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Nerín-Fonz F, Caprai C, Morales-Pastor A, Lopez-Balastegui M, Aranda-García D, Giorgino T, Selent J. AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks. Comput Struct Biotechnol J 2024; 23:1938-1944. [PMID: 38736696 PMCID: PMC11087696 DOI: 10.1016/j.csbj.2024.04.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.
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Affiliation(s)
- Francho Nerín-Fonz
- Hospital del Mar Research Institute & Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Camilla Caprai
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan, 20133, Italy
- National Research Council of Italy, Biophysics Institute (CNR-IBF), Via Celoria 26, Milan, 20133, Italy
| | - Adrián Morales-Pastor
- Hospital del Mar Research Institute & Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Marta Lopez-Balastegui
- Hospital del Mar Research Institute & Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain
| | - David Aranda-García
- Hospital del Mar Research Institute & Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Toni Giorgino
- National Research Council of Italy, Biophysics Institute (CNR-IBF), Via Celoria 26, Milan, 20133, Italy
| | - Jana Selent
- Hospital del Mar Research Institute & Universitat Pompeu Fabra, C/ Dr. Aiguader 88, Barcelona, 08003, Spain
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Erkip A, Erman B. Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins. Proteins 2024. [PMID: 38687146 DOI: 10.1002/prot.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/07/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.
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Affiliation(s)
- Albert Erkip
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Burak Erman
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Istanbul, Turkey
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Demirtaş K, Erman B, Haliloğlu T. Dynamic correlations: exact and approximate methods for mutual information. Bioinformatics 2024; 40:btae076. [PMID: 38341647 PMCID: PMC10898342 DOI: 10.1093/bioinformatics/btae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/17/2024] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions. RESULTS Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths. AVAILABILITY AND IMPLEMENTATION Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.
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Affiliation(s)
- Kemal Demirtaş
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
| | - Burak Erman
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey
| | - Türkan Haliloğlu
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
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Wu N, Barahona M, Yaliraki SN. Allosteric communication and signal transduction in proteins. Curr Opin Struct Biol 2024; 84:102737. [PMID: 38171189 DOI: 10.1016/j.sbi.2023.102737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 01/05/2024]
Abstract
Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based framework, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.
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Affiliation(s)
- Nan Wu
- Department of Chemistry, Imperial College London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, United Kingdom. https://twitter.com/@CMPHImperial
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Hou M, Jin S, Cui X, Peng C, Zhao K, Song L, Zhang G. Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm. Interdiscip Sci 2024:10.1007/s12539-023-00597-5. [PMID: 38190097 DOI: 10.1007/s12539-023-00597-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 01/09/2024]
Abstract
The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .
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Affiliation(s)
- Minghua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Sirong Jin
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Xinyue Cui
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Chunxiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Le Song
- BioMap & MBZUAI, Beijing, 100038, China.
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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Nussinov R, Liu Y, Zhang W, Jang H. Protein conformational ensembles in function: roles and mechanisms. RSC Chem Biol 2023; 4:850-864. [PMID: 37920394 PMCID: PMC10619138 DOI: 10.1039/d3cb00114h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/02/2023] [Indexed: 11/04/2023] Open
Abstract
The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research Frederick MD 21702 USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University Tel Aviv 69978 Israel
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Wengang Zhang
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research Frederick MD 21702 USA
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
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Erman B. Mutual information analysis of mutation, nonlinearity, and triple interactions in proteins. Proteins 2023; 91:121-133. [PMID: 36000344 DOI: 10.1002/prot.26415] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 12/15/2022]
Abstract
Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.
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Affiliation(s)
- Burak Erman
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
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