1
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Li J, Wang L, Zhu Z, Song C. Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction. J Chem Inf Model 2024; 64:301-315. [PMID: 38117138 PMCID: PMC10777399 DOI: 10.1021/acs.jcim.3c01381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Abstract
The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.
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Affiliation(s)
- Jiaxuan Li
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Lei Wang
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Zefeng Zhu
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chen Song
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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2
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Schafer JW, Porter LL. Evolutionary selection of proteins with two folds. Nat Commun 2023; 14:5478. [PMID: 37673981 PMCID: PMC10482954 DOI: 10.1038/s41467-023-41237-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/24/2023] [Indexed: 09/08/2023] Open
Abstract
Although most globular proteins fold into a single stable structure, an increasing number have been shown to remodel their secondary and tertiary structures in response to cellular stimuli. State-of-the-art algorithms predict that these fold-switching proteins adopt only one stable structure, missing their functionally critical alternative folds. Why these algorithms predict a single fold is unclear, but all of them infer protein structure from coevolved amino acid pairs. Here, we hypothesize that coevolutionary signatures are being missed. Suspecting that single-fold variants could be masking these signatures, we developed an approach, called Alternative Contact Enhancement (ACE), to search both highly diverse protein superfamilies-composed of single-fold and fold-switching variants-and protein subfamilies with more fold-switching variants. ACE successfully revealed coevolution of amino acid pairs uniquely corresponding to both conformations of 56/56 fold-switching proteins from distinct families. Then, we used ACE-derived contacts to (1) predict two experimentally consistent conformations of a candidate protein with unsolved structure and (2) develop a blind prediction pipeline for fold-switching proteins. The discovery of widespread dual-fold coevolution indicates that fold-switching sequences have been preserved by natural selection, implying that their functionalities provide evolutionary advantage and paving the way for predictions of diverse protein structures from single sequences.
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Affiliation(s)
- Joseph W Schafer
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Lauren L Porter
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA.
- National Heart, Lung, and Blood Institute, Biochemistry and Biophysics Center, National Institutes of Health, Bethesda, MD, 20892, USA.
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3
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Wang S, Lei H, Ji Z. Exploring Oxidoreductases from Extremophiles for Biosynthesis in a Non-Aqueous System. Int J Mol Sci 2023; 24:ijms24076396. [PMID: 37047370 PMCID: PMC10094897 DOI: 10.3390/ijms24076396] [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: 10/30/2022] [Revised: 03/19/2023] [Accepted: 03/27/2023] [Indexed: 04/14/2023] Open
Abstract
Organic solvent tolerant oxidoreductases are significant for both scientific research and biomanufacturing. However, it is really challenging to obtain oxidoreductases due to the shortages of natural resources and the difficulty to obtained it via protein modification. This review summarizes the recent advances in gene mining and structure-functional study of oxidoreductases from extremophiles for non-aqueous reaction systems. First, new strategies combining genome mining with bioinformatics provide new insights to the discovery and identification of novel extreme oxidoreductases. Second, analysis from the perspectives of amino acid interaction networks explain the organic solvent tolerant mechanism, which regulate the discrete structure-functional properties of extreme oxidoreductases. Third, further study by conservation and co-evolution analysis of extreme oxidoreductases provides new perspectives and strategies for designing robust enzymes for an organic media reaction system. Furthermore, the challenges and opportunities in designing biocatalysis non-aqueous systems are highlighted.
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Affiliation(s)
- Shizhen Wang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Xiamen Key Laboratory of Synthetic Biotechnology, Xiamen University, Xiamen 361005, China
| | - Hangbin Lei
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Zhehui Ji
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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4
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Luan Y, Tang Z, He Y, Xie Z. Intra-Domain Residue Coevolution in Transcription Factors Contributes to DNA Binding Specificity. Microbiol Spectr 2023; 11:e0365122. [PMID: 36943132 PMCID: PMC10100741 DOI: 10.1128/spectrum.03651-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/22/2023] [Indexed: 03/23/2023] Open
Abstract
Understanding the basis of the DNA-binding specificity of transcription factors (TFs) has been of long-standing interest. Despite extensive efforts to map millions of putative TF binding sequences, identifying the critical determinants for DNA binding specificity remains a major challenge. The coevolution of residues in proteins occurs due to a shared evolutionary history. However, it is unclear how coevolving residues in TFs contribute to DNA binding specificity. Here, we systematically collected publicly available data sets from multiple large-scale high-throughput TF-DNA interaction screening experiments for the major TF families with large numbers of TF members. These families included the Homeobox, HLH, bZIP_1, Ets, HMG_box, ZF-C4, and Zn_clus TFs. We detected TF subclass-determining sites (TSDSs) and showed that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs, particularly for the Homeobox, HLH, Ets, bZIP_1, and HMG_box TF families. By in silico modeling, we showed that mutation of the highly coevolving residues could significantly reduce the stability of the TF-DNA complex. The distant residues from the DNA interface also contributed to TF-DNA binding activity. Overall, our study gave evidence that coevolved residues relate to transcriptional regulation and provided insights into the potential application of engineered DNA-binding domains and proteins. IMPORTANCE While unraveling DNA-binding specificity of TFs is the key to understanding the basis and molecular mechanism of gene expression regulation, identifying the critical determinants that contribute to DNA binding specificity remains a major challenge. In this study, we provided evidence showing that coevolving residues in TF domains contributed to DNA binding specificity. We demonstrated that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs. Mutation of the coevolving residue pairs (CRPs) could significantly reduce the stability of THE TF-DNA complex, and even the distant residues from the DNA interface contribute to TF-DNA binding activity. Collectively, our study expands our knowledge of the interactions among coevolved residues in TFs, tertiary contacting, and functional importance in refined transcriptional regulation. Understanding the impact of coevolving residues in TFs will help understand the details of transcription of gene regulation and advance the application of engineered DNA-binding domains and protein.
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Affiliation(s)
- Yizhao Luan
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehua Tang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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5
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Krishnamohan A, Hamilton GL, Goutam R, Sanabria H, Morcos F. Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins. J Phys Chem B 2023; 127:884-898. [PMID: 36693159 PMCID: PMC9900596 DOI: 10.1021/acs.jpcb.2c06720] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.
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Affiliation(s)
- Aishwarya Krishnamohan
- Departments of Biological Sciences and Bioengineering, University of Texas at Dallas, Richardson, Texas75080, United States
| | - George L Hamilton
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Rajen Goutam
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina29634, United States
| | - Faruck Morcos
- Departments of Biological Sciences and Bioengineering, University of Texas at Dallas, Richardson, Texas75080, United States.,Center for Systems Biology, University of Texas at Dallas, Richardson, Texas75080, United States
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6
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Schafer JW, Porter LL. Evolutionary selection of proteins with two folds. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524637. [PMID: 36789442 PMCID: PMC9928049 DOI: 10.1101/2023.01.18.524637] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Although most globular proteins fold into a single stable structure 1 , an increasing number have been shown to remodel their secondary and tertiary structures in response to cellular stimuli 2 . State-of-the-art algorithms 3-5 predict that these fold-switching proteins assume only one stable structure 6,7 , missing their functionally critical alternative folds. Why these algorithms predict a single fold is unclear, but all of them infer protein structure from coevolved amino acid pairs. Here, we hypothesize that coevolutionary signatures are being missed. Suspecting that over-represented single-fold sequences may be masking these signatures, we developed an approach to search both highly diverse protein superfamilies-composed of single-fold and fold-switching variants-and protein subfamilies with more fold-switching variants. This approach successfully revealed coevolution of amino acid pairs uniquely corresponding to both conformations of 56/58 fold-switching proteins from distinct families. Then, using a set of coevolved amino acid pairs predicted by our approach, we successfully biased AlphaFold2 5 to predict two experimentally consistent conformations of a candidate protein with unsolved structure. The discovery of widespread dual-fold coevolution indicates that fold-switching sequences have been preserved by natural selection, implying that their functionalities provide evolutionary advantage and paving the way for predictions of diverse protein structures from single sequences.
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Affiliation(s)
- Joseph W. Schafer
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Lauren L. Porter
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
- National Heart, Lung, and Blood Institute, Biochemistry and Biophysics Center, National Institutes of Health, Bethesda, MD 20892, USA
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7
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Sala D, Del Alamo D, Mchaourab HS, Meiler J. Modeling of protein conformational changes with Rosetta guided by limited experimental data. Structure 2022; 30:1157-1168.e3. [PMID: 35597243 PMCID: PMC9357069 DOI: 10.1016/j.str.2022.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/08/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022]
Abstract
Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.
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Affiliation(s)
- Davide Sala
- Institute for Drug Discovery, Leipzig University, Leipzig, Saxony 04103, Germany
| | - Diego Del Alamo
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37235, USA
| | - Hassane S Mchaourab
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37235, USA
| | - Jens Meiler
- Institute for Drug Discovery, Leipzig University, Leipzig, Saxony 04103, Germany; Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA.
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8
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van Keulen SC, Martin J, Colizzi F, Frezza E, Trpevski D, Diaz NC, Vidossich P, Rothlisberger U, Hellgren Kotaleski J, Wade RC, Carloni P. Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Siri C. van Keulen
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Science for Life, Faculty of Science – Chemistry Utrecht University Utrecht The Netherlands
| | - Juliette Martin
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry University of Lyon Lyon France
| | - Francesco Colizzi
- Molecular Ocean Laboratory, Department of Marine Biology and Oceanography Institute of Marine Sciences, ICM‐CSIC Barcelona Spain
| | - Elisa Frezza
- Université Paris Cité, CiTCoM, CNRS Paris France
| | - Daniel Trpevski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm
| | - Nuria Cirauqui Diaz
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry University of Lyon Lyon France
| | - Pietro Vidossich
- Molecular Modeling and Drug Discovery Lab Istituto Italiano di Tecnologia Genoa Italy
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm
- Department of Neuroscience Karolinska Institute Stockholm
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany
- Center for Molecular Biology (ZMBH), DKFZ‐ZMBH Alliance, and Interdisciplinary Center for Scientific Computing (IWR) Heidelberg University Heidelberg Germany
| | - Paolo Carloni
- Institute for Neuroscience and Medicine (INM‐9) and Institute for Advanced Simulations (IAS‐5) “Computational biomedicine” Forschungszentrum Jülich Jülich Germany
- INM‐11 JARA‐Institute: Molecular Neuroscience and Neuroimaging Forschungszentrum Jülich Jülich Germany
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9
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Sanchez-Pulido L, Ponting CP. Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction. J Mol Biol 2021; 433:167106. [PMID: 34139218 PMCID: PMC8527833 DOI: 10.1016/j.jmb.2021.167106] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a "beads-on-a-string" model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.
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Affiliation(s)
- Luis Sanchez-Pulido
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.
| | - Chris P Ponting
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.
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10
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Kim AK, Looger LL, Porter LL. A high-throughput predictive method for sequence-similar fold switchers. Biopolymers 2021; 112:e23416. [PMID: 33462801 PMCID: PMC8404102 DOI: 10.1002/bip.23416] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 12/02/2022]
Abstract
Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> β-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> β-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10-19 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.
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Affiliation(s)
- Allen K. Kim
- National Library of MedicineNational Institutes of HealthBethesdaMarylandUSA
- National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaMarylandUSA
| | - Loren L. Looger
- Howard Hughes Medical InstituteJanelia Research CampusAshburnVirginiaUSA
| | - Lauren L. Porter
- National Library of MedicineNational Institutes of HealthBethesdaMarylandUSA
- National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaMarylandUSA
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11
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Mishra S, Looger LL, Porter LL. A sequence-based method for predicting extant fold switchers that undergo α-helix ↔ β-strand transitions. Biopolymers 2021; 112:e23471. [PMID: 34498740 PMCID: PMC8545793 DOI: 10.1002/bip.23471] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/10/2021] [Accepted: 08/10/2021] [Indexed: 12/21/2022]
Abstract
Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. Despite their biological importance, these proteins remain understudied. Predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Most previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (a) α-helix ↔ β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (b) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed by themselves (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on 99-fold-switching proteins and found strong correspondence between predicted and experimentally observed α-helix ↔ β-strand discrepancies. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer predicted α-helix ↔ β-strand discrepancies. Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier to identify extant fold switchers (Matthews correlation coefficient of .71). Although this classifier had a high false-negative rate (7/17), its false-positive rate was very low (2/136), suggesting that it can be used to predict a subset of extant fold switchers from a multitude of available genomic sequences.
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Affiliation(s)
- Soumya Mishra
- National Library of MedicineNational Institutes of HealthBethesdaMarylandUSA
- Howard Hughes Medical Institute, Janelia Research CampusAshburnVirginiaUSA
| | - Loren L. Looger
- Howard Hughes Medical Institute, Janelia Research CampusAshburnVirginiaUSA
| | - Lauren L. Porter
- National Library of MedicineNational Institutes of HealthBethesdaMarylandUSA
- National Heart, Lung, and Blood InstituteNational Institutes of HealthBethesdaMarylandUSA
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12
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Das M, Chen N, LiWang A, Wang LP. Identification and characterization of metamorphic proteins: Current and future perspectives. Biopolymers 2021; 112:e23473. [PMID: 34528703 DOI: 10.1002/bip.23473] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/06/2022]
Abstract
Proteins that can reversibly alternate between distinctly different folds under native conditions are described as being metamorphic. The "metamorphome" is the collection of all metamorphic proteins in the proteome, but it remains unknown the extent to which the proteome is populated by this class of proteins. We propose that uncovering the metamorphome will require a synergy of computational screening of protein sequences to identify potential metamorphic behavior and validation through experimental techniques. This perspective discusses computational and experimental approaches that are currently used to predict and characterize metamorphic proteins as well as the need for developing improved methodologies. Since metamorphic proteins act as molecular switches, understanding their properties and behavior could lead to novel applications of these proteins as sensors in biological or environmental contexts.
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Affiliation(s)
- Madhurima Das
- School of Natural Sciences, University of California, Merced, California, USA
| | - Nanhao Chen
- Department of Chemistry, University of California, Davis, California, USA
| | - Andy LiWang
- School of Natural Sciences, University of California, Merced, California, USA.,Department of Chemistry and Biochemistry, University of California, Merced, California, USA.,Center for Cellular and Biomolecular Machines, University of California, Merced, California, USA.,Health Sciences Research Institute, University of California, Merced, California, USA.,Center for Circadian Biology, University of California, San Diego, California, USA
| | - Lee-Ping Wang
- Department of Chemistry, University of California, Davis, California, USA
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13
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Laine E, Eismann S, Elofsson A, Grudinin S. Protein sequence-to-structure learning: Is this the end(-to-end revolution)? Proteins 2021; 89:1770-1786. [PMID: 34519095 DOI: 10.1002/prot.26235] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
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Affiliation(s)
- Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Stephan Eismann
- Department of Computer Science and Applied Physics, Stanford University, Stanford, California, USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
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14
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Colizzi F, Orozco M. Probing allosteric regulations with coevolution-driven molecular simulations. SCIENCE ADVANCES 2021; 7:eabj0786. [PMID: 34516882 PMCID: PMC8442858 DOI: 10.1126/sciadv.abj0786] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
Protein-mediated allosteric regulations are essential in biology, but their quantitative characterization continues to posit formidable challenges for both experiments and computations. Here, we combine coevolutionary information, multiscale molecular simulations, and free-energy methods to interrogate and quantify the allosteric regulation of functional changes in protein complexes. We apply this approach to investigate the regulation of adenylyl cyclase (AC) by stimulatory and inhibitory G proteins—a prototypical allosteric system that has long escaped from in-depth molecular characterization. We reveal a surprisingly simple ON/OFF regulation of AC functional dynamics through multiple pathways of information transfer. The binding of G proteins reshapes the free-energy landscape of AC following the classical population-shift paradigm. The model agrees with structural and biochemical data and reveals previously unknown experimentally consistent intermediates. Our approach showcases a general strategy to explore uncharted functional space in complex biomolecular regulations.
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Affiliation(s)
- Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri Reixac 10, Barcelona 08028, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri Reixac 10, Barcelona 08028, Spain
- Departament de Bioquímica i Biomedicina, Facultat de Biologia, Universitat de Barcelona, Avinguda Diagonal 647, Barcelona 08028, Spain
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15
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Laine E, Grudinin S. HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps. J Phys Chem B 2021; 125:2577-2588. [PMID: 33687221 DOI: 10.1021/acs.jpcb.0c11633] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.
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Affiliation(s)
- Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005 Paris, France
| | - Sergei Grudinin
- CNRS, Inria, Grenoble INP, LJK, Univ. Grenoble Alpes, 38000 Grenoble, France
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16
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Kim AK, Porter LL. Functional and Regulatory Roles of Fold-Switching Proteins. Structure 2020; 29:6-14. [PMID: 33176159 DOI: 10.1016/j.str.2020.10.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/15/2020] [Accepted: 10/20/2020] [Indexed: 01/08/2023]
Abstract
Fold-switching proteins respond to cellular stimuli by remodeling their secondary structures and changing their functions. Whereas several previous reviews have focused on various structural, physical-chemical, and evolutionary aspects of this newly emerging class of proteins, this minireview focuses on how fold switching modulates protein function and regulates biological processes. It first compares and contrasts fold switchers with other known types of proteins. Second, it presents examples of how various proteins can change their functions through fold switching. Third, it demonstrates that fold switchers can regulate biological processes by discussing two proteins, RfaH and KaiB, whose dramatic secondary structure remodeling events directly affect gene expression and a circadian clock, respectively. Finally, this minireview discusses how the field of protein fold switching might advance.
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Affiliation(s)
- Allen K Kim
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lauren L Porter
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA; National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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17
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Feng J, Shukla D. FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution. J Phys Chem B 2020; 124:3605-3615. [PMID: 32283936 DOI: 10.1021/acs.jpcb.9b11869] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e., spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.
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18
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Orellana L. Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier. Front Mol Biosci 2019; 6:117. [PMID: 31750315 PMCID: PMC6848229 DOI: 10.3389/fmolb.2019.00117] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Accepted: 10/14/2019] [Indexed: 12/16/2022] Open
Abstract
Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.
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Affiliation(s)
- Laura Orellana
- Institutionen för Biokemi och Biofysik, Stockholms Universitet, Stockholm, Sweden.,Science for Life Laboratory, Solna, Sweden
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19
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Li Y, De la Paz JA, Jiang X, Liu R, Pokkulandra AP, Bleris L, Morcos F. Coevolutionary Couplings Unravel PAM-Proximal Constraints of CRISPR-SpCas9. Biophys J 2019; 117:1684-1691. [PMID: 31648792 DOI: 10.1016/j.bpj.2019.09.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/25/2019] [Accepted: 09/30/2019] [Indexed: 01/07/2023] Open
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR) system, an immune system analog found in prokaryotes, allows a single-guide RNA to direct a CRISPR-associated protein (Cas) with combined helicase and nuclease activity to DNA. The presence of a specific protospacer adjacent motif (PAM) next to the DNA target site plays a crucial role in determining both efficacy and specificity of gene editing. Herein, we introduce a coevolutionary framework to computationally unveil nonobvious molecular interactions in CRISPR systems and experimentally probe their functional role. Specifically, we use direct coupling analysis, a statistical inference framework used to infer direct coevolutionary couplings, in the context of protein/nucleic acid interactions. Applied to Streptococcus pyogenes Cas9, a Hamiltonian metric obtained from coevolutionary relationships reveals, to our knowledge, novel PAM-proximal nucleotide preferences at the seventh position of S. pyogenes Cas9 PAM (5'-NGRNNNT-3'), which was experimentally confirmed by in vitro and functional assays in human cells. We show that coevolved and conserved interactions point to specific clues toward rationally engineering new generations of Cas9 systems and may eventually help decipher the diversity of this family of proteins.
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Affiliation(s)
- Yi Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas
| | - José A De la Paz
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Xianli Jiang
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Richard Liu
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas
| | - Adarsha P Pokkulandra
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Leonidas Bleris
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas; Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas.
| | - Faruck Morcos
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas; Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas.
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20
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Saldaño TE, Tosatto SCE, Parisi G, Fernandez-Alberti S. Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2019; 48:559-568. [PMID: 31273390 DOI: 10.1007/s00249-019-01384-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/31/2019] [Accepted: 07/01/2019] [Indexed: 11/26/2022]
Abstract
According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like β-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.
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Affiliation(s)
- Tadeo E Saldaño
- Universidad Nacional de Quilmes/CONICET, Roque Saenz Peña 352, B1876BXD, Bernal, Argentina
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padova, Viale G. Colombo 3, 5131, Padua, Italy
| | - Gustavo Parisi
- Universidad Nacional de Quilmes/CONICET, Roque Saenz Peña 352, B1876BXD, Bernal, Argentina
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21
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The role of coevolutionary signatures in protein interaction dynamics, complex inference, molecular recognition, and mutational landscapes. Curr Opin Struct Biol 2019; 56:179-186. [PMID: 31029927 DOI: 10.1016/j.sbi.2019.03.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 03/18/2019] [Accepted: 03/19/2019] [Indexed: 11/22/2022]
Abstract
Evolution imposes constraints at the interface of interacting biomolecules in order to preserve function or maintain fitness. This pressure may have a direct effect on the sequence composition of interacting biomolecules. As a result, statistical patterns of amino acid or nucleotide covariance that encode for physical and functional interactions are observed in sequences of extant organisms. In recent years, global pairwise models of amino acid and nucleotide coevolution from multiple sequence alignments have been developed and utilized to study molecular interactions in structural biology. In proteins, for which the energy landscape is funneled and minimally frustrated, a direct connection between the physical and sequence space landscapes can be established. Estimating coevolutionary information from sequences of interacting molecules has a broad impact in molecular biology. Applications include the accurate determination of 3D structures of molecular complexes, inference of protein interaction partners, models of protein-protein interaction specificity, the elucidation, and design of protein-nucleic acid recognition as well as the discovery of genome-wide epistatic effects. The current state of the art of coevolutionary analysis includes biomedical applications ranging from mutational landscapes and drug-design to vaccine development.
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22
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Liang Z, Verkhivker GM, Hu G. Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications. Brief Bioinform 2019; 21:815-835. [DOI: 10.1093/bib/bbz029] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/04/2019] [Accepted: 02/21/2019] [Indexed: 12/24/2022] Open
Abstract
Abstract
Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.
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Affiliation(s)
- Zhongjie Liang
- School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Gennady M Verkhivker
- Department of Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, USA
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA
| | - Guang Hu
- School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
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23
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Abstract
The native state of proteins is composed of conformers in dynamical equilibrium. In this chapter, different issues related to conformational diversity are explored using a curated and experimentally based database called CoDNaS (Conformational Diversity in the Native State). This database is a collection of redundant structures for the same sequence. CoDNaS estimates the degree of conformational diversity using different global and local structural similarity measures. It allows the user to explore how structural differences among conformers change as a function of several structural features providing further biological information. This chapter explores the measurement of conformational diversity and its relationship with sequence divergence. Also, it discusses how proteins with high conformational diversity could affect homology modeling techniques.
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Affiliation(s)
- Alexander Miguel Monzon
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, Bernal, Argentina
| | - Maria Silvina Fornasari
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, Bernal, Argentina
| | - Diego Javier Zea
- Structural Bioinformatics Unit, Fundación Instituto Leloir, CONICET, Buenos Aires, Argentina
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, Bernal, Argentina.
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24
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Abstract
Thanks to the explosion of genomic sequencing, coevolutionary analysis of protein sequences has gained great and ever-increasing popularity in the last decade, and it is currently an important and well-established tool in structural bioinformatics and computational biology. This chapter concisely introduces the theoretical foundation and the practical aspects of coevolutionary analysis, as well as discusses the molecular modeling strategies to exploit its results in the study of protein structure, dynamics, and interactions. We present here a complete pipeline from sequence extraction to contact prediction through two examples, focusing on the predictions of inter-residue contacts in a single protein domain and on the analysis of a multi-domain protein that undergoes functional, large-scale conformational transitions.
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Affiliation(s)
- Duccio Malinverni
- Laboratory of Statistical Biophysics, Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Alessandro Barducci
- Centre de Biochimie Structurale (CBS), INSERM, CNRS, Université de Montpellier, Montpellier, France.
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25
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Coevolutionary Signals and Structure-Based Models for the Prediction of Protein Native Conformations. Methods Mol Biol 2019; 1851:83-103. [PMID: 30298393 DOI: 10.1007/978-1-4939-8736-8_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The analysis of coevolutionary signals from families of evolutionarily related sequences is a recent conceptual framework that provides valuable information about unique intramolecular interactions and, therefore, can assist in the elucidation of biomolecular conformations. It is based on the idea that compensatory mutations at specific residue positions in a sequence help preserve stability of protein architecture and function and leave a statistical signature related to residue-residue interactions in the 3D structure of the protein. Consequently, statistical analysis of these correlated mutations in subsets of protein sequence alignments can be used to predict which residue pairs should be in spatial proximity in the native functional protein fold. These predicted signals can be then used to guide molecular dynamics (MD) simulations to predict the three-dimensional coordinates of a functional amino acid chain. In this chapter, we introduce a general and efficient methodology to perform coevolutionary analysis on protein sequences and to use this information in combination with computational physical models to predict the native 3D conformation of functional polypeptides. We present a step-by-step methodology that includes the description and application of software tools and databases required to infer tertiary structures of a protein fold. The general pipeline includes instructions on (1) how to obtain direct amino acid couplings from protein sequences using direct coupling analysis (DCA), (2) how to incorporate such signals as interaction potentials in Cα structure-based models (SBMs) to drive protein-folding MD simulations, (3) a procedure to estimate secondary structure and how to include such estimates in the topology files required in the MD simulations, and (4) how to build full atomic models based on the top Cα candidates selected in the pipeline. The information presented in this chapter is self-contained and sufficient to allow a computational scientist to predict structures of proteins using publicly available algorithms and databases.
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26
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Mechanical variations in proteins with large-scale motions highlight the formation of structural locks. J Struct Biol 2018; 203:195-204. [DOI: 10.1016/j.jsb.2018.05.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 05/18/2018] [Accepted: 05/22/2018] [Indexed: 12/18/2022]
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27
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Peng JH, Wang W, Yu YQ, Gu HL, Huang X. Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems. CHINESE J CHEM PHYS 2018. [DOI: 10.1063/1674-0068/31/cjcp1806147] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Jun-hui Peng
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Wei Wang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Ye-qing Yu
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Han-lin Gu
- Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xuhui Huang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
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28
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Abstract
![]()
Since
the proposal of Anfinsen’s thermodynamic hypothesis
in 1963, our understanding of protein folding and dynamics has gained
significant appreciation of its nuance and complexity. Intrinsically
disordered proteins, chameleonic sequences, morpheeins, and metamorphic
proteins have broadened the protein folding paradigm. Here, we discuss
noncanonical protein folding patterns, with an emphasis on metamorphic
proteins, and we review known metamorphic proteins that occur naturally
and that have been engineered in the laboratory. Finally, we discuss
research areas surrounding metamorphic proteins that are primed for
future exploration, including evolution, drug discovery, and the quest
for previously unrecognized metamorphs. As we enter an age where we
are capable of complex bioinformatic searches and de novo protein design, we are primed to search for previously unrecognized
metamorphic proteins and to design our own metamorphs to act as targeted,
switchable drugs; biosensors; and more.
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Affiliation(s)
- Acacia F. Dishman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States
| | - Brian F. Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States
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29
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dos Santos RN, Khan S, Morcos F. Characterization of C-ring component assembly in flagellar motors from amino acid coevolution. ROYAL SOCIETY OPEN SCIENCE 2018; 5:171854. [PMID: 29892378 PMCID: PMC5990795 DOI: 10.1098/rsos.171854] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Accepted: 04/05/2018] [Indexed: 06/08/2023]
Abstract
Bacterial flagellar motility, an important virulence factor, is energized by a rotary motor localized within the flagellar basal body. The rotor module consists of a large framework (the C-ring), composed of the FliG, FliM and FliN proteins. FliN and FliM contacts the FliG torque ring to control the direction of flagellar rotation. We report that structure-based models constrained only by residue coevolution can recover the binding interface of atomic X-ray dimer complexes with remarkable accuracy (approx. 1 Å RMSD). We propose a model for FliM-FliN heterodimerization, which agrees accurately with homologous interfaces as well as in situ cross-linking experiments, and hence supports a proposed architecture for the lower portion of the C-ring. Furthermore, this approach allowed the identification of two discrete and interchangeable homodimerization interfaces between FliM middle domains that agree with experimental measurements and might be associated with C-ring directional switching dynamics triggered upon binding of CheY signal protein. Our findings provide structural details of complex formation at the C-ring that have been difficult to obtain with previous methodologies and clarify the architectural principle that underpins the ultra-sensitive allostery exhibited by this ring assembly that controls the clockwise or counterclockwise rotation of flagella.
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Affiliation(s)
- Ricardo Nascimento dos Santos
- Institute of Chemistry and Center for Computational Engineering and Science, University of Campinas, Campinas, SP, Brazil
| | - Shahid Khan
- Molecular Biology Consortium, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA
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30
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Nicoludis JM, Gaudet R. Applications of sequence coevolution in membrane protein biochemistry. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2018; 1860:895-908. [PMID: 28993150 PMCID: PMC5807202 DOI: 10.1016/j.bbamem.2017.10.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 09/28/2017] [Accepted: 10/02/2017] [Indexed: 12/22/2022]
Abstract
Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.
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Affiliation(s)
- John M Nicoludis
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, United States
| | - Rachelle Gaudet
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, United States.
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31
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dos Santos RN, Ferrari AJR, de Jesus HCR, Gozzo FC, Morcos F, Martínez L. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics 2018; 34:2201-2208. [DOI: 10.1093/bioinformatics/bty074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/10/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ricardo N dos Santos
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
| | | | | | - Fábio C Gozzo
- Institute of Chemistry, University of Campinas, Campinas, Brazil
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, USA
| | - Leandro Martínez
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
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32
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Feng J, Shukla D. Characterizing Conformational Dynamics of Proteins Using Evolutionary Couplings. J Phys Chem B 2018; 122:1017-1025. [DOI: 10.1021/acs.jpcb.7b07529] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jiangyan Feng
- Department
of Chemical and Biomolecular Engineering, ‡Center for Biophysics and Quantitative
Biology, §Department of Plant Biology, and ∥National Center for Supercomputing Applications, University of Illinois, Urbana, Illinois 61801, United States
| | - Diwakar Shukla
- Department
of Chemical and Biomolecular Engineering, ‡Center for Biophysics and Quantitative
Biology, §Department of Plant Biology, and ∥National Center for Supercomputing Applications, University of Illinois, Urbana, Illinois 61801, United States
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33
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Cossins BP, Lawson ADG, Shi J. Computational Exploration of Conformational Transitions in Protein Drug Targets. Methods Mol Biol 2018; 1762:339-365. [PMID: 29594780 DOI: 10.1007/978-1-4939-7756-7_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.
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Affiliation(s)
- Benjamin P Cossins
- Computer-Aided Drug Design and Structural Biology, UCB Pharma, Slough, UK.
| | | | - Jiye Shi
- Computer-Aided Drug Design and Structural Biology, UCB Pharma, Slough, UK
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Prediction of Structures and Interactions from Genome Information. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:123-152. [DOI: 10.1007/978-981-13-2200-6_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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35
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Kedrov A, Wickles S, Crevenna AH, van der Sluis EO, Buschauer R, Berninghausen O, Lamb DC, Beckmann R. Structural Dynamics of the YidC:Ribosome Complex during Membrane Protein Biogenesis. Cell Rep 2017; 17:2943-2954. [PMID: 27974208 PMCID: PMC5186731 DOI: 10.1016/j.celrep.2016.11.059] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 10/26/2016] [Accepted: 11/20/2016] [Indexed: 01/30/2023] Open
Abstract
Members of the YidC/Oxa1/Alb3 family universally facilitate membrane protein biogenesis, via mechanisms that have thus far remained unclear. Here, we investigated two crucial functional aspects: the interaction of YidC with ribosome:nascent chain complexes (RNCs) and the structural dynamics of RNC-bound YidC in nanodiscs. We observed that a fully exposed nascent transmembrane domain (TMD) is required for high-affinity YidC:RNC interactions, while weaker binding may already occur at earlier stages of translation. YidC efficiently catalyzed the membrane insertion of nascent TMDs in both fluid and gel phase membranes. Cryo-electron microscopy and fluorescence analysis revealed a conformational change in YidC upon nascent chain insertion: the essential TMDs 2 and 3 of YidC were tilted, while the amphipathic helix EH1 relocated into the hydrophobic core of the membrane. We suggest that EH1 serves as a mechanical lever, facilitating a coordinated movement of YidC TMDs to trigger the release of nascent chains into the membrane.
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Affiliation(s)
- Alexej Kedrov
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany.
| | - Stephan Wickles
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany
| | - Alvaro H Crevenna
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), the NanoSystems Initiative Munich (NIM), Ludwig-Maximilians-University Munich, Butenandtstrasse 11, Munich 81377, Germany
| | - Eli O van der Sluis
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany
| | - Robert Buschauer
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany
| | - Otto Berninghausen
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany
| | - Don C Lamb
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), the NanoSystems Initiative Munich (NIM), Ludwig-Maximilians-University Munich, Butenandtstrasse 11, Munich 81377, Germany; Center for Integrated Protein Science Munich (CiPSM), Ludwig-Maximilians-University, Butenandtstrasse 5-13, Munich 81377, Germany
| | - Roland Beckmann
- Gene Center Munich, Department of Biochemistry, Ludwig-Maximilians-University Munich, Feodor-Lynen-Strasse 25, Munich 81377, Germany; Center for Integrated Protein Science Munich (CiPSM), Ludwig-Maximilians-University, Butenandtstrasse 5-13, Munich 81377, Germany.
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36
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Cirauqui N, Abriata LA, van der Goot FG, Dal Peraro M. Structural, physicochemical and dynamic features conserved within the aerolysin pore-forming toxin family. Sci Rep 2017; 7:13932. [PMID: 29066778 PMCID: PMC5654971 DOI: 10.1038/s41598-017-13714-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 09/26/2017] [Indexed: 11/10/2022] Open
Abstract
Aerolysin is the founding member of a major class of β-pore-forming toxins (β-PFTs) found throughout all kingdoms of life. PFTs are cytotoxic proteins produced as soluble monomers, which oligomerize at the membrane of target host cells forming pores that may lead to osmotic lysis and cell death. Besides their role in microbial infection, they have become interesting for their potential as biotechnological sensors and delivery systems. Using an approach that integrates bioinformatics with molecular modeling and simulation, we looked for conserved features across this large toxin family. The cell surface-binding domains present high variability within the family to provide membrane receptor specificity. On the contrary, the novel concentric double β-barrel structure found in aerolysin is highly conserved in terms of sequence, structure and conformational dynamics, which likely contribute to preserve a common transition mechanism from the prepore to the mature pore within the family.Our results point to the key role of several amino acids in the conformational changes needed for oligomerization and further pore formation, such as Y221, W227, P248, Q263 and L277, which we propose are involved in the release of the stem loop and the two adjacent β-strands to form the transmembrane β-barrel.
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Affiliation(s)
- Nuria Cirauqui
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
- Department of Pharmaceutical Biotechnology, Universidade Federal do Rio de Janeiro, 21941-902, Rio de Janeiro, Brazil
| | - Luciano A Abriata
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - F Gisou van der Goot
- Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
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37
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Shamsi Z, Moffett AS, Shukla D. Enhanced unbiased sampling of protein dynamics using evolutionary coupling information. Sci Rep 2017; 7:12700. [PMID: 28983093 PMCID: PMC5629199 DOI: 10.1038/s41598-017-12874-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 09/14/2017] [Indexed: 12/25/2022] Open
Abstract
One of the major challenges in atomistic simulations of proteins is efficient sampling of pathways associated with rare conformational transitions. Recent developments in statistical methods for computation of direct evolutionary couplings between amino acids within and across polypeptide chains have allowed for inference of native residue contacts, informing accurate prediction of protein folds and multimeric structures. In this study, we assess the use of distances between evolutionarily coupled residues as natural choices for reaction coordinates which can be incorporated into Markov state model-based adaptive sampling schemes and potentially used to predict not only functional conformations but also pathways of conformational change, protein folding, and protein-protein association. We demonstrate the utility of evolutionary couplings in sampling and predicting activation pathways of the β 2-adrenergic receptor (β 2-AR), folding of the FiP35 WW domain, and dimerization of the E. coli molybdopterin synthase subunits. We find that the time required for β 2-AR activation and folding of the WW domain are greatly diminished using evolutionary couplings-guided adaptive sampling. Additionally, we were able to identify putative molybdopterin synthase association pathways and near-crystal structure complexes from protein-protein association simulations.
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Affiliation(s)
- Zahra Shamsi
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, 61801, USA
| | - Alexander S Moffett
- Center for Biophysics and Quantitative Biology, University of Illinois, Urbana, IL, 61801, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, 61801, USA.
- Center for Biophysics and Quantitative Biology, University of Illinois, Urbana, IL, 61801, USA.
- Department of Plant Biology, University of Illinois, Urbana, IL, 61801, USA.
- National Center for Supercomputing Applications, University of Illinois, Urbana, IL, 61801, USA.
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38
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Monzon AM, Zea DJ, Marino-Buslje C, Parisi G. Homology modeling in a dynamical world. Protein Sci 2017; 26:2195-2206. [PMID: 28815769 DOI: 10.1002/pro.3274] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 08/09/2017] [Accepted: 08/09/2017] [Indexed: 12/31/2022]
Abstract
A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.
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Affiliation(s)
- Alexander Miguel Monzon
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, B1876BXD, Bernal, Argentina
| | - Diego Javier Zea
- Structural Bioinformatics Unit, Fundación Instituto Leloir, CONICET, C1405BWE Ciudad Autónoma de Buenos Aires, Argentina
| | - Cristina Marino-Buslje
- Structural Bioinformatics Unit, Fundación Instituto Leloir, CONICET, C1405BWE Ciudad Autónoma de Buenos Aires, Argentina
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, B1876BXD, Bernal, Argentina
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39
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Abstract
Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.
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40
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Simkovic F, Ovchinnikov S, Baker D, Rigden DJ. Applications of contact predictions to structural biology. IUCRJ 2017; 4:291-300. [PMID: 28512576 PMCID: PMC5414403 DOI: 10.1107/s2052252517005115] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 04/03/2017] [Indexed: 06/07/2023]
Abstract
Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo-graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.
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Affiliation(s)
- Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195, USA
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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41
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Bastolla U, Dehouck Y, Echave J. What evolution tells us about protein physics, and protein physics tells us about evolution. Curr Opin Struct Biol 2017; 42:59-66. [DOI: 10.1016/j.sbi.2016.10.020] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 10/19/2016] [Accepted: 10/24/2016] [Indexed: 12/21/2022]
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42
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Stetz G, Verkhivker GM. Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication. PLoS Comput Biol 2017; 13:e1005299. [PMID: 28095400 PMCID: PMC5240922 DOI: 10.1371/journal.pcbi.1005299] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 12/06/2016] [Indexed: 12/28/2022] Open
Abstract
Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. The diversity of allosteric mechanisms in the Hsp70 proteins could range from modulation of the inter-domain interactions and conformational dynamics to fine-tuning of the Hsp70 interactions with co-chaperones. The goal of this study is to present a systematic computational analysis of the dynamic and evolutionary factors underlying allosteric structural transformations of the Hsp70 proteins. We investigated the relationship between functional dynamics, residue coevolution, and network organization of residue interactions in the Hsp70 proteins. The results of this study revealed that conformational dynamics of the Hsp70 proteins may be linked with coevolutionary propensities and mutual information dependencies of the protein residues. Modularity and connectivity of allosteric interactions in the Hsp70 chaperones are coordinated by stable functional sites that feature unique coevolutionary signatures and high network centrality. The emergence of the inter-domain communities that are coordinated by functional centers and include highly coevolving residues could facilitate structural transitions through cooperative reorganization of the local interacting modules. We determined that the differences in the modularity of the residue interactions and organization of coevolutionary networks in DnaK may be associated with variations in their allosteric mechanisms. The network signatures of the DnaK structures are characteristic of a population-shift allostery that allows for coordinated structural rearrangements of local communities. A dislocation of mediating centers and insufficient coevolutionary coupling between functional regions may render a reduced cooperativity and promote a limited entropy-driven allostery in the Sse1 chaperone that occurs without structural changes. The results of this study showed that a network-centric framework and a community-hopping model of allosteric communication pathways may provide novel insights into molecular and evolutionary principles of allosteric regulation in the Hsp70 proteins.
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Affiliation(s)
- Gabrielle Stetz
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
| | - Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America
- Chapman University School of Pharmacy, Irvine, California, United States of America
- * E-mail:
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43
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Schueler-Furman O, Wodak SJ. Computational approaches to investigating allostery. Curr Opin Struct Biol 2016; 41:159-171. [PMID: 27607077 DOI: 10.1016/j.sbi.2016.06.017] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 06/23/2016] [Indexed: 01/01/2023]
Abstract
Allosteric regulation plays a key role in many biological processes, such as signal transduction, transcriptional regulation, and many more. It is rooted in fundamental thermodynamic and dynamic properties of macromolecular systems that are still poorly understood and are moreover modulated by the cellular context. Here we review the computational approaches used in the investigation of allosteric processes in protein systems. We outline how the models of allostery have evolved from their initial formulation in the sixties to the current views, which more fully account for the roles of the thermodynamic and dynamic properties of the system. We then describe the major classes of computational approaches employed to elucidate the mechanisms of allostery, the insights they have provided, as well as their limitations. We complement this analysis by highlighting the role of computational approaches in promising practical applications, such as the engineering of regulatory modules and identifying allosteric binding sites.
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Affiliation(s)
- Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada (IMRIC), Hebrew University, Hadassah Medical School, POB 12272, Jerusalem 91120, Israel
| | - Shoshana J Wodak
- VIB Structural Biology Research Center, VUB, Pleinlaan 2, 1050 Brussels, Belgium.
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Pandini A, Morcos F, Khan S. The Gearbox of the Bacterial Flagellar Motor Switch. Structure 2016; 24:1209-20. [PMID: 27345932 PMCID: PMC4938800 DOI: 10.1016/j.str.2016.05.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Revised: 04/26/2016] [Accepted: 05/23/2016] [Indexed: 12/11/2022]
Abstract
Switching of flagellar motor rotation sense dictates bacterial chemotaxis. Multi-subunit FliM-FliG rotor rings couple signal protein binding in FliM with reversal of a distant FliG C-terminal (FliGC) helix involved in stator contacts. Subunit dynamics were examined in conformer ensembles generated by molecular simulations from the X-ray structures. Principal component analysis extracted collective motions. Interfacial loop immobilization by complex formation coupled elastic fluctuations of the FliM middle (FliMM) and FliG middle (FliGM) domains. Coevolved mutations captured interfacial dynamics as well as contacts. FliGM rotation was amplified via two central hinges to the FliGC helix. Intrinsic flexibility, reported by the FliGMC ensembles, reconciled conformers with opposite FliGC helix orientations. FliG domain stacking deformed the inter-domain linker and reduced flexibility; but conformational changes were not triggered by engineered linker deletions that cause a rotation-locked phenotype. These facts suggest that binary rotation states arise from conformational selection by stacking interactions. Switch complex exploits differential subunit stiffness for mechanical amplification Distinct rotor protein X-ray structures generate overlapping conformer ensembles Stacking constraints on a flexible helix linker could select diverse rotation states Non-contact elastic couplings at the subunit interface in the complex have coevolved
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Affiliation(s)
- Alessandro Pandini
- Department of Computer Science and Synthetic Biology Theme, Brunel University London, Uxbridge UB8 3PH, UK; Computational Cell and Molecular Biology, The Francis Crick Institute, London NW1 1AT, UK
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Shahid Khan
- Molecular Biology Consortium, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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