1
|
Krieger JM, Sorzano COS, Carazo JM. Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing. Int J Mol Sci 2023; 24:14245. [PMID: 37762547 PMCID: PMC10532346 DOI: 10.3390/ijms241814245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/10/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.
Collapse
Affiliation(s)
- James M. Krieger
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
| | | | - Jose Maria Carazo
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
| |
Collapse
|
2
|
Banerjee A, Bahar I. Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions. Int J Mol Sci 2023; 24:8450. [PMID: 37176156 PMCID: PMC10179678 DOI: 10.3390/ijms24098450] [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: 03/24/2023] [Revised: 05/01/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.
Collapse
Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ivet Bahar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA
| |
Collapse
|
3
|
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.
Collapse
|
4
|
Chen L, Gong W, Han Z, Zhou W, Yang S, Li C. Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method. J Chem Inf Model 2022; 62:6727-6738. [PMID: 36073904 DOI: 10.1021/acs.jcim.2c00513] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.
Collapse
Affiliation(s)
- Lei Chen
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Weikang Gong
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Zhongjie Han
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Wenxue Zhou
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shuang Yang
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| |
Collapse
|
5
|
Zhang H, Shan G, Yang B. Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1064-1074. [PMID: 32915744 DOI: 10.1109/tcbb.2020.3023147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.
Collapse
|
6
|
Nafshi R, Lezon TR. Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization. FRONTIERS IN BIOINFORMATICS 2021; 1:708815. [PMID: 36303743 PMCID: PMC9581062 DOI: 10.3389/fbinf.2021.708815] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/30/2021] [Indexed: 12/12/2022] Open
Abstract
Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.
Collapse
|
7
|
Gong W, Liu Y, Zhao Y, Wang S, Han Z, Li C. Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models. J Chem Inf Model 2021; 61:921-937. [PMID: 33496590 DOI: 10.1021/acs.jcim.0c01178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.
Collapse
Affiliation(s)
- Weikang Gong
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Yang Liu
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Yanpeng Zhao
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Shihao Wang
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Zhongjie Han
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.,Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China
| |
Collapse
|
8
|
Foley TT, Kidder KM, Shell MS, Noid WG. Exploring the landscape of model representations. Proc Natl Acad Sci U S A 2020; 117:24061-24068. [PMID: 32929015 PMCID: PMC7533877 DOI: 10.1073/pnas.2000098117] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.
Collapse
Affiliation(s)
- Thomas T Foley
- Department of Chemistry, The Pennsylvania State University, University Park, PA 16802
- Department of Physics, The Pennsylvania State University, University Park, PA 16802
| | - Katherine M Kidder
- Department of Chemistry, The Pennsylvania State University, University Park, PA 16802
| | - M Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106
| | - W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, PA 16802;
| |
Collapse
|
9
|
Alemasov NA, Ivanisenko NV, Ivanisenko VA. Learning the changes of barnase mutants thermostability from structural fluctuations obtained using anisotropic network modeling. J Mol Graph Model 2020; 97:107572. [PMID: 32114079 DOI: 10.1016/j.jmgm.2020.107572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 01/29/2020] [Accepted: 02/19/2020] [Indexed: 11/17/2022]
Abstract
In biotechnology applications, rational design of new proteins with improved physico-chemical properties includes a number of important tasks. One of the greatest practical and fundamental challenges is the design of highly thermostable protein enzymes that maintain catalytic activity at high temperatures. This problem may be solved by introducing mutations into the wild-type enzyme protein. In this work, to predict the impact of such mutations in barnase protein we applied the anisotropic network modeling approach, revealing atomic fluctuations in structural regions that are changed in mutants compared to the wild-type protein. A regression model was constructed based on these structural features that can allow one to predict the thermal stability of new barnase mutants. Moreover, the analysis of regression model provides a mechanistic explanation of how the structural features can contribute to the thermal stability of barnase mutants.
Collapse
Affiliation(s)
- Nikolay A Alemasov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia.
| | - Nikita V Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
| | - Vladimir A Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
| |
Collapse
|
10
|
Götz A, Mylonas N, Högel P, Silber M, Heinel H, Menig S, Vogel A, Feyrer H, Huster D, Luy B, Langosch D, Scharnagl C, Muhle-Goll C, Kamp F, Steiner H. Modulating Hinge Flexibility in the APP Transmembrane Domain Alters γ-Secretase Cleavage. Biophys J 2019; 116:2103-2120. [PMID: 31130234 PMCID: PMC6554489 DOI: 10.1016/j.bpj.2019.04.030] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 02/14/2019] [Accepted: 04/15/2019] [Indexed: 01/27/2023] Open
Abstract
Intramembrane cleavage of the β-amyloid precursor protein C99 substrate by γ-secretase is implicated in Alzheimer's disease pathogenesis. Biophysical data have suggested that the N-terminal part of the C99 transmembrane domain (TMD) is separated from the C-terminal cleavage domain by a di-glycine hinge. Because the flexibility of this hinge might be critical for γ-secretase cleavage, we mutated one of the glycine residues, G38, to a helix-stabilizing leucine and to a helix-distorting proline. Both mutants impaired γ-secretase cleavage and also altered its cleavage specificity. Circular dichroism, NMR, and backbone amide hydrogen/deuterium exchange measurements as well as molecular dynamics simulations showed that the mutations distinctly altered the intrinsic structural and dynamical properties of the substrate TMD. Although helix destabilization and/or unfolding was not observed at the initial ε-cleavage sites of C99, subtle changes in hinge flexibility were identified that substantially affected helix bending and twisting motions in the entire TMD. These resulted in altered orientation of the distal cleavage domain relative to the N-terminal TMD part. Our data suggest that both enhancing and reducing local helix flexibility of the di-glycine hinge may decrease the occurrence of enzyme-substrate complex conformations required for normal catalysis and that hinge mobility can thus be conducive for productive substrate-enzyme interactions.
Collapse
Affiliation(s)
- Alexander Götz
- Physics of Synthetic Biological Systems (E14), Technical University of Munich, Freising, Germany
| | - Nadine Mylonas
- Biomedical Center (BMC), Metabolic Biochemistry, Ludwig-Maximilians-University, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Philipp Högel
- Center for Integrated Protein Science Munich at the Lehrstuhl Chemie der Biopolymere, Technical University Munich, Freising, Germany
| | - Mara Silber
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Hannes Heinel
- Institute for Medical Physics and Biophysics, Leipzig University, Leipzig, Germany
| | - Simon Menig
- Physics of Synthetic Biological Systems (E14), Technical University of Munich, Freising, Germany
| | - Alexander Vogel
- Institute for Medical Physics and Biophysics, Leipzig University, Leipzig, Germany
| | - Hannes Feyrer
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Daniel Huster
- Institute for Medical Physics and Biophysics, Leipzig University, Leipzig, Germany
| | - Burkhard Luy
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Dieter Langosch
- Center for Integrated Protein Science Munich at the Lehrstuhl Chemie der Biopolymere, Technical University Munich, Freising, Germany
| | - Christina Scharnagl
- Physics of Synthetic Biological Systems (E14), Technical University of Munich, Freising, Germany.
| | - Claudia Muhle-Goll
- Institute of Organic Chemistry and Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Frits Kamp
- Biomedical Center (BMC), Metabolic Biochemistry, Ludwig-Maximilians-University, Munich, Germany
| | - Harald Steiner
- Biomedical Center (BMC), Metabolic Biochemistry, Ludwig-Maximilians-University, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
| |
Collapse
|
11
|
Zhang W, Xie J, Lai L. Correlation Between Allosteric and Orthosteric Sites. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1163:89-105. [PMID: 31707701 DOI: 10.1007/978-981-13-8719-7_5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Correlation between an allosteric site and its orthosteric site refers to the phenomenon that perturbations like ligand binding, mutation, or posttranslational modifications at the allosteric site leverage variation in the orthosteric site. Understanding this kind of correlation not only helps to disclose how information is transmitted in allosteric regulation but also provides clues for allosteric drug discovery. This chapter starts with an overview of correlation studies on allosteric and orthosteric sites and then introduces recent progress in evolutionary and simulation-based dynamic studies. Discussions and perspectives on future directions are also given.
Collapse
Affiliation(s)
- Weilin Zhang
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Center for Quantitative Biology, AAIS, Peking University, Beijing, China
| | - Juan Xie
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Center for Quantitative Biology, AAIS, Peking University, Beijing, China
| | - Luhua Lai
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
- Center for Quantitative Biology, AAIS, Peking University, Beijing, China.
| |
Collapse
|
12
|
Götz A, Scharnagl C. Dissecting conformational changes in APP's transmembrane domain linked to ε-efficiency in familial Alzheimer's disease. PLoS One 2018; 13:e0200077. [PMID: 29966005 PMCID: PMC6028146 DOI: 10.1371/journal.pone.0200077] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 06/19/2018] [Indexed: 02/02/2023] Open
Abstract
The mechanism by which familial Alzheimer's disease (FAD) mutations within the transmembrane domain (TMD) of the Amyloid Precursor Protein (APP) affect ε-endoproteolysis is only poorly understood. Thereby, mutations in the cleavage domain reduce ε-efficiency of γ-secretase cleavage and some even shift entry into production lines. Since cleavage occurs within the TMD, a relationship between processing and TMD structure and dynamics seems obvious. Using molecular dynamic simulations, we dissect the dynamic features of wild-type and seven FAD-mutants into local and global components. Mutations consistently enhance hydrogen-bond fluctuations upstream of the ε-cleavage sites but maintain strong helicity there. Dynamic perturbation-response scanning reveals that FAD-mutants target backbone motions utilized in the bound state. Those motions, obscured by large-scale motions in the pre-bound state, provide (i) a dynamic mechanism underlying the proposed coupling between binding and ε-cleavage, (ii) key sites consistent with experimentally determined docking sites, and (iii) the distinction between mutants and wild-type.
Collapse
Affiliation(s)
- Alexander Götz
- Technical University of Munich, Chair of Physics of Synthetic Biological Systems, Freising, Germany
| | - Christina Scharnagl
- Technical University of Munich, Chair of Physics of Synthetic Biological Systems, Freising, Germany
| |
Collapse
|
13
|
Dehouck Y, Bastolla U. The maximum penalty criterion for ridge regression: application to the calibration of the force constant in elastic network models. Integr Biol (Camb) 2018; 9:627-641. [PMID: 28555214 DOI: 10.1039/c7ib00079k] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Tikhonov regularization, or ridge regression, is a popular technique to deal with collinearity in multivariate regression. We unveil a formal analogy between ridge regression and statistical mechanics, where the objective function is comparable to a free energy, and the ridge parameter plays the role of temperature. This analogy suggests two novel criteria for selecting a suitable ridge parameter: specific-heat (Cv) and maximum penalty (MP). We apply these fits to evaluate the relative contributions of rigid-body and internal fluctuations, which are typically highly collinear, to crystallographic B-factors. This issue is particularly important for computational models of protein dynamics, such as the elastic network model (ENM), since the amplitude of the predicted internal motion is commonly calibrated using B-factor data. After validation on simulated datasets, our results indicate that rigid-body motions account on average for more than 80% of the amplitude of B-factors. Furthermore, we evaluate the ability of different fits to reproduce the amplitudes of internal fluctuations in X-ray ensembles from the B-factors in the corresponding single X-ray structures. The new ridge criteria are shown to be markedly superior to the commonly used two-parameter fit that neglects rigid-body rotations and to the full fits regularized under generalized cross-validation. In conclusion, the proposed fits ensure a more robust calibration of the ENM force constant and should prove valuable in other applications.
Collapse
Affiliation(s)
- Yves Dehouck
- Machine Learning Group, Université Libre de Bruxelles (ULB), Boulevard du Triomphe CP 212, 1050 Brussels, Belgium.
| | | |
Collapse
|
14
|
Putz I, Brock O. Elastic network model of learned maintained contacts to predict protein motion. PLoS One 2017; 12:e0183889. [PMID: 28854238 PMCID: PMC5576689 DOI: 10.1371/journal.pone.0183889] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 08/14/2017] [Indexed: 12/21/2022] Open
Abstract
We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein's contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a "deformation-invariant" contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.
Collapse
Affiliation(s)
- Ines Putz
- Robotics and Biology Laboratory, Department of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Berlin, Germany
| | - Oliver Brock
- Robotics and Biology Laboratory, Department of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Berlin, Germany
| |
Collapse
|
15
|
Tang QY, Zhang YY, Wang J, Wang W, Chialvo DR. Critical Fluctuations in the Native State of Proteins. PHYSICAL REVIEW LETTERS 2017; 118:088102. [PMID: 28282168 DOI: 10.1103/physrevlett.118.088102] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Indexed: 06/06/2023]
Abstract
Based on protein structural ensembles determined by nuclear magnetic resonance, we study the position fluctuations of residues by calculating distance-dependent correlations and conducting finite-size scaling analysis. The fluctuations exhibit high susceptibility and long-range correlations up to the protein sizes. The scaling relations between the correlations or susceptibility and protein sizes resemble those in other physical and biological systems near their critical points. These results indicate that, at the native states, motions of each residue are felt by every other one in the protein. We also find that proteins with larger susceptibility are more frequently observed in nature. Overall, our results suggest that the protein's native state is critical.
Collapse
Affiliation(s)
- Qian-Yuan Tang
- National Lab of Solid State Microstructure, Collaborative Innovation Center of Advanced Microstructures, and Department of Physics, Nanjing University, Nanjing 210093, China
| | - Yang-Yang Zhang
- National Lab of Solid State Microstructure, Collaborative Innovation Center of Advanced Microstructures, and Department of Physics, Nanjing University, Nanjing 210093, China
| | - Jun Wang
- National Lab of Solid State Microstructure, Collaborative Innovation Center of Advanced Microstructures, and Department of Physics, Nanjing University, Nanjing 210093, China
| | - Wei Wang
- National Lab of Solid State Microstructure, Collaborative Innovation Center of Advanced Microstructures, and Department of Physics, Nanjing University, Nanjing 210093, China
| | - Dante R Chialvo
- CEMSC3, Center for Complex Systems & Brain Sciences, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín & Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), 25 de Mayo y Francia, San Martín(1650), Buenos Aires, Argentina
| |
Collapse
|
16
|
Bergman S, Lezon TR. Modeling global changes induced by local perturbations to the HIV-1 capsid. J Mol Graph Model 2016; 71:218-226. [PMID: 27951510 DOI: 10.1016/j.jmgm.2016.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 11/04/2016] [Accepted: 12/01/2016] [Indexed: 02/02/2023]
Abstract
The HIV-1 capsid is a conical protein shell made up of hexamers and pentamers of the capsid protein. The capsid houses the viral genome and replication machinery, and its opening, or uncoating, within the host cell marks a critical step in the HIV-1 lifecycle. Binding of host factors such as TRIM5α and cyclophilin A (CypA) can alter the capsid's stability, accelerating or delaying the onset of uncoating and disrupting infectivity. We employ coarse-grained computational modeling to investigate the effects of point mutations and host factor binding on HIV-1 capsid stability. We find that the largest fluctuations occur in the low-curvature regions of the capsid, and that its structural dynamics are affected by perturbations at the inter-hexamer interfaces and near the CypA binding loop, suggesting roles for these features in capsid stability. Our models show that linking capsid proteins across hexamers attenuates vibration in the low-curvature regions of the capsid, but that linking within hexamers does not. These results indicate a possible mechanism through which CypA binding alters capsid stability and highlight the utility of coarse-grained network modeling for understanding capsid mechanics.
Collapse
Affiliation(s)
- Shana Bergman
- Department of Computational and Systems Biology, University of Pittsburgh, Suite 3064 Biomedical Science Tower 3, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA.
| | - Timothy R Lezon
- Department of Computational and Systems Biology, University of Pittsburgh, Suite 3064 Biomedical Science Tower 3, 3501 Fifth Avenue, Pittsburgh, PA 15260, USA; University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, W965 Biomedical Science Tower, 200 Lothrop Street, Pittsburgh, PA 15261, USA.
| |
Collapse
|
17
|
Foley TT, Shell MS, Noid WG. The impact of resolution upon entropy and information in coarse-grained models. J Chem Phys 2016; 143:243104. [PMID: 26723589 DOI: 10.1063/1.4929836] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
By eliminating unnecessary degrees of freedom, coarse-grained (CG) models tremendously facilitate numerical calculations and theoretical analyses of complex phenomena. However, their success critically depends upon the representation of the system and the effective potential that governs the CG degrees of freedom. This work investigates the relationship between the CG representation and the many-body potential of mean force (PMF), W, which is the appropriate effective potential for a CG model that exactly preserves the structural and thermodynamic properties of a given high resolution model. In particular, we investigate the entropic component of the PMF and its dependence upon the CG resolution. This entropic component, SW, is a configuration-dependent relative entropy that determines the temperature dependence of W. As a direct consequence of eliminating high resolution details from the CG model, the coarsening process transfers configurational entropy and information from the configuration space into SW. In order to further investigate these general results, we consider the popular Gaussian Network Model (GNM) for protein conformational fluctuations. We analytically derive the exact PMF for the GNM as a function of the CG representation. In the case of the GNM, -TSW is a positive, configuration-independent term that depends upon the temperature, the complexity of the protein interaction network, and the details of the CG representation. This entropic term demonstrates similar behavior for seven model proteins and also suggests, in each case, that certain resolutions provide a more efficient description of protein fluctuations. These results may provide general insight into the role of resolution for determining the information content, thermodynamic properties, and transferability of CG models. Ultimately, they may lead to a rigorous and systematic framework for optimizing the representation of CG models.
Collapse
Affiliation(s)
- Thomas T Foley
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - M Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| |
Collapse
|
18
|
López-Blanco JR, Chacón P. New generation of elastic network models. Curr Opin Struct Biol 2015; 37:46-53. [PMID: 26716577 DOI: 10.1016/j.sbi.2015.11.013] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/23/2015] [Accepted: 11/26/2015] [Indexed: 12/16/2022]
Abstract
The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation.
Collapse
Affiliation(s)
- José Ramón López-Blanco
- Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain
| | - Pablo Chacón
- Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain.
| |
Collapse
|
19
|
Heterogeneous elastic network model improves description of slow motions of proteins in solution. Chem Phys Lett 2015. [DOI: 10.1016/j.cplett.2014.11.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
20
|
Bakan A, Dutta A, Mao W, Liu Y, Chennubhotla C, Lezon TR, Bahar I. Evol and ProDy for bridging protein sequence evolution and structural dynamics. Bioinformatics 2014; 30:2681-3. [PMID: 24849577 DOI: 10.1093/bioinformatics/btu336] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. AVAILABILITY AND IMPLEMENTATION ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/.
Collapse
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
| | - Anindita Dutta
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Wenzhi Mao
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ying Liu
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Chakra Chennubhotla
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Timothy R Lezon
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, and Clinical & Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| |
Collapse
|
21
|
Abstract
By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.
Collapse
Affiliation(s)
- W G Noid
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| |
Collapse
|
22
|
Identifying essential pairwise interactions in elastic network model using the alpha shape theory. J Comput Chem 2014; 35:1111-21. [DOI: 10.1002/jcc.23587] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 01/24/2014] [Accepted: 02/26/2014] [Indexed: 11/07/2022]
|
23
|
Dehouck Y, Mikhailov AS. Effective harmonic potentials: insights into the internal cooperativity and sequence-specificity of protein dynamics. PLoS Comput Biol 2013; 9:e1003209. [PMID: 24009495 PMCID: PMC3757084 DOI: 10.1371/journal.pcbi.1003209] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 07/19/2013] [Indexed: 11/18/2022] Open
Abstract
The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or on their ability to perform wider and sometimes highly elaborated motions. Hence, there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics, as an alternative to more computationally expensive approaches. In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. We propose, for the first time, a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.
Collapse
Affiliation(s)
- Yves Dehouck
- Department of Physical Chemistry, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany.
| | | |
Collapse
|
24
|
Xia F, Tong D, Lu L. Robust Heterogeneous Anisotropic Elastic Network Model Precisely Reproduces the Experimental B-factors of Biomolecules. J Chem Theory Comput 2013; 9:3704-14. [PMID: 26584122 DOI: 10.1021/ct4002575] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
A computational method called the progressive fluctuation matching (PFM) is developed for constructing robust heterogeneous anisotropic network models (HANMs) for biomolecular systems. An HANM derived through the PFM approach consists of harmonic springs with realistic positive force constants, and yields the calculated B-factors that are basically identical to the experimental ones. For the four tested protein systems including crambin, trypsin inhibitor, HIV-1 protease, and lysozyme, the root-mean-square deviations between the experimental and the computed B-factors are only 0.060, 0.095, 0.247, and 0.049 Å(2), respectively, and the correlation coefficients are 0.99 for all. By comparing the HANM/ANM normal modes to their counterparts derived from both an atomistic force field and an NMR structure ensemble, it is found that HANM may provide more accurate results on protein dynamics.
Collapse
Affiliation(s)
- Fei Xia
- School of Biological Sciences, Nanyang Technological University , 60 Nanyang Drive, Singapore, 637551
| | - Dudu Tong
- School of Biological Sciences, Nanyang Technological University , 60 Nanyang Drive, Singapore, 637551
| | - Lanyuan Lu
- School of Biological Sciences, Nanyang Technological University , 60 Nanyang Drive, Singapore, 637551
| |
Collapse
|
25
|
Gur M, Erman B. Quasi-harmonic fluctuations of two bound peptides. Proteins 2012; 80:2769-79. [DOI: 10.1002/prot.24160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2012] [Revised: 07/27/2012] [Accepted: 08/06/2012] [Indexed: 11/10/2022]
|
26
|
Lezon TR. The effects of rigid motions on elastic network model force constants. Proteins 2012; 80:1133-42. [PMID: 22228562 DOI: 10.1002/prot.24014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2011] [Revised: 11/15/2011] [Accepted: 12/06/2011] [Indexed: 11/10/2022]
Abstract
Elastic network models provide an efficient way to quickly calculate protein global dynamics from experimentally determined structures. The model's single parameter, its force constant, determines the physical extent of equilibrium fluctuations. The values of force constants can be calculated by fitting to experimental data, but the results depend on the type of experimental data used. Here, we investigate the differences between calculated values of force constants and data from NMR and X-ray structures. We find that X-ray B factors carry the signature of rigid-body motions, to the extent that B factors can be almost entirely accounted for by rigid motions alone. When fitting to more refined anisotropic temperature factors, the contributions of rigid motions are significantly reduced, indicating that the large contribution of rigid motions to B factors is a result of over-fitting. No correlation is found between force constants fit to NMR data and those fit to X-ray data, possibly due to the inability of NMR data to accurately capture protein dynamics.
Collapse
Affiliation(s)
- Timothy R Lezon
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.
| |
Collapse
|
27
|
Meireles L, Gur M, Bakan A, Bahar I. Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins. Protein Sci 2011; 20:1645-58. [PMID: 21826755 PMCID: PMC3218357 DOI: 10.1002/pro.711] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2011] [Revised: 08/02/2011] [Accepted: 08/03/2011] [Indexed: 11/11/2022]
Abstract
Modeling protein flexibility constitutes a major challenge in accurate prediction of protein-ligand and protein-protein interactions in docking simulations. The lack of a reliable method for predicting the conformational changes relevant to substrate binding prevents the productive application of computational docking to proteins that undergo large structural rearrangements. Here, we examine how coarse-grained normal mode analysis has been advantageously applied to modeling protein flexibility associated with ligand binding. First, we highlight recent studies that have shown that there is a close agreement between the large-scale collective motions of proteins predicted by elastic network models and the structural changes experimentally observed upon ligand binding. Then, we discuss studies that have exploited the predicted soft modes in docking simulations. Two general strategies are noted: pregeneration of conformational ensembles that are then utilized as input for standard fixed-backbone docking and protein structure deformation along normal modes concurrent to docking. These studies show that the structural changes apparently "induced" upon ligand binding occur selectively along the soft modes accessible to the protein prior to ligand binding. They further suggest that proteins offer suitable means of accommodating/facilitating the recognition and binding of their ligand, presumably acquired by evolutionary selection of the suitable three-dimensional structure.
Collapse
Affiliation(s)
- Lidio Meireles
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | | | | | | |
Collapse
|
28
|
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
Collapse
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.
| | | | | |
Collapse
|
29
|
Bernard A, Vranken WF, Bardiaux B, Nilges M, Malliavin TE. Bayesian estimation of NMR restraint potential and weight: a validation on a representative set of protein structures. Proteins 2011; 79:1525-37. [PMID: 21365680 DOI: 10.1002/prot.22980] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2010] [Revised: 12/15/2010] [Accepted: 12/22/2010] [Indexed: 11/07/2022]
Abstract
The classical procedure for nuclear magnetic resonance structure calculation allocates empirical distance ranges and uses historical values for weighting factors. However, Bayesian analysis suggests that there are more optimal choices for potential shape (bounds-free log-harmonic shape) and restraints weights. We compare the classical protocol with the Bayesian approach for more than 300 protein structures. We analyze the conformation similarity to the corresponding X-ray crystal structure, the distribution of the conformations around their average, and independent validation criteria. On average, the log-harmonic potential reduces the difference to the X-ray crystal structure. If the log-harmonic potential is used, the constant weighting tightens the distribution around the average conformation, with respect to the distributions obtained with Bayesian weighting. Conversely, the structure quality is improved by the Bayesian weighting over the classical procedure, whereas constant weighting worsens some criteria. The quality improvement obtained with the log-harmonic potential coupled to Bayesian weighting validates this approach on a representative set of protein structures.
Collapse
Affiliation(s)
- Aymeric Bernard
- Unité de Bioinformatique Structurale, CNRS URA 2185, Institut Pasteur, 25-28 rue du Dr. Roux, Paris 75724, France
| | | | | | | | | |
Collapse
|