1
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Li Y, Arcos S, Sabsay KR, te Velthuis AJW, Lauring AS. Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein. J Virol 2023; 97:e0132923. [PMID: 37882522 PMCID: PMC10688322 DOI: 10.1128/jvi.01329-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
IMPORTANCE The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus' natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.
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Affiliation(s)
- Yuan Li
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sarah Arcos
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kimberly R. Sabsay
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | | | - Adam S. Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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2
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Yu CC, Raj N, Chu JW. Statistical Learning of Protein Elastic Network from Positional Covariance Matrix. Comput Struct Biotechnol J 2023; 21:2524-2535. [PMID: 37095762 PMCID: PMC10121796 DOI: 10.1016/j.csbj.2023.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/20/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.
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3
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In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.
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4
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Pramudwiatmoko A, Gutmann G, Ueno Y, Kakugo A, Yamamura M, Konagaya A. Tensegrity representation of microtubule objects using unified particle objects and springs. CHEM-BIO INFORMATICS JOURNAL 2020. [DOI: 10.1273/cbij.20.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Arif Pramudwiatmoko
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
- Universitas Teknologi Yogyakarta
| | - Gregory Gutmann
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
| | - Yutaka Ueno
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology
| | - Akira Kakugo
- Faculty of Science, Hokkaido University
- Graduate School of Chemical Sciences and Engineering, Hokkaido University
| | - Masayuki Yamamura
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
| | - Akihiko Konagaya
- School of Computing, Department of Computer Science, Tokyo Institute of Technology
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5
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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6
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Wienen-Schmidt B, Jonker HRA, Wulsdorf T, Gerber HD, Saxena K, Kudlinzki D, Sreeramulu S, Parigi G, Luchinat C, Heine A, Schwalbe H, Klebe G. Paradoxically, Most Flexible Ligand Binds Most Entropy-Favored: Intriguing Impact of Ligand Flexibility and Solvation on Drug–Kinase Binding. J Med Chem 2018; 61:5922-5933. [DOI: 10.1021/acs.jmedchem.8b00105] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Barbara Wienen-Schmidt
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany
| | - Hendrik R. A. Jonker
- Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität Frankfurt, Max-von-Laue-Straße 7, N160-3.14, 60438 Frankfurt am Main, Germany
| | - Tobias Wulsdorf
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany
| | - Hans-Dieter Gerber
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany
| | - Krishna Saxena
- Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität Frankfurt, Max-von-Laue-Straße 7, N160-3.14, 60438 Frankfurt am Main, Germany
| | - Denis Kudlinzki
- Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität Frankfurt, Max-von-Laue-Straße 7, N160-3.14, 60438 Frankfurt am Main, Germany
| | - Sridhar Sreeramulu
- Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität Frankfurt, Max-von-Laue-Straße 7, N160-3.14, 60438 Frankfurt am Main, Germany
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM/CIRMMP) and Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM/CIRMMP) and Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Italy
| | - Andreas Heine
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany
| | - Harald Schwalbe
- Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität Frankfurt, Max-von-Laue-Straße 7, N160-3.14, 60438 Frankfurt am Main, Germany
| | - Gerhard Klebe
- Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany
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7
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Tan X, Wang B. Inward open characterization of EmrD transporter with molecular dynamics simulation. Biochem Biophys Res Commun 2016; 474:640-645. [PMID: 27055595 DOI: 10.1016/j.bbrc.2016.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 04/03/2016] [Indexed: 10/22/2022]
Abstract
EmrD is a member of the multidrug resistance exporter family. Up to now, little is known about the structural dynamics that underline the function of the EmrD protein in inward-facing open state and how the EmrD transits from an occluded state to an inward open state. For the first time the article applied the AT simulation to investigate the membrane transporter protein EmrD, and described the dynamic features of the whole protein, the domain, the helices, and the amino acid residues during an inward-open process from its occluded state. The gradual inward-open process is different from the current model of rigid-body domain motion in alternating-access mechanism. Simulation results show that the EmrD inward-open conformational fluctuation propagates from a C-terminal domain to an N-terminal domain via the linker region during the transition from its occluded state. The conformational fluctuation of the C-terminal domain is larger than that of the N-terminal domain. In addition, it is observed that the helices exposed to the surrounding membrane show a higher level of flexibility than the other regions, and the protonated E227 plays a key role in the transition from the occluded to the open state.
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Affiliation(s)
- Xianwei Tan
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Boxiong Wang
- Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
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8
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Tiwari SP, Reuter N. Similarity in Shape Dictates Signature Intrinsic Dynamics Despite No Functional Conservation in TIM Barrel Enzymes. PLoS Comput Biol 2016; 12:e1004834. [PMID: 27015412 PMCID: PMC4807811 DOI: 10.1371/journal.pcbi.1004834] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Accepted: 02/25/2016] [Indexed: 11/19/2022] Open
Abstract
The conservation of the intrinsic dynamics of proteins emerges as we attempt to understand the relationship between sequence, structure and functional conservation. We characterise the conservation of such dynamics in a case where the structure is conserved but function differs greatly. The triosephosphate isomerase barrel fold (TBF), renowned for its 8 β-strand-α-helix repeats that close to form a barrel, is one of the most diverse and abundant folds found in known protein structures. Proteins with this fold have diverse enzymatic functions spanning five of six Enzyme Commission classes, and we have picked five different superfamily candidates for our analysis using elastic network models. We find that the overall shape is a large determinant in the similarity of the intrinsic dynamics, regardless of function. In particular, the β-barrel core is highly rigid, while the α-helices that flank the β-strands have greater relative mobility, allowing for the many possibilities for placement of catalytic residues. We find that these elements correlate with each other via the loops that link them, as opposed to being directly correlated. We are also able to analyse the types of motions encoded by the normal mode vectors of the α-helices. We suggest that the global conservation of the intrinsic dynamics in the TBF contributes greatly to its success as an enzymatic scaffold both through evolution and enzyme design.
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Affiliation(s)
- Sandhya P. Tiwari
- Department of Molecular Biology, University of Bergen, Pb. 7803, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, Bergen, Norway
| | - Nathalie Reuter
- Department of Molecular Biology, University of Bergen, Pb. 7803, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, Bergen, Norway
- * E-mail:
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9
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Sfriso P, Duran-Frigola M, Mosca R, Emperador A, Aloy P, Orozco M. Residues Coevolution Guides the Systematic Identification of Alternative Functional Conformations in Proteins. Structure 2016; 24:116-126. [DOI: 10.1016/j.str.2015.10.025] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 10/13/2015] [Accepted: 10/17/2015] [Indexed: 12/12/2022]
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10
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Orellana L, Rueda M, Ferrer-Costa C, Lopez-Blanco JR, Chacón P, Orozco M. Approaching Elastic Network Models to Molecular Dynamics Flexibility. J Chem Theory Comput 2015; 6:2910-23. [PMID: 26616090 DOI: 10.1021/ct100208e] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Elastic network models (ENMs) are coarse-grained descriptions of proteins as networks of coupled harmonic oscillators. However, despite their widespread application to study collective movements, there is still no consensus parametrization for the ENMs. When compared to molecular dynamics (MD) flexibility in solution, the ENMs tend to disperse the important motions into multiple modes. We present here a new ENM, trained against a database of atomistic MD trajectories. The role of residue connectivity, the analytical form of the force constants, and the threshold for interactions were systematically explored. We found that contacts between the three nearest sequence neighbors are crucial determinants of the fundamental motions. We developed a new general potential function including both the sequential and spatial relationships between interacting residue pairs which is robust against size and fold variations. The proposed model provides a systematic improvement compared to standard ENMs: Not only do its results match the MD results-even for long time scales-but also the model is able to capture large X-ray conformational transitions as well as NMR ensemble diversity.
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Affiliation(s)
- Laura Orellana
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
| | - Manuel Rueda
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
| | - Carles Ferrer-Costa
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
| | - José Ramón Lopez-Blanco
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
| | - Pablo Chacón
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
| | - Modesto Orozco
- Joint Research Program in Computational Biology from the Institute for Research in Biomedicine Barcelona (IRBB) and Barcelona Supercomputing Center (BSC), Barcelona, Spain, Chemical and Physical Biology, Centro de Investigaciones Biológicas, Madrid, Spain, Departament de Bioquímica i Biologia Molecular, Universitat de Barcelona, Barcelona, Spain, and Skaggs School of Pharmacy, University of California-San Diego, La Jolla, California 92093
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11
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Hospital A, Goñi JR, Orozco M, Gelpí JL. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 2015; 8:37-47. [PMID: 26604800 PMCID: PMC4655909 DOI: 10.2147/aabc.s70333] [Citation(s) in RCA: 233] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.
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Affiliation(s)
- Adam Hospital
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, University of Barcelona, Barcelona, Spain
| | - Josep Ramon Goñi
- Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, University of Barcelona, Barcelona, Spain ; Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain ; Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, Spain
| | - Josep L Gelpí
- Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain ; Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, Spain
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12
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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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13
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Butler BM, Gerek ZN, Kumar S, Ozkan SB. Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association. Proteins 2015; 83:428-35. [PMID: 25546381 DOI: 10.1002/prot.24748] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 11/20/2014] [Accepted: 12/10/2014] [Indexed: 12/12/2022]
Abstract
Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease-associated non-synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site-specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non-interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease-associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome-wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease.
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14
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Tiwari SP, Fuglebakk E, Hollup SM, Skjærven L, Cragnolini T, Grindhaug SH, Tekle KM, Reuter N. WEBnm@ v2.0: Web server and services for comparing protein flexibility. BMC Bioinformatics 2014; 15:427. [PMID: 25547242 PMCID: PMC4339738 DOI: 10.1186/s12859-014-0427-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2014] [Accepted: 12/11/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Normal mode analysis (NMA) using elastic network models is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. Further insight into the dynamics-function relationship can be gained by comparing protein motions between protein homologs and functional classifications. This can be achieved by comparing normal modes obtained from sets of evolutionary related proteins. RESULTS We have developed an automated tool for comparative NMA of a set of pre-aligned protein structures. The user can submit a sequence alignment in the FASTA format and the corresponding coordinate files in the Protein Data Bank (PDB) format. The computed normalised squared atomic fluctuations and atomic deformation energies of the submitted structures can be easily compared on graphs provided by the web user interface. The web server provides pairwise comparison of the dynamics of all proteins included in the submitted set using two measures: the Root Mean Squared Inner Product and the Bhattacharyya Coefficient. The Comparative Analysis has been implemented on our web server for NMA, WEBnm@, which also provides recently upgraded functionality for NMA of single protein structures. This includes new visualisations of protein motion, visualisation of inter-residue correlations and the analysis of conformational change using the overlap analysis. In addition, programmatic access to WEBnm@ is now available through a SOAP-based web service. Webnm@ is available at http://apps.cbu.uib.no/webnma . CONCLUSION WEBnm@ v2.0 is an online tool offering unique capability for comparative NMA on multiple protein structures. Along with a convenient web interface, powerful computing resources, and several methods for mode analyses, WEBnm@ facilitates the assessment of protein flexibility within protein families and superfamilies. These analyses can give a good view of how the structures move and how the flexibility is conserved over the different structures.
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Affiliation(s)
- Sandhya P Tiwari
- Department of Molecular Biology, University of Bergen, Bergen, Norway.
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Edvin Fuglebakk
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Siv M Hollup
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Lars Skjærven
- Department of Biomedicine, University of Bergen, Bergen, Norway.
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Tristan Cragnolini
- Department of Molecular Biology, University of Bergen, Bergen, Norway.
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
- Present address: University Chemical Laboratories, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | - Svenn H Grindhaug
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Kidane M Tekle
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
| | - Nathalie Reuter
- Department of Molecular Biology, University of Bergen, Bergen, Norway.
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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15
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On the modularity of the intrinsic flexibility of the µ opioid receptor: a computational study. PLoS One 2014; 9:e115856. [PMID: 25549261 PMCID: PMC4280117 DOI: 10.1371/journal.pone.0115856] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 12/01/2014] [Indexed: 11/19/2022] Open
Abstract
The µ opioid receptor (µOR), the principal target to control pain, belongs to the G protein-coupled receptors (GPCRs) family, one of the most highlighted protein families due to their importance as therapeutic targets. The conformational flexibility of GPCRs is one of their essential characteristics as they take part in ligand recognition and subsequent activation or inactivation mechanisms. It is assessed that the intrinsic mechanical properties of the µOR, more specifically its particular flexibility behavior, would facilitate the accomplishment of specific biological functions, at least in their first steps, even in the absence of a ligand or any chemical species usually present in its biological environment. The study of the mechanical properties of the µOR would thus bring some indications regarding the highly efficient ability of the µOR to transduce cellular message. We therefore investigate the intrinsic flexibility of the µOR in its apo-form using all-atom Molecular Dynamics simulations at the sub-microsecond time scale. We particularly consider the µOR embedded in a simplified membrane model without specific ions, particular lipids, such as cholesterol moieties, or any other chemical species that could affect the flexibility of the µOR. Our analyses highlighted an important local effect due to the various bendability of the helices resulting in a diversity of shape and volume sizes adopted by the µOR binding site. Such property explains why the µOR can interact with ligands presenting highly diverse structural geometry. By investigating the topology of the µOR binding site, a conformational global effect is depicted: the correlation between the motional modes of the extra- and intracellular parts of µOR on one hand, along with a clear rigidity of the central µOR domain on the other hand. Our results show how the modularity of the µOR flexibility is related to its pre-ability to activate and to present a basal activity.
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16
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Fuglebakk E, Tiwari SP, Reuter N. Comparing the intrinsic dynamics of multiple protein structures using elastic network models. Biochim Biophys Acta Gen Subj 2014; 1850:911-922. [PMID: 25267310 DOI: 10.1016/j.bbagen.2014.09.021] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 09/15/2014] [Accepted: 09/16/2014] [Indexed: 12/15/2022]
Abstract
BACKGROUND Elastic network models (ENMs) are based on the simple idea that a protein can be described as a set of particles connected by springs, which can then be used to describe its intrinsic flexibility using, for example, normal mode analysis. Since the introduction of the first ENM by Monique Tirion in 1996, several variants using coarser protein models have been proposed and their reliability for the description of protein intrinsic dynamics has been widely demonstrated. Lately an increasing number of studies have focused on the meaning of slow dynamics for protein function and its potential conservation through evolution. This leads naturally to comparisons of the intrinsic dynamics of multiple protein structures with varying levels of similarity. SCOPE OF REVIEW We describe computational strategies for calculating and comparing intrinsic dynamics of multiple proteins using elastic network models, as well as a selection of examples from the recent literature. MAJOR CONCLUSIONS The increasing interest for comparing dynamics across protein structures with various levels of similarity, has led to the establishment and validation of reliable computational strategies using ENMs. Comparing dynamics has been shown to be a viable way for gaining greater understanding for the mechanisms employed by proteins for their function. Choices of ENM parameters, structure alignment or similarity measures will likely influence the interpretation of the comparative analysis of protein motion. GENERAL SIGNIFICANCE Understanding the relation between protein function and dynamics is relevant to the fundamental understanding of protein structure-dynamics-function relationship. This article is part of a Special Issue entitled Recent developments of molecular dynamics.
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Affiliation(s)
- Edvin Fuglebakk
- Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.
| | - Sandhya P Tiwari
- Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.
| | - Nathalie Reuter
- Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway.
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17
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18
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Abstract
Proteins are fascinating supramolecular structures, which are able to recognize ligands transforming binding information into chemical signals. They can transfer information across the cell, can catalyse complex chemical reactions, and are able to transform energy into work with much more efficiency than any human engine. The unique abilities of proteins are tightly coupled with their dynamic properties, which are coded in a complex way in the sequence and carefully refined by evolution. Despite its importance, our experimental knowledge of protein dynamics is still rather limited, and mostly derived from theoretical calculations. I will review here, in a systematic way, the current state-of-the-art theoretical approaches to the study of protein dynamics, emphasizing the most recent advances, examples of use and the expected lines of development in the near future.
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Affiliation(s)
- Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Baldiri i Reixac 8, Barcelona 08028, Spain.
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19
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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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20
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Sfriso P, Hospital A, Emperador A, Orozco M. Exploration of conformational transition pathways from coarse-grained simulations. ACTA ACUST UNITED AC 2013; 29:1980-6. [PMID: 23740746 DOI: 10.1093/bioinformatics/btt324] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
MOTIVATION A new algorithm to trace conformational transitions in proteins is presented. The method uses discrete molecular dynamics as engine to sample protein conformational space. A multiple minima Go-like potential energy function is used in combination with several enhancing sampling strategies, such as metadynamics, Maxwell Demon molecular dynamics and essential dynamics. The method, which shows an unprecedented computational efficiency, is able to trace a wide range of known experimental transitions. Contrary to simpler methods our strategy does not introduce distortions in the chemical structure of the protein and is able to reproduce well complex non-linear conformational transitions. The method, called GOdMD, can easily introduce additional restraints to the transition (presence of ligand, known intermediate, known maintained contacts, …) and is freely distributed to the community through the Spanish National Bioinformatics Institute (http://mmb.irbbarcelona.org/GOdMD). AVAILABILITY Freely available on the web at http://mmb.irbbarcelona.org/GOdMD.
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Affiliation(s)
- Pedro Sfriso
- Institute for Research in Biomedicine (IRB Barcelona), Joint IRB-BSC Program in Computational Biology, Baldiri Reixac 10, Barcelona, Spain
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21
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22
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Nevin Gerek Z, Kumar S, Banu Ozkan S. Structural dynamics flexibility informs function and evolution at a proteome scale. Evol Appl 2013; 6:423-33. [PMID: 23745135 PMCID: PMC3673471 DOI: 10.1111/eva.12052] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 01/13/2013] [Indexed: 01/04/2023] Open
Abstract
Protein structures are dynamic entities with a myriad of atomic fluctuations, side-chain rotations, and collective domain movements. Although the importance of these dynamics to proper functioning of proteins is emerging in the studies of many protein families, there is a lack of broad evidence for the critical role of protein dynamics in shaping the biological functions of a substantial fraction of residues for a large number of proteins in the human proteome. Here, we propose a novel dynamic flexibility index (dfi) to quantify the dynamic properties of individual residues in any protein and use it to assess the importance of protein dynamics in 100 human proteins. Our analyses involving functionally critical positions, disease-associated and putatively neutral population variations, and the rate of interspecific substitutions per residue produce concordant patterns at a proteome scale. They establish that the preservation of dynamic properties of residues in a protein structure is critical for maintaining the protein/biological function. Therefore, structural dynamics needs to become a major component of the analysis of protein function and evolution. Such analyses will be facilitated by the dfi, which will also enable the integrative use of structural dynamics with evolutionary conservation in genomic medicine as well as functional genomics investigations.
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Affiliation(s)
- Zeynep Nevin Gerek
- Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University Tempe, AZ, USA ; Department of Physics, Center for Biological Physics, Bateman Physical Sciences F-Wing, Arizona State University Tempe, AZ, USA
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23
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Using simulations to provide the framework for experimental protein folding studies. Arch Biochem Biophys 2012; 531:128-35. [PMID: 23266569 DOI: 10.1016/j.abb.2012.12.015] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Revised: 12/10/2012] [Accepted: 12/14/2012] [Indexed: 12/27/2022]
Abstract
Molecular dynamics simulations are a powerful theoretical tool to model the protein folding process in atomistic details under realistic conditions. Combined with a number of experimental techniques, simulations provide a detailed picture of how a protein folds or unfolds in the presence of explicit solvent and other molecular species, such as cosolvents, osmolytes, cofactors, active binding partners or inert crowding agents. The denaturing effects of temperature, pressure and external mechanical forces can also be probed. Qualitative and quantitative agreement with experiment contributes to a comprehensive molecular picture of protein states along the folding/unfolding pathway. The variety of systems examined reveals key features of the protein folding process.
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24
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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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25
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Sfriso P, Emperador A, Orellana L, Hospital A, Gelpí JL, Orozco M. Finding Conformational Transition Pathways from Discrete Molecular Dynamics Simulations. J Chem Theory Comput 2012; 8:4707-18. [DOI: 10.1021/ct300494q] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Pedro Sfriso
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
| | - Agusti Emperador
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
| | - Laura Orellana
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
| | - Adam Hospital
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
- Structural Bioinformatics Node,
Instituto Nacional De Bioinformática, Institute of Research
in Biomedicine, Josep Samitier 1-5, Barcelona, 08028, Spain
| | - Josep Lluis Gelpí
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
- Computational Bioinformatics Node,
Instituto Nacional De Bioinformática, Barcelona Supercomputing
Center, Jordi Girona 29, Barcelona, 08034, Spain
- Departament de Bioquímica,
Facultat de Biologia, Universtitat de Barcelona, Avgda Diagonal 647,
Barcelona, 08028, Spain
| | - Modesto Orozco
- Joint IRB-BSC Program in Computational
Biology, Institute of Research in Biomedicine, Josep Samitier 1-5,
Barcelona, 08028, Spain
- Structural Bioinformatics Node,
Instituto Nacional De Bioinformática, Institute of Research
in Biomedicine, Josep Samitier 1-5, Barcelona, 08028, Spain
- Departament de Bioquímica,
Facultat de Biologia, Universtitat de Barcelona, Avgda Diagonal 647,
Barcelona, 08028, Spain
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26
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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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27
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Tobi D. Dynamics alignment: Comparison of protein dynamics in the scop database. Proteins 2012; 80:1167-76. [DOI: 10.1002/prot.24017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2011] [Revised: 11/27/2011] [Accepted: 12/13/2011] [Indexed: 11/07/2022]
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28
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Fraccalvieri D, Pandini A, Stella F, Bonati L. Conformational and functional analysis of molecular dynamics trajectories by self-organising maps. BMC Bioinformatics 2011; 12:158. [PMID: 21569575 PMCID: PMC3118354 DOI: 10.1186/1471-2105-12-158] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2010] [Accepted: 05/14/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering. RESULTS The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions. CONCLUSIONS The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.
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Affiliation(s)
- Domenico Fraccalvieri
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università degli Studi di Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
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29
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Abstract
Summary: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. Availability:ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/. Contact:ahb12@pitt.edu; bahar@pitt.edu
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Affiliation(s)
- Ahmet Bakan
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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30
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Meyer T, D'Abramo M, Hospital A, Rueda M, Ferrer-Costa C, Pérez A, Carrillo O, Camps J, Fenollosa C, Repchevsky D, Gelpí JL, Orozco M. MoDEL (Molecular Dynamics Extended Library): A Database of Atomistic Molecular Dynamics Trajectories. Structure 2010; 18:1399-409. [DOI: 10.1016/j.str.2010.07.013] [Citation(s) in RCA: 108] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2010] [Revised: 07/19/2010] [Accepted: 07/27/2010] [Indexed: 11/26/2022]
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31
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Raimondi F, Orozco M, Fanelli F. Deciphering the deformation modes associated with function retention and specialization in members of the Ras superfamily. Structure 2010; 18:402-14. [PMID: 20223222 DOI: 10.1016/j.str.2009.12.015] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2009] [Revised: 12/07/2009] [Accepted: 12/29/2009] [Indexed: 10/19/2022]
Abstract
The evolutionary and physical deformability patterns of members of the Ras GTPase superfamily were investigated by Principal Component and Elastic Network-Normal Mode analyses. The study helped to decipher the dynamics information encrypted into the conserved core and to separate the trans-family intrinsic flexibility associated with a common function from the protein motions related to functional specialization of selected families or family members. The conserved core is dynamically divided into two lobes. The deformation modes, which allow the Ras GTPases to accomplish their switching function, are conserved along evolution and are localized in lobe 1 portions close to the nucleotide. These modes lead to functional specialization when associated with evolution-driven deformations of protein portions essentially located in lobe 2, distal from the nucleotide, and involved in peculiar interactions with membrane, guanine nucleotide exchange factors, or effectors. Overall, a complete picture of the functional and evolutionary dynamics of the Ras superfamily emerges.
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Affiliation(s)
- Francesco Raimondi
- Dulbecco Telethon Institute and Department of Chemistry, via Campi 183, Modena, Italy
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32
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Potestio R, Aleksiev T, Pontiggia F, Cozzini S, Micheletti C. ALADYN: a web server for aligning proteins by matching their large-scale motion. Nucleic Acids Res 2010; 38:W41-5. [PMID: 20444876 PMCID: PMC2896196 DOI: 10.1093/nar/gkq293] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The ALADYN web server aligns pairs of protein structures by comparing their internal dynamics and detecting regions that sustain similar large-scale movements. The latter often accompany functional conformational changes in proteins and enzymes. The ALADYN dynamics-based alignment can therefore highlight functionally-oriented correspondences that could be more elusive to sequence- or structure-based comparisons. The ALADYN server takes the structure files of the two proteins as input. The optimal relative positioning of the molecules is found by maximizing the similarity of the pattern of structural fluctuations which are calculated via an elastic network model. The resulting alignment is presented via an interactive graphical Java applet and is accompanied by a number of quantitative indicators and downloadable data files. The ALADYN web server is freely accessible at the http://aladyn.escience-lab.org address.
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Affiliation(s)
- R Potestio
- Scuola Internazionale Superiore di Studi Avanzati and eLab, via Bonomea 265, 34136 Trieste, Italy
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