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Conev A, Rigo MM, Devaurs D, Fonseca AF, Kalavadwala H, de Freitas MV, Clementi C, Zanatta G, Antunes DA, Kavraki LE. EnGens: a computational framework for generation and analysis of representative protein conformational ensembles. Brief Bioinform 2023; 24:bbad242. [PMID: 37418278 PMCID: PMC10359083 DOI: 10.1093/bib/bbad242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/23/2023] [Accepted: 06/10/2023] [Indexed: 07/08/2023] Open
Abstract
Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.
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Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston 77005, TX, USA
| | | | - Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | | | - Hussain Kalavadwala
- Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA
| | | | - Cecilia Clementi
- Department of Physics, Freie Universität Berlin, Berlin 14195, Germany
| | - Geancarlo Zanatta
- Department of Biophysics, Institute of Biosciences, Federal University of Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, Houston 77004, TX, USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston 77005, TX, USA
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2
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Conev A, Rigo MM, Devaurs D, Fonseca AF, Kalavadwala H, de Freitas MV, Clementi C, Zanatta G, Antunes DA, Kavraki L. EnGens: a computational framework for generation and analysis of representative protein conformational ensembles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538094. [PMID: 37163076 PMCID: PMC10168271 DOI: 10.1101/2023.04.24.538094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.
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Lamers C, Xue X, Smieško M, van Son H, Wagner B, Berger N, Sfyroera G, Gros P, Lambris JD, Ricklin D. Insight into mode-of-action and structural determinants of the compstatin family of clinical complement inhibitors. Nat Commun 2022; 13:5519. [PMID: 36127336 PMCID: PMC9488889 DOI: 10.1038/s41467-022-33003-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/17/2022] [Indexed: 12/04/2022] Open
Abstract
With the addition of the compstatin-based complement C3 inhibitor pegcetacoplan, another class of complement targeted therapeutics have recently been approved. Moreover, compstatin derivatives with enhanced pharmacodynamic and pharmacokinetic profiles are in clinical development (e.g., Cp40/AMY-101). Despite this progress, the target binding and inhibitory modes of the compstatin family remain incompletely described. Here, we present the crystal structure of Cp40 complexed with its target C3b at 2.0-Å resolution. Structure-activity-relationship studies rationalize the picomolar affinity and long target residence achieved by lead optimization, and reveal a role for structural water in inhibitor binding. We provide explanations for the narrow species specificity of this drug class and demonstrate distinct target selection modes between clinical compstatin derivatives. Functional studies provide further insight into physiological complement activation and corroborate the mechanism of its compstatin-mediated inhibition. Our study may thereby guide the application of existing and development of next-generation compstatin analogs.
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Affiliation(s)
- Christina Lamers
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland
- Institute of Drug Discovery, Faculty of Medicine, Leipzig University, Brüderstrasse 34, 04103, Leipzig, Germany
| | - Xiaoguang Xue
- Department of Chemistry, Faculty of Science, Utrecht University, Padualaan 8, 3584, Utrecht, The Netherlands
| | - Martin Smieško
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland
| | - Henri van Son
- Department of Chemistry, Faculty of Science, Utrecht University, Padualaan 8, 3584, Utrecht, The Netherlands
| | - Bea Wagner
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland
| | - Nadja Berger
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 401 Stellar Chance, 422 Curie Blvd, Philadelphia, 19104, PA, USA
| | - Georgia Sfyroera
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 401 Stellar Chance, 422 Curie Blvd, Philadelphia, 19104, PA, USA
| | - Piet Gros
- Department of Chemistry, Faculty of Science, Utrecht University, Padualaan 8, 3584, Utrecht, The Netherlands
| | - John D Lambris
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 401 Stellar Chance, 422 Curie Blvd, Philadelphia, 19104, PA, USA.
| | - Daniel Ricklin
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
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Devaurs D, Antunes DA, Borysik AJ. Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:215-237. [PMID: 35077179 DOI: 10.1021/jasms.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.
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Affiliation(s)
- Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, U.K
| | - Dinler A Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77005, United States
| | - Antoni J Borysik
- Department of Chemistry, King's College London, London SE1 1DB, U.K
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Liu Q, Wan J, Wang G. A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Brief Bioinform 2021; 23:6384382. [PMID: 34623382 PMCID: PMC8524468 DOI: 10.1093/bib/bbab416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [
1–
5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.
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Affiliation(s)
- Qiaoming Liu
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Guohua Wang
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China.,Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150001, China
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