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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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Konecki DM, Hamrick S, Wang C, Agosto MA, Wensel TG, Lichtarge O. CovET: A covariation-evolutionary trace method that identifies protein structure-function modules. J Biol Chem 2023; 299:104896. [PMID: 37290531 PMCID: PMC10338321 DOI: 10.1016/j.jbc.2023.104896] [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: 01/25/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.
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Affiliation(s)
- Daniel M Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Spencer Hamrick
- Chemical, Physical, and Structural Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Melina A Agosto
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Theodore G Wensel
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Olivier Lichtarge
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA.
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Alfonso-Prieto M, Capelli R. Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study. J Chem Inf Model 2023; 63:2911-2917. [PMID: 37145455 DOI: 10.1021/acs.jcim.3c00380] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ∼400 members of this family. Given the almost concurrent publication of a CryoEM structure of the same receptor in the active state, we propose this protocol as an in silico complement to the growing field of ORs structure determination.
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Affiliation(s)
- Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, D-52428 Jülich, Germany
| | - Riccardo Capelli
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, I-20133 Milan, Italy
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Cong X, Zhang X, Liang X, He X, Tang Y, Zheng X, Lu S, Zhang J, Chen T. Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study. Comput Struct Biotechnol J 2022; 20:2268-2279. [PMID: 35615027 PMCID: PMC9117689 DOI: 10.1016/j.csbj.2022.05.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 12/22/2022] Open
Abstract
As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT2R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT2R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT2R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT2R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT2R mechanism at an atomic level and provide hints for the design of novel AT2R modulators.
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Affiliation(s)
- Xiaoliang Cong
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xiaogang Zhang
- Department of Cardiology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai 201318, China
| | - Xin Liang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xinheng He
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Yehua Tang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xing Zheng
- Department of Cardiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Shaoyong Lu
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Corresponding authors.
| | - Jiayou Zhang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
- Corresponding authors.
| | - Ting Chen
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
- Corresponding authors.
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Wang Y, Li M, Liang W, Shi X, Fan J, Kong R, Liu Y, Zhang J, Chen T, Lu S. Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor. Comput Struct Biotechnol J 2022; 20:628-639. [PMID: 35140883 PMCID: PMC8801358 DOI: 10.1016/j.csbj.2022.01.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 02/09/2023] Open
Abstract
Class B G protein-coupled receptors (GPCRs) are important targets in the treatment of metabolic syndrome and diabetes. Although multiple structures of class B GPCRs-G protein complexes have been elucidated, the detailed activation mechanism of the receptors remains unclear. Here, we combine Gaussian accelerated molecular dynamics simulations and Markov state models (MSM) to investigate the activation mechanism of a canonical class B GPCR, human glucagon receptor-GCGR, including the negative allosteric modulator-bound inactive state, the agonist glucagon-bound active state, and both glucagon- and Gs-bound fully active state. The free-energy landscapes of GCGR show the conformational ensemble consisting of three activation-associated states: inactive, active, and fully active. The structural analysis indicates the high dynamics of GCGR upon glucagon binding with both active and inactive conformations in the ensemble. Significantly, the H8 and TM6 exhibits distinct features from the inactive to the active states. The additional simulations demonstrate the role of H8 in the recruitment of Gs. Gs binding presents a crucial function of stabilizing the glucagon binding site and MSM highlights the absolute requirement of Gs to help the GCGR reach the fully active state. Together, our results reveal the detailed activation mechanism of GCGR from the view of conformational dynamics.
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Affiliation(s)
- Ying Wang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Mingyu Li
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Wenqi Liang
- Department of Emergency, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xinchao Shi
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jigang Fan
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai 200023, China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
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Cheung NJ, John Peter AT, Kornmann B. Leri: A web-server for identifying protein functional networks from evolutionary couplings. Comput Struct Biotechnol J 2021; 19:3556-3563. [PMID: 34257835 PMCID: PMC8239741 DOI: 10.1016/j.csbj.2021.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/30/2021] [Accepted: 06/02/2021] [Indexed: 12/12/2022] Open
Abstract
Identify the evolutionary signatures (termed “residue communities”) from protein sequences. The identified residue communities specify the signatures of protein evolution and function sites. Guide the engineering of functional proteins with altered (bio) chemical activities.
Information on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed ”residue communities”, relevant to protein function. On the benchmark dataset (67 proteins with both catalytic and allosteric residues), the Pearson’s correlation between the identified residues in the communities at functional sites is 0.53, and it is higher than 0.8 by taking account of conserved residues derived from the method. On the endoplasmic reticulum-mitochondria encounter structure complex, the results indicate three distinguishable residue communities that are relevant to functional roles in the protein family, suggesting that the residue communities could be general evolutionary signatures in proteins. Based on the method, we provide a webserver for the scientific community to explore the signatures in protein families, which establishes a powerful tool to analyze residue-level profiling for the discovery of functional sites and biological pathway identification. This web-server is freely available for non-commercial users at https://kornmann.bioch.ox.ac.uk/leri/services/ecs.html, neither login nor e-mail required.
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Affiliation(s)
- Ngaam J Cheung
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK.,Leri Ltd, Oxford, UK
| | | | - Benoit Kornmann
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK
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Crippa M, Andreghetti D, Capelli R, Tiana G. Evolution of frustrated and stabilising contacts in reconstructed ancient proteins. EUROPEAN BIOPHYSICS JOURNAL 2021; 50:699-712. [PMID: 33569610 PMCID: PMC8260555 DOI: 10.1007/s00249-021-01500-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/14/2020] [Accepted: 01/13/2021] [Indexed: 11/30/2022]
Abstract
Energetic properties of a protein are a major determinant of its evolutionary fitness. Using a reconstruction algorithm, dating the reconstructed proteins and calculating the interaction network between their amino acids through a coevolutionary approach, we studied how the interactions that stabilise 890 proteins, belonging to five families, evolved for billions of years. In particular, we focused our attention on the network of most strongly attractive contacts and on that of poorly optimised, frustrated contacts. Our results support the idea that the cluster of most attractive interactions extends its size along evolutionary time, but from the data, we cannot conclude that protein stability or that the degree of frustration tends always to decrease.
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Affiliation(s)
- Martina Crippa
- Department of Physics and Center for Complexity and Biosystems, Università degli Studi di Milano and INFN, via Celoria 16, 20133, Milan, Italy
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Damiano Andreghetti
- Department of Physics and Center for Complexity and Biosystems, Università degli Studi di Milano and INFN, via Celoria 16, 20133, Milan, Italy
| | - Riccardo Capelli
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Guido Tiana
- Department of Physics and Center for Complexity and Biosystems, Università degli Studi di Milano and INFN, via Celoria 16, 20133, Milan, Italy.
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Sena DM, Cong X, Giorgetti A. Ligand based conformational space studies of the μ-opioid receptor. Biochim Biophys Acta Gen Subj 2020; 1865:129838. [PMID: 33373630 DOI: 10.1016/j.bbagen.2020.129838] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 01/25/2023]
Abstract
BACKGROUND G protein-coupled receptors (GPCRs) comprise a family of membrane proteins that can be activated by a variety of external factors. The μ-opioid receptor (MOR), a class A GPCR, is the main target of morphine. Recently, enhanced sampling molecular dynamics simulations of a constitutively active mutant of MOR in its apo form allowed us to capture the novel intermediate states of activation, as well as the active state. This prompted us to apply the same techniques to wild type MOR in complex with ligands, in order to explore their contributions to the receptor conformational changes in the activation process. METHODS MOR was modeled in complex with agonists (morphine, BU72), a partial agonist (naloxone benzoylhydrazone) and an antagonist (naloxone). Replica exchange with solute tempering (REST2) molecular dynamics simulations were carried out for all systems. Trajectory frames were clustered, and the activation state of each cluster was assessed by two different methods. RESULTS Cluster sizes and activation indices show that while agonists stabilized structures in a higher activation state, the antagonist behaved oppositely. Morphine tends to drive the receptor towards increasing R165-T279 distances, while naloxone tends to increase the NPxxYA motif conformational change. CONCLUSIONS Despite not observing a full transition between inactive and active states, an important conformational change of transmembrane helix 5 was observed and associated with a ligand-driven step of the process. GENERAL SIGNIFICANCE The activation process of GPCRs is widely studied but still not fully understood. Here we carried out a step forward in the direction of gaining more details of this process.
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Affiliation(s)
- Diniz M Sena
- Universidade Regional do Cariri - URCA, Biological Chemistry Dept., Crato, CE 63105-000, Brazil.
| | - Xiaojing Cong
- Université Côte d'Azur, CNRS, Institut de Chimie de Nice UMR7272, Nice 06108, France
| | - Alejandro Giorgetti
- Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich, Germany; Department of Biotechnology, University of Verona, Verona, Italy.
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