of interchanging conformations a protein adopts. Ensemble epistasis occurs because mutations can have different effects on different conformations of the same protein, leading to nonadditive effects on its average, observable properties. Using a simple analytical model, we found that ensemble epistasis arises when two conditions are met: (1) a protein populates at least three conformations and (2) mutations have differential effects on at least two conformations. To explore the relative magnitude of ensemble epistasis, we performed a virtual deep-mutational scan of the allosteric Ca2+ signaling protein S100A4. We found that 47% of mutation pairs exhibited ensemble epistasis with a magnitude on the order of thermal fluctuations. We observed many forms of epistasis: magnitude, sign, and reciprocal sign epistasis. The same mutation pair could even exhibit different forms of epistasis under different environmental conditions. The ubiquity of thermodynamic ensembles in biology and the pervasiveness of ensemble epistasis in our dataset suggests that it may be a common mechanism of epistasis in proteins and other macromolecules.
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Affiliation(s)
- Anneliese J Morrison
- Institute of Molecular Biology, University of Oregon, Eugene, OR 97403, USA
- Department of Chemistry and Biochemistry, University of Oregon, Eugene OR 97403, USA
| | - Daria R Wonderlick
- Institute of Molecular Biology, University of Oregon, Eugene, OR 97403, USA
- Department of Chemistry and Biochemistry, University of Oregon, Eugene OR 97403, USA
| | - Michael J Harms
- Institute of Molecular Biology, University of Oregon, Eugene, OR 97403, USA
- Department of Chemistry and Biochemistry, University of Oregon, Eugene OR 97403, USA
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42
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A single historical substitution drives an increase in acetylcholine receptor complexity. Proc Natl Acad Sci U S A 2021; 118:2018731118. [PMID: 33579823 DOI: 10.1073/pnas.2018731118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Human adult muscle-type acetylcholine receptors are heteropentameric ion channels formed from four different, but evolutionarily related, subunits. These subunits assemble with a precise stoichiometry and arrangement such that two chemically distinct agonist-binding sites are formed between specific subunit pairs. How this subunit complexity evolved and became entrenched is unclear. Here we show that a single historical amino acid substitution is able to constrain the subunit stoichiometry of functional acetylcholine receptors. Using a combination of ancestral sequence reconstruction, single-channel electrophysiology, and concatenated subunits, we reveal that an ancestral β-subunit can not only replace the extant β-subunit but can also supplant the neighboring δ-subunit. By forward evolving the ancestral β-subunit with a single amino acid substitution, we restore the requirement for a δ-subunit for functional channels. These findings reveal that a single historical substitution necessitates an increase in acetylcholine receptor complexity and, more generally, that simple stepwise mutations can drive subunit entrenchment in this model heteromeric protein.
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43
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Verkhivker GM. Making the invisible visible: Toward structural characterization of allosteric states, interaction networks, and allosteric regulatory mechanisms in protein kinases. Curr Opin Struct Biol 2021; 71:71-78. [PMID: 34237520 DOI: 10.1016/j.sbi.2021.06.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/27/2021] [Accepted: 06/03/2021] [Indexed: 02/07/2023]
Abstract
Despite the established view of protein kinases as dynamic and versatile allosteric regulatory machines, our knowledge of allosteric functional states, allosteric interaction networks, and the intrinsic folding energy landscapes is surprisingly limited. We discuss the latest developments in structural characterization of allosteric molecular events underlying protein kinase dynamics and functions using structural, biophysical, and computational biology approaches. The recent studies highlighted progress in making the invisible aspects of protein kinase 'life' visible, including the determination of hidden allosteric states and mapping of allosteric energy landscapes, discovery of new mechanisms underlying ligand-induced modulation of allosteric activity, evolutionary adaptation of kinase allostery, and characterization of allosteric interaction networks as the intrinsic driver of kinase adaptability and signal transmission in the regulatory assemblies.
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Affiliation(s)
- Gennady M Verkhivker
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, 9401 Jeronimo Road, Irvine, CA, 92618, USA.
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44
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Pack LR, Daigh LH, Chung M, Meyer T. Clinical CDK4/6 inhibitors induce selective and immediate dissociation of p21 from cyclin D-CDK4 to inhibit CDK2. Nat Commun 2021; 12:3356. [PMID: 34099663 PMCID: PMC8184839 DOI: 10.1038/s41467-021-23612-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 05/06/2021] [Indexed: 12/11/2022] Open
Abstract
Since their discovery as drivers of proliferation, cyclin-dependent kinases (CDKs) have been considered therapeutic targets. Small molecule inhibitors of CDK4/6 are used and tested in clinical trials to treat multiple cancer types. Despite their clinical importance, little is known about how CDK4/6 inhibitors affect the stability of CDK4/6 complexes, which bind cyclins and inhibitory proteins such as p21. We develop an assay to monitor CDK complex stability inside the nucleus. Unexpectedly, treatment with CDK4/6 inhibitors-palbociclib, ribociclib, or abemaciclib-immediately dissociates p21 selectively from CDK4 but not CDK6 complexes. This effect mediates indirect inhibition of CDK2 activity by p21 but not p27 redistribution. Our work shows that CDK4/6 inhibitors have two roles: non-catalytic inhibition of CDK2 via p21 displacement from CDK4 complexes, and catalytic inhibition of CDK4/6 independent of p21. By broadening the non-catalytic displacement to p27 and CDK6 containing complexes, next-generation CDK4/6 inhibitors may have improved efficacy and overcome resistance mechanisms.
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Affiliation(s)
- Lindsey R Pack
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Leighton H Daigh
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Mingyu Chung
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Tobias Meyer
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA.
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45
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Wang L, Zhang J, Han M, Zhang L, Chen C, Huang A, Xie R, Wang G, Zhu J, Wang Y, Liu X, Zhuang W, Li Y, Wang J. A Genetically Encoded Two‐Dimensional Infrared Probe for Enzyme Active‐Site Dynamics. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202016880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Li Wang
- School of Life Sciences University of Chinese Academy of Sciences Yuquan Road, Shijingshan District Beijing 100049 China
- Institute of Biophysics Chinese Academy of Sciences Datun Road, Chaoyang District Beijing 100101 China
| | - Jia Zhang
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China
- School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China
| | - Ming‐Jie Han
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences West 7th Avenue, Tianjin Airport Economic Area Tianjin 300308 China
- Shenzhen Institute of Transfusion Medicine Shenzhen Blood Center Futian District Shenzhen 518052 China
| | - Lu Zhang
- State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou 350002 China
| | - Chao Chen
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences West 7th Avenue, Tianjin Airport Economic Area Tianjin 300308 China
| | - Aiping Huang
- Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences West 7th Avenue, Tianjin Airport Economic Area Tianjin 300308 China
| | - Ruipei Xie
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China
- School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China
| | - Guosheng Wang
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China
| | - Jiangrui Zhu
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China
- School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China
| | - Yuchuan Wang
- Shenzhen Institute of Transfusion Medicine Shenzhen Blood Center Futian District Shenzhen 518052 China
| | - Xiaohong Liu
- Institute of Biophysics Chinese Academy of Sciences Datun Road, Chaoyang District Beijing 100101 China
| | - Wei Zhuang
- State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou 350002 China
- Institute of urban environment Chinese Academy of Sciences Xiamen Fujian 361021 China
| | - Yunliang Li
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China
- School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China
- Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China
| | - Jiangyun Wang
- Institute of Biophysics Chinese Academy of Sciences Datun Road, Chaoyang District Beijing 100101 China
- Shenzhen Institute of Transfusion Medicine Shenzhen Blood Center Futian District Shenzhen 518052 China
- School of Life Sciences University of Chinese Academy of Sciences Yuquan Road, Shijingshan District Beijing 100049 China
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46
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Carrijo de Oliveira L, Figueiredo Costa MA, Gonçalves Pedersolli N, Heleno Batista FA, Migliorini Figueira AC, Salgado Ferreira R, Alves Pinto Nagem R, Alves Nahum L, Bleicher L. Reenacting the Birth of a Function: Functional Divergence of HIUases and Transthyretins as Inferred by Evolutionary and Biophysical Studies. J Mol Evol 2021; 89:370-383. [PMID: 33956179 DOI: 10.1007/s00239-021-10010-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
Transthyretin was discovered in the 1940s, named after its ability to bind thyroid hormones and retinol. In the genomic era, transthyretins were found to be part of a larger family with homologs of no obvious function, then called transthyretin-related proteins. Thus, it was proposed that the transthyretin gene could be the result of gene duplication of an ancestral of this newly identified homolog, later found out to be an enzyme involved in uric acid degradation, then named HIUase (5-hydroxy-isourate hydrolase). Here, we sought to re-enact the evolutionary history of this protein family by reconstructing, from a phylogeny inferred from 123 vertebrate sequences, three ancestors corresponding to key moments in their evolution-before duplication; the common transthyretin ancestor after gene duplication and the common ancestor of Eutheria transthyretins. Experimental and computational characterization showed the reconstructed ancestor before duplication was unable to bind thyroxine and likely presented the modern HIUase reaction mechanism, while the substitutions after duplication prevented that activity and were enough to provide stable thyroxine binding, as confirmed by calorimetry and x-ray diffraction. The Eutheria transthyretin ancestor was less prone to characterization, but limited data suggested thyroxine binding as expected. Sequence/structure analysis suggests an early ability to bind the Retinol Binding Protein. We solved the X-ray structures from the two first ancestors, the first at 1.46 resolution, the second at 1.55 resolution with well-defined electron density for thyroxine, providing a useful tool for the understanding of structural adaptation from enzyme to hormone distributor.
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Affiliation(s)
| | | | | | | | | | | | | | - Laila Alves Nahum
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Brazil
| | - Lucas Bleicher
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
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47
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Wang L, Zhang J, Han MJ, Zhang L, Chen C, Huang A, Xie R, Wang G, Zhu J, Wang Y, Liu X, Zhuang W, Li Y, Wang J. A Genetically Encoded Two-Dimensional Infrared Probe for Enzyme Active-Site Dynamics. Angew Chem Int Ed Engl 2021; 60:11143-11147. [PMID: 33644946 DOI: 10.1002/anie.202016880] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/10/2021] [Indexed: 11/08/2022]
Abstract
While two-dimensional infrared (2D-IR) spectroscopy is uniquely suitable for monitoring femtosecond (fs) to picosecond (ps) water dynamics around static protein structures, its utility for probing enzyme active-site dynamics is limited due to the lack of site-specific 2D-IR probes. We demonstrate the genetic incorporation of a novel 2D-IR probe, m-azido-L-tyrosine (N3Y) in the active-site of DddK, an iron-dependent enzyme that catalyzes the conversion of dimethylsulfoniopropionate to dimethylsulphide. Our results show that both the oxidation of active-site iron to FeIII , and the addition of denaturation reagents, result in significant decrease in enzyme activity and active-site water motion confinement. As tyrosine residues play important roles, including as general acids and bases, and electron transfer agents in many key enzymes, the genetically encoded 2D-IR probe N3Y should be broadly applicable to investigate how the enzyme active-site motions at the fs-ps time scale direct reaction pathways to facilitating specific chemical reactions.
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Affiliation(s)
- Li Wang
- School of Life Sciences, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing, 100049, China.,Institute of Biophysics, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing, 100101, China
| | - Jia Zhang
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.,School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ming-Jie Han
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, West 7th Avenue, Tianjin Airport Economic Area, Tianjin, 300308, China.,Shenzhen Institute of Transfusion Medicine, Shenzhen Blood Center, Futian District, Shenzhen, 518052, China
| | - Lu Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, China
| | - Chao Chen
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, West 7th Avenue, Tianjin Airport Economic Area, Tianjin, 300308, China
| | - Aiping Huang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, West 7th Avenue, Tianjin Airport Economic Area, Tianjin, 300308, China
| | - Ruipei Xie
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.,School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guosheng Wang
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jiangrui Zhu
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.,School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuchuan Wang
- Shenzhen Institute of Transfusion Medicine, Shenzhen Blood Center, Futian District, Shenzhen, 518052, China
| | - Xiaohong Liu
- Institute of Biophysics, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing, 100101, China
| | - Wei Zhuang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, China.,Institute of urban environment, Chinese Academy of Sciences, Xiamen, Fujian, 361021, China
| | - Yunliang Li
- Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.,School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, China
| | - Jiangyun Wang
- Institute of Biophysics, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing, 100101, China.,Shenzhen Institute of Transfusion Medicine, Shenzhen Blood Center, Futian District, Shenzhen, 518052, China.,School of Life Sciences, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing, 100049, China
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48
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Hendrikse NM, Sandegren A, Andersson T, Blomqvist J, Makower Å, Possner D, Su C, Thalén N, Tjernberg A, Westermark U, Rockberg J, Svensson Gelius S, Syrén PO, Nordling E. Ancestral lysosomal enzymes with increased activity harbor therapeutic potential for treatment of Hunter syndrome. iScience 2021; 24:102154. [PMID: 33665572 PMCID: PMC7907806 DOI: 10.1016/j.isci.2021.102154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/11/2020] [Accepted: 02/02/2021] [Indexed: 11/18/2022] Open
Abstract
We show the successful application of ancestral sequence reconstruction to enhance the activity of iduronate-2-sulfatase (IDS), thereby increasing its therapeutic potential for the treatment of Hunter syndrome—a lysosomal storage disease caused by impaired function of IDS. Current treatment, enzyme replacement therapy with recombinant human IDS, does not alleviate all symptoms, and an unmet medical need remains. We reconstructed putative ancestral sequences of mammalian IDS and compared them with extant IDS. Some ancestral variants displayed up to 2-fold higher activity than human IDS in in vitro assays and cleared more substrate in ex vivo experiments in patient fibroblasts. This could potentially allow for lower dosage or enhanced therapeutic effect in enzyme replacement therapy, thereby improving treatment outcomes and cost efficiency, as well as reducing treatment burden. In summary, we showed that ancestral sequence reconstruction can be applied to lysosomal enzymes that function in concert with modern enzymes and receptors in cells. Reconstruction of ancestral lysosomal enzymes that function in complex cellular context Ancestral iduronate-2-sulfatases with increased activity compared with the human enzyme Increased clearance of substrate in patient fibroblasts indicates therapeutic potential
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Affiliation(s)
- Natalie M. Hendrikse
- Swedish Orphan Biovitrum AB, Stockholm 112 76, Sweden
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna 171 21, Sweden
- Department of Fibre and Polymer Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm 100 44, Sweden
| | | | | | | | - Åsa Makower
- Swedish Orphan Biovitrum AB, Stockholm 112 76, Sweden
| | | | - Chao Su
- Swedish Orphan Biovitrum AB, Stockholm 112 76, Sweden
| | - Niklas Thalén
- Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm 10691, Sweden
| | | | | | - Johan Rockberg
- Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm 10691, Sweden
| | | | - Per-Olof Syrén
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna 171 21, Sweden
- Department of Fibre and Polymer Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm 100 44, Sweden
- Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm 10691, Sweden
- Corresponding author
| | - Erik Nordling
- Swedish Orphan Biovitrum AB, Stockholm 112 76, Sweden
- Corresponding author
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49
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Asangani I, Blair IA, Van Duyne G, Hilser VJ, Moiseenkova-Bell V, Plymate S, Sprenger C, Wand AJ, Penning TM. Using biochemistry and biophysics to extinguish androgen receptor signaling in prostate cancer. J Biol Chem 2021; 296:100240. [PMID: 33384381 PMCID: PMC7949100 DOI: 10.1074/jbc.rev120.012411] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/19/2020] [Accepted: 12/31/2020] [Indexed: 12/12/2022] Open
Abstract
Castration resistant prostate cancer (CRPC) continues to be androgen receptor (AR) driven. Inhibition of AR signaling in CRPC could be advanced using state-of-the-art biophysical and biochemical techniques. Structural characterization of AR and its complexes by cryo-electron microscopy would advance the development of N-terminal domain (NTD) and ligand-binding domain (LBD) antagonists. The structural basis of AR function is unlikely to be determined by any single structure due to the intrinsic disorder of its NTD, which not only interacts with coregulators but likely accounts for the constitutive activity of AR-splice variants (SV), which lack the LBD and emerge in CRPC. Using different AR constructs lacking the LBD, their effects on protein folding, DNA binding, and transcriptional activity could reveal how interdomain coupling explains the activity of AR-SVs. The AR also interacts with coregulators that promote chromatin looping. Elucidating the mechanisms involved can identify vulnerabilities to treat CRPC, which do not involve targeting the AR. Phosphorylation of the AR coactivator MED-1 by CDK7 is one mechanism that can be blocked by the use of CDK7 inhibitors. CRPC gains resistance to AR signaling inhibitors (ARSI). Drug resistance may involve AR-SVs, but their role requires their reliable quantification by SILAC-mass spectrometry during disease progression. ARSI drug resistance also occurs by intratumoral androgen biosynthesis catalyzed by AKR1C3 (type 5 17β-hydroxysteroid dehydrogenase), which is unique in that its acts as a coactivator of AR. Novel bifunctional inhibitors that competitively inhibit AKR1C3 and block its coactivator function could be developed using reverse-micelle NMR and fragment-based drug discovery.
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Affiliation(s)
- Irfan Asangani
- Department Cancer Biology, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ian A Blair
- Department Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gregory Van Duyne
- Department of Biochemistry & Biophysics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vincent J Hilser
- Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vera Moiseenkova-Bell
- Department Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stephen Plymate
- Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Washington, and GRECC, Seattle, Washington, USA
| | - Cynthia Sprenger
- Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Washington, and GRECC, Seattle, Washington, USA
| | - A Joshua Wand
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, Texas, USA
| | - Trevor M Penning
- Department Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Intermolecular Interactions in Crystal Structures of Imatinib-Containing Compounds. Int J Mol Sci 2020; 21:ijms21238970. [PMID: 33255944 PMCID: PMC7731260 DOI: 10.3390/ijms21238970] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
Imatinib, one of the most used therapeutic agents to treat leukemia, is an inhibitor that specifically blocks the activity of tyrosine kinases. The molecule of imatinib is flexible and contains several functional groups able to take part in H-bonding and hydrophobic interactions. Analysis of molecular conformations for this drug was carried out using density functional theory calculations of rotation potentials along single bonds and by analyzing crystal structures of imatinib-containing compounds taken from the Cambridge Structural Database and the Protein Data Bank. Rotation along the N-C bond in the region of the amide group was found to be the reason for two relatively stable molecular conformations, an extended and a folded one. The role of various types of intermolecular interactions in stabilization of the particular molecular conformation was studied in terms of (i) the likelihood of H-bond formation, and (ii) their contribution to the Voronoi molecular surface. It is shown that experimentally observed hydrogen bonds are in accord with the likelihood of their formation. The number of H-bonds in ligand-receptor complexes surpasses that in imatinib salts due to the large number of donors and acceptors of H-bonding within the binding pocket of tyrosine kinases. Contribution of hydrophilic intermolecular interactions to the Voronoi molecular surface is similar for both conformations, while π...π stacking is more typical for the folded conformation of imatinib.
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