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Gaur NK, Ghosh B, Goyal VD, Kulkarni K, Makde RD. Evolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of 'peptidase' domain of Spt16. J Biomol Struct Dyn 2023; 41:1445-1457. [PMID: 34971347 DOI: 10.1080/07391102.2021.2021990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses µs scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of ∼50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62% sequence identity with Sc-Spt16N) was solved at 1.7 Å resolution. Cumulative MD simulations of 6 µs were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Neeraj K Gaur
- Beamline Development and Application Section, Bhabha Atomic Research Centre, Mumbai, India.,Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Biplab Ghosh
- Beamline Development and Application Section, Bhabha Atomic Research Centre, Mumbai, India
| | - Venuka Durani Goyal
- Beamline Development and Application Section, Bhabha Atomic Research Centre, Mumbai, India
| | - Kiran Kulkarni
- Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Ravindra D Makde
- Beamline Development and Application Section, Bhabha Atomic Research Centre, Mumbai, India
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2
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Babbitt GA, Fokoue EP, Evans JR, Diller KI, Adams LE. DROIDS 3.0-Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics. Biophys J 2019; 118:541-551. [PMID: 31928763 PMCID: PMC7002913 DOI: 10.1016/j.bpj.2019.12.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/09/2019] [Accepted: 12/10/2019] [Indexed: 01/07/2023] Open
Abstract
The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.
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Affiliation(s)
- Gregory A Babbitt
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.
| | - Ernest P Fokoue
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York
| | - Joshua R Evans
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Kyle I Diller
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York; Golisano College for Computing and Information Science, Rochester, New York
| | - Lily E Adams
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
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3
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Aharoni R, Tobi D. Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities. PLoS One 2019; 14:e0226147. [PMID: 31821368 PMCID: PMC6903759 DOI: 10.1371/journal.pone.0226147] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 11/20/2019] [Indexed: 12/01/2022] Open
Abstract
Drosha and Dicer are RNase III family members of classes II and III, respectively, which play a major role in the maturation of micro-RNAs. The two proteins share similar domain arrangement and overall fold despite no apparent sequence homology. The overall structural and catalytic reaction similarity of both proteins, on the one hand, and differences in the substrate and its binding mechanisms, on the other, suggest that both proteins also share dynamic similarities and dissimilarities. Since dynamics is essential for protein function, a comparison at their dynamics level is fundamental for a complete understanding of the overall relations between these proteins. In this study, we present a dynamical comparison between human Drosha and Giardia Dicer. Gaussian Network Model and Anisotropic Network Model modes of motion of the proteins are calculated. Dynamical comparison is performed using global and local dynamic programming algorithms for aligning modes of motion. These algorithms were recently developed based on the commonly used Needleman-Wunsch and Smith-Waterman algorithms for global and local sequence alignment. The slowest mode of Drosha is different from that of Dicer due to its more bended posture and allow the motion of the double-stranded RNA-binding domain toward and away from its substrate. Among the five slowest modes dynamics similarity exists only for the second slow mode of motion of Drosha and Dicer. In addition, high local dynamics similarity is observed at the catalytic domains, in the vicinity of the catalytic residues. The results suggest that the proteins exert a similar catalytic mechanism using similar motions, especially at the catalytic sites.
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Affiliation(s)
- Rotem Aharoni
- Department of Molecular Biology, Ariel University, Ariel, Israel
| | - Dror Tobi
- Department of Molecular Biology, Ariel University, Ariel, Israel
- Department of Computer Sciences, Ariel University, Ariel, Israel
- * E-mail:
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4
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Abstract
A methodology to cluster proteins based on their dynamics’ similarity is presented. For each pair of proteins from a dataset, the structures are superimposed, and the Anisotropic Network Model modes of motions are calculated. The twelve slowest modes from each protein are matched using a local mode alignment algorithm based on the local sequence alignment algorithm of Smith–Waterman. The dynamical similarity distance matrix is calculated based on the top scoring matches of each pair and the proteins are clustered using a hierarchical clustering algorithm. The utility of this method is exemplified on a dataset of protein chains from the globin family and a dataset of tetrameric hemoglobins. The results demonstrate the effect of the quaternary structure of globin members on their intrinsic dynamics and show good ability to distinguish between different states of hemoglobin, revealing the dynamical relations between them.
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Affiliation(s)
- Dror Tobi
- Department of Molecular Biology, Ariel University, Ariel, Israel
- Department of Computer Sciences, Ariel University, Ariel, Israel
- * E-mail:
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5
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Tiwari SP, Reuter N. Conservation of intrinsic dynamics in proteins — what have computational models taught us? Curr Opin Struct Biol 2018; 50:75-81. [DOI: 10.1016/j.sbi.2017.12.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/24/2017] [Accepted: 12/08/2017] [Indexed: 12/12/2022]
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6
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Tobi D. Dynamical differences of hemoglobin and the ionotropic glutamate receptor in different states revealed by a new dynamics alignment method. Proteins 2017; 85:1507-1517. [PMID: 28459140 DOI: 10.1002/prot.25311] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 04/02/2017] [Accepted: 04/24/2017] [Indexed: 12/26/2022]
Abstract
A new algorithm for comparison of protein dynamics is presented. Compared protein structures are superposed and their modes of motions are calculated using the anisotropic network model. The obtained modes are aligned using the dynamic programming algorithm of Needleman and Wunsch, commonly used for sequence alignment. Dynamical comparison of hemoglobin in the T and R2 states reveals that the dynamics of the allosteric effector 2,3-bisphosphoglycerate binding site is different in the two states. These differences can contribute to the selectivity of the effector to the T state. Similar comparison of the ionotropic glutamate receptor in the kainate+(R,R)-2b and ZK bound states reveals that the kainate+(R,R)-2b bound states slow modes describe upward motions of ligand binding domain and the transmembrane domain regions. Such motions may lead to the opening of the receptor. The upper lobes of the LBDs of the ZK bound state have a smaller interface with the amino terminal domains above them and have a better ability to move together. The present study exemplifies the use of dynamics comparison as a tool to study protein function. Proteins 2017; 85:1507-1517. © 2014 Wiley Periodicals, Inc.
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Affiliation(s)
- Dror Tobi
- Department of Computer Sciences, Ariel University, Ariel, 40700, Israel.,Department of Molecular Biology, Ariel University, Ariel, 40700, Israel
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7
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Zhou H, Li S, Makowski L. Visualizing global properties of a molecular dynamics trajectory. Proteins 2015; 84:82-91. [PMID: 26522428 DOI: 10.1002/prot.24957] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2014] [Revised: 08/13/2015] [Accepted: 10/14/2015] [Indexed: 11/10/2022]
Abstract
Molecular dynamics (MD) trajectories are very large data sets that contain substantial information about the dynamic behavior of a protein. Condensing these data into a form that can provide intuitively useful understanding of the molecular behavior during the trajectory is a substantial challenge that has received relatively little attention. Here, we introduce the sigma-r plot, a plot of the standard deviation of intermolecular distances as a function of that distance. This representation of global dynamics contains within a single, one-dimensional plot, the average range of motion between pairs of atoms within a macromolecule. Comparison of sigma-r plots calculated from 10 ns trajectories of proteins representing the four major SCOP fold classes indicates diversity of dynamic behaviors which are recognizably different among the four classes. Differences in domain structure and molecular weight also produce recognizable features in sigma-r plots, reflective of differences in global dynamics. Plots generated from trajectories with progressively increasing simulation time reflect the increased sampling of the structural ensemble as a function of time. Single amino acid replacements can give rise to changes in global dynamics detectable through comparison of sigma-r plots. Dynamic behavior of substructures can be monitored by careful choice of interatomic vectors included in the calculation. These examples provide demonstrations of the utility of the sigma-r plot to provide a simple measure of the global dynamics of a macromolecule.
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Affiliation(s)
- Hao Zhou
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Shangyang Li
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Lee Makowski
- Department of Bioengineering, Northeastern University, Boston, Massachusetts.,Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts
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8
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Carvalho HF, Roque ACA, Iranzo O, Branco RJF. Comparison of the Internal Dynamics of Metalloproteases Provides New Insights on Their Function and Evolution. PLoS One 2015; 10:e0138118. [PMID: 26397984 PMCID: PMC4580569 DOI: 10.1371/journal.pone.0138118] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 08/25/2015] [Indexed: 11/20/2022] Open
Abstract
Metalloproteases have evolved in a vast number of biological systems, being one of the most diverse types of proteases and presenting a wide range of folds and catalytic metal ions. Given the increasing understanding of protein internal dynamics and its role in enzyme function, we are interested in assessing how the structural heterogeneity of metalloproteases translates into their dynamics. Therefore, the dynamical profile of the clan MA type protein thermolysin, derived from an Elastic Network Model of protein structure, was evaluated against those obtained from a set of experimental structures and molecular dynamics simulation trajectories. A close correspondence was obtained between modes derived from the coarse-grained model and the subspace of functionally-relevant motions observed experimentally, the later being shown to be encoded in the internal dynamics of the protein. This prompted the use of dynamics-based comparison methods that employ such coarse-grained models in a representative set of clan members, allowing for its quantitative description in terms of structural and dynamical variability. Although members show structural similarity, they nonetheless present distinct dynamical profiles, with no apparent correlation between structural and dynamical relatedness. However, previously unnoticed dynamical similarity was found between the relevant members Carboxypeptidase Pfu, Leishmanolysin, and Botulinum Neurotoxin Type A, despite sharing no structural similarity. Inspection of the respective alignments shows that dynamical similarity has a functional basis, namely the need for maintaining proper intermolecular interactions with the respective substrates. These results suggest that distinct selective pressure mechanisms act on metalloproteases at structural and dynamical levels through the course of their evolution. This work shows how new insights on metalloprotease function and evolution can be assessed with comparison schemes that incorporate information on protein dynamics. The integration of these newly developed tools, if applied to other protein families, can lead to more accurate and descriptive protein classification systems.
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Affiliation(s)
- Henrique F. Carvalho
- UCIBIO-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780–157 Oeiras, Portugal
| | - Ana C. A. Roque
- UCIBIO-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Olga Iranzo
- Aix Marseille Université, Centrale Marseille, CNRS, iSm2 UMR 7313, 13397, Marseille, France
| | - Ricardo J. F. Branco
- UCIBIO-REQUIMTE, Department of Chemistry, Faculty of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
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9
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Grosso M, Kalstein A, Parisi G, Roitberg AE, Fernandez-Alberti S. On the analysis and comparison of conformer-specific essential dynamics upon ligand binding to a protein. J Chem Phys 2015; 142:245101. [DOI: 10.1063/1.4922925] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Marcos Grosso
- Universidad Nacional de Quilmes, Roque Saenz Peña 352, B1876BXD Bernal, Argentina
| | - Adrian Kalstein
- Universidad Nacional de Quilmes, Roque Saenz Peña 352, B1876BXD Bernal, Argentina
| | - Gustavo Parisi
- Universidad Nacional de Quilmes, Roque Saenz Peña 352, B1876BXD Bernal, Argentina
| | - Adrian E. Roitberg
- Departments of Physics and Chemistry, University of Florida, Gainesville, Florida 32611, USA
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10
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Davis M, Tobi D. Multiple Gaussian network modes alignment reveals dynamically variable regions: The hemoglobin case. Proteins 2014; 82:2097-105. [DOI: 10.1002/prot.24565] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 02/26/2014] [Accepted: 03/19/2014] [Indexed: 12/17/2022]
Affiliation(s)
- Meir Davis
- Department of Computer Sciences and Mathematics; Ariel University; Ariel 40700 Israel
| | - Dror Tobi
- Department of Computer Sciences and Mathematics; Ariel University; Ariel 40700 Israel
- Department of Molecular Biology; Ariel University; Ariel 40700 Israel
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11
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12
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Marsh JA, Teichmann SA. Parallel dynamics and evolution: Protein conformational fluctuations and assembly reflect evolutionary changes in sequence and structure. Bioessays 2013; 36:209-18. [DOI: 10.1002/bies.201300134] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Joseph A. Marsh
- European Molecular Biology Laboratory; European Bioinformatics Institute; Wellcome Trust Genome Campus, Hinxton Cambridge UK
| | - Sarah A. Teichmann
- European Molecular Biology Laboratory; European Bioinformatics Institute; Wellcome Trust Genome Campus, Hinxton Cambridge UK
- Wellcome Trust Sanger Institute; Wellcome Trust Genome Campus; Hinxton Cambridge UK
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13
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Tobi D. Large-scale analysis of the dynamics of enzymes. Proteins 2013; 81:1910-8. [PMID: 23737241 DOI: 10.1002/prot.24335] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 05/21/2013] [Accepted: 05/24/2013] [Indexed: 12/26/2022]
Abstract
Protein enzymes enable the cell to execute chemical reactions in short time by accelerating the rate of the reactions in a selective manner. The motions or dynamics of the enzymes are essential for their function. Comparison of the dynamics of a set of 1247 nonhomologous enzymes was performed. For each enzyme, the slowest modes of motion are calculated using the Gaussian network model (GNM) and they are globally aligned. Alignment is done using the dynamic programming algorithm of Needleman and Wunsch, commonly used for sequence alignment. Only 96 pairs of proteins were identified to have three similar GNM slow modes with 63 of them having a similar structure. The most frequent slowest mode of motion describes a two domains anticorrelated motion that characterizes at least 23% of the enzymes. Therefore, dynamics uniqueness cannot be accounted for by the slowest mode itself but rather by the combination of several slow modes. Different quaternary structure packing can restrain the motion of enzyme subunits differently and may serve as another mechanism that increases the dynamics uniqueness.
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Affiliation(s)
- Dror Tobi
- Department of Computer Sciences and Mathematics, Ariel University, Ariel, 40700, Israel; Department of Molecular Biology, Ariel University, Ariel, 40700, Israel
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14
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A mechanistic understanding of allosteric immune escape pathways in the HIV-1 envelope glycoprotein. PLoS Comput Biol 2013; 9:e1003046. [PMID: 23696718 PMCID: PMC3656115 DOI: 10.1371/journal.pcbi.1003046] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 03/15/2013] [Indexed: 11/19/2022] Open
Abstract
The HIV-1 envelope (Env) spike, which consists of a compact, heterodimeric trimer of the glycoproteins gp120 and gp41, is the target of neutralizing antibodies. However, the high mutation rate of HIV-1 and plasticity of Env facilitates viral evasion from neutralizing antibodies through various mechanisms. Mutations that are distant from the antibody binding site can lead to escape, probably by changing the conformation or dynamics of Env; however, these changes are difficult to identify and define mechanistically. Here we describe a network analysis-based approach to identify potential allosteric immune evasion mechanisms using three known HIV-1 Env gp120 protein structures from two different clades, B and C. First, correlation and principal component analyses of molecular dynamics (MD) simulations identified a high degree of long-distance coupled motions that exist between functionally distant regions within the intrinsic dynamics of the gp120 core, supporting the presence of long-distance communication in the protein. Then, by integrating MD simulations with network theory, we identified the optimal and suboptimal communication pathways and modules within the gp120 core. The results unveil both strain-dependent and -independent characteristics of the communication pathways in gp120. We show that within the context of three structurally homologous gp120 cores, the optimal pathway for communication is sequence sensitive, i.e. a suboptimal pathway in one strain becomes the optimal pathway in another strain. Yet the identification of conserved elements within these communication pathways, termed inter-modular hotspots, could present a new opportunity for immunogen design, as this could be an additional mechanism that HIV-1 uses to shield vulnerable antibody targets in Env that induce neutralizing antibody breadth.
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15
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Biggin PC. Protein dynamics--a moving target: comment on "Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments" by C. Micheletti. Phys Life Rev 2012; 10:27-8; discussion 39-40. [PMID: 23122425 DOI: 10.1016/j.plrev.2012.10.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2012] [Accepted: 10/19/2012] [Indexed: 10/27/2022]
Affiliation(s)
- Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, England, OX1 3QU, United Kingdom.
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16
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The role of flexibility and conformational selection in the binding promiscuity of PDZ domains. PLoS Comput Biol 2012; 8:e1002749. [PMID: 23133356 PMCID: PMC3486844 DOI: 10.1371/journal.pcbi.1002749] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Accepted: 09/02/2012] [Indexed: 11/23/2022] Open
Abstract
In molecular recognition, it is often the case that ligand binding is coupled to conformational change in one or both of the binding partners. Two hypotheses describe the limiting cases involved; the first is the induced fit and the second is the conformational selection model. The conformational selection model requires that the protein adopts conformations that are similar to the ligand-bound conformation in the absence of ligand, whilst the induced-fit model predicts that the ligand-bound conformation of the protein is only accessible when the ligand is actually bound. The flexibility of the apo protein clearly plays a major role in these interpretations. For many proteins involved in signaling pathways there is the added complication that they are often promiscuous in that they are capable of binding to different ligand partners. The relationship between protein flexibility and promiscuity is an area of active research and is perhaps best exemplified by the PDZ domain family of proteins. In this study we use molecular dynamics simulations to examine the relationship between flexibility and promiscuity in five PDZ domains: the human Dvl2 (Dishevelled-2) PDZ domain, the human Erbin PDZ domain, the PDZ1 domain of InaD (inactivation no after-potential D protein) from fruit fly, the PDZ7 domain of GRIP1 (glutamate receptor interacting protein 1) from rat and the PDZ2 domain of PTP-BL (protein tyrosine phosphatase) from mouse. We show that despite their high structural similarity, the PDZ binding sites have significantly different dynamics. Importantly, the degree of binding pocket flexibility was found to be closely related to the various characteristics of peptide binding specificity and promiscuity of the five PDZ domains. Our findings suggest that the intrinsic motions of the apo structures play a key role in distinguishing functional properties of different PDZ domains and allow us to make predictions that can be experimentally tested. Proteins that are capable of binding to many different ligands are said to have broad specificity. This is sometimes also referred to as promiscuity. Whether a protein is promiscuous or not can sometimes be readily explained by the structure of the protein and the ligand in terms of electrostatic and steric effects. Sometimes however, this simple interpretation can struggle to explain the experimentally observed data. A prominent case in point is the PDZ domains. These small protein domains bind to unstructured regions of other proteins and are involved in many signaling pathways. Some PDZ domains appear to be more promiscuous than others, but this has been difficult to explain purely on the basis of the composition of residues in the binding groove. In this work we examine the dynamics and conformational flexibility of five key PDZ domains and demonstrate that despite similar folds, these proteins can exhibit quite different dynamics. Furthermore the difference in the dynamic behavior appears to correlate with the observed promiscuity. Our findings suggest that knowledge of the dynamic behavior of the PDZs can be used to rationalize the extent of expected promiscuity. Such knowledge will be critical for drug design against PDZ domains.
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17
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Micheletti C. Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments. Phys Life Rev 2012. [PMID: 23199577 DOI: 10.1016/j.plrev.2012.10.009] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The growing interest for comparing protein internal dynamics owes much to the realisation that protein function can be accompanied or assisted by structural fluctuations and conformational changes. Analogously to the case of functional structural elements, those aspects of protein flexibility and dynamics that are functionally oriented should be subject to evolutionary conservation. Accordingly, dynamics-based protein comparisons or alignments could be used to detect protein relationships that are more elusive to sequence and structural alignments. Here we provide an account of the progress that has been made in recent years towards developing and applying general methods for comparing proteins in terms of their internal dynamics and advance the understanding of the structure-function relationship.
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Affiliation(s)
- Cristian Micheletti
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, Trieste, Italy.
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18
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Fuglebakk E, Echave J, Reuter N. Measuring and comparing structural fluctuation patterns in large protein datasets. ACTA ACUST UNITED AC 2012; 28:2431-40. [PMID: 22796957 DOI: 10.1093/bioinformatics/bts445] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
MOTIVATION The function of a protein depends not only on its structure but also on its dynamics. This is at the basis of a large body of experimental and theoretical work on protein dynamics. Further insight into the dynamics-function relationship can be gained by studying the evolutionary divergence of protein motions. To investigate this, we need appropriate comparative dynamics methods. The most used dynamical similarity score is the correlation between the root mean square fluctuations (RMSF) of aligned residues. Despite its usefulness, RMSF is in general less evolutionarily conserved than the native structure. A fundamental issue is whether RMSF is not as conserved as structure because dynamics is less conserved or because RMSF is not the best property to use to study its conservation. RESULTS We performed a systematic assessment of several scores that quantify the (dis)similarity between protein fluctuation patterns. We show that the best scores perform as well as or better than structural dissimilarity, as assessed by their consistency with the SCOP classification. We conclude that to uncover the full extent of the evolutionary conservation of protein fluctuation patterns, it is important to measure the directions of fluctuations and their correlations between sites. CONTACT Nathalie.Reuter@mbi.uib.no SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics Online.
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Affiliation(s)
- Edvin Fuglebakk
- Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway
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19
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Abstract
Proteins fluctuate, and such fluctuations are functionally important. As with any functionally relevant trait, it is interesting to study how fluctuations change during evolution. In contrast with sequence and structure, the study of the evolution of protein motions is much more recent. Yet, it has been shown that the overall fluctuation pattern is evolutionarily conserved. Moreover, the lowest-energy normal modes have been found to be the most conserved. The reasons behind such a differential conservation have not been explicitly studied. There are two limiting explanations. A “biological” explanation is that because such modes are functional, there is natural selection pressure against their variation. An alternative “physical” explanation is that the lowest-energy normal modes may be more conserved because they are just more robust with respect to random mutations. To investigate this issue, I studied a set of globin-like proteins using a perturbed elastic network model (ENM) of the effect of random mutations on normal modes. I show that the conservation predicted by the model is in excellent agreement with observations. These results support the physical explanation: the lowest normal modes are more conserved because they are more robust.
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Liberles DA, Teichmann SA, Bahar I, Bastolla U, Bloom J, Bornberg-Bauer E, Colwell LJ, de Koning APJ, Dokholyan NV, Echave J, Elofsson A, Gerloff DL, Goldstein RA, Grahnen JA, Holder MT, Lakner C, Lartillot N, Lovell SC, Naylor G, Perica T, Pollock DD, Pupko T, Regan L, Roger A, Rubinstein N, Shakhnovich E, Sjölander K, Sunyaev S, Teufel AI, Thorne JL, Thornton JW, Weinreich DM, Whelan S. The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci 2012; 21:769-85. [PMID: 22528593 PMCID: PMC3403413 DOI: 10.1002/pro.2071] [Citation(s) in RCA: 149] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Revised: 03/22/2012] [Accepted: 03/23/2012] [Indexed: 12/20/2022]
Abstract
Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.
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Affiliation(s)
- David A Liberles
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Sarah A Teichmann
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of PittsburghPittsburgh, Pennsylvania 15213
| | - Ugo Bastolla
- Bioinformatics Unit. Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Universidad Autonoma de Madrid28049 Cantoblanco Madrid, Spain
| | - Jesse Bloom
- Division of Basic Sciences, Fred Hutchinson Cancer Research CenterSeattle, Washington 98109
| | - Erich Bornberg-Bauer
- Evolutionary Bioinformatics Group, Institute for Evolution and Biodiversity, University of MuensterGermany
| | - Lucy J Colwell
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - A P Jason de Koning
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of ColoradoAurora, Colorado
| | - Nikolay V Dokholyan
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel HillNorth Carolina 27599
| | - Julian Echave
- Escuela de Ciencia y Tecnología, Universidad Nacional de San MartínMartín de Irigoyen 3100, 1650 San Martín, Buenos Aires, Argentina
| | - Arne Elofsson
- Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, Science for Life Laboratory, Swedish E-science Research Center, Stockholm University106 91 Stockholm, Sweden
| | - Dietlind L Gerloff
- Biomolecular Engineering Department, University of CaliforniaSanta Cruz, California 95064
| | - Richard A Goldstein
- Division of Mathematical Biology, National Institute for Medical Research (MRC)Mill Hill, London NW7 1AA, United Kingdom
| | - Johan A Grahnen
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Mark T Holder
- Department of Ecology and Evolutionary Biology, University of KansasLawrence, Kansas 66045
| | - Clemens Lakner
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, North Carolina 27695
| | - Nicholas Lartillot
- Département de Biochimie, Faculté de Médecine, Université de MontréalMontréal, QC H3T1J4, Canada
| | - Simon C Lovell
- Faculty of Life Sciences, University of ManchesterManchester M13 9PT, United Kingdom
| | - Gavin Naylor
- Department of Biology, College of CharlestonCharleston, South Carolina 29424
| | - Tina Perica
- MRC Laboratory of Molecular BiologyHills Road, Cambridge CB2 0QH, United Kingdom
| | - David D Pollock
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of ColoradoAurora, Colorado
| | - Tal Pupko
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityTel Aviv, Israel
| | - Lynne Regan
- Department of Molecular Biophysics and Biochemistry, Yale UniversityNew Haven 06511
| | - Andrew Roger
- Department of Biochemistry and Molecular Biology, Dalhousie UniversityHalifax, NS, Canada
| | - Nimrod Rubinstein
- Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityTel Aviv, Israel
| | - Eugene Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard UniversityCambridge, Massachusetts 02138
| | - Kimmen Sjölander
- Department of Bioengineering, University of CaliforniaBerkeley, Berkeley, California 94720
| | - Shamil Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School77 Avenue Louis Pasteur, Boston, Massachusetts 02115
| | - Ashley I Teufel
- Department of Molecular Biology, University of WyomingLaramie, Wyoming 82071
| | - Jeffrey L Thorne
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, North Carolina 27695
| | - Joseph W Thornton
- Howard Hughes Medical Institute and Institute for Ecology and Evolution, University of OregonEugene, Oregon 97403
- Department of Human Genetics, University of ChicagoChicago, Illinois 60637
- Department of Ecology and Evolution, University of ChicagoChicago, Illinois 60637
| | - Daniel M Weinreich
- Department of Ecology and Evolutionary Biology, and Center for Computational Molecular Biology, Brown UniversityProvidence, Rhode Island 02912
| | - Simon Whelan
- Faculty of Life Sciences, University of ManchesterManchester M13 9PT, United Kingdom
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Hensen U, Meyer T, Haas J, Rex R, Vriend G, Grubmüller H. Exploring protein dynamics space: the dynasome as the missing link between protein structure and function. PLoS One 2012; 7:e33931. [PMID: 22606222 PMCID: PMC3350514 DOI: 10.1371/journal.pone.0033931] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2011] [Accepted: 02/20/2012] [Indexed: 12/25/2022] Open
Abstract
Proteins are usually described and classified according to amino acid sequence, structure or function. Here, we develop a minimally biased scheme to compare and classify proteins according to their internal mobility patterns. This approach is based on the notion that proteins not only fold into recurring structural motifs but might also be carrying out only a limited set of recurring mobility motifs. The complete set of these patterns, which we tentatively call the dynasome, spans a multi-dimensional space with axes, the dynasome descriptors, characterizing different aspects of protein dynamics. The unique dynamic fingerprint of each protein is represented as a vector in the dynasome space. The difference between any two vectors, consequently, gives a reliable measure of the difference between the corresponding protein dynamics. We characterize the properties of the dynasome by comparing the dynamics fingerprints obtained from molecular dynamics simulations of 112 proteins but our approach is, in principle, not restricted to any specific source of data of protein dynamics. We conclude that: 1. the dynasome consists of a continuum of proteins, rather than well separated classes. 2. For the majority of proteins we observe strong correlations between structure and dynamics. 3. Proteins with similar function carry out similar dynamics, which suggests a new method to improve protein function annotation based on protein dynamics.
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Affiliation(s)
- Ulf Hensen
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Tim Meyer
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Jürgen Haas
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - René Rex
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Gert Vriend
- CMBI, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Helmut Grubmüller
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
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