DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

Hall-Swan, Sarah; Devaurs, Didier; Rigo, Maurício; Antunes, Dinler Amaral; Kavraki, Lydia E.; Zanatta, Geancarlo

doi:10.1016/j.compbiomed.2021.104943

Cited by 9 publications

(3 citation statements)

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“…The task we address is that of extracting representative conformational ensembles from datasets of protein structures. Extracted representative ensembles can be useful for many downstream tasks such as protein–ligand ensemble docking [ 26 ], analysis of mutational effects [ 27 ] and extensive Markov state modeling of protein dynamics [ 28 , 29 ]. It is important to provide sufficient analysis of the extracted ensemble to summarize important properties of each protein state (e.g.…”

Section: Introductionmentioning

confidence: 99%

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Conev

Rigo

Devaurs

et al. 2023

Briefings in Bioinformatics

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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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Section: Introductionmentioning

confidence: 99%

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Conev

Rigo

Devaurs

et al. 2023

Briefings in Bioinformatics

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“…The web server of Virus-CKB applies pharmacology target mapping to rapidly predict the FDA-approved drugs for COVID-19 ( 14 ). DINC-COVID is a webserver that performs the simultaneous docking of a ligand against multiple receptor conformations ( 15 ). The web server of MolAICal supplies a deep learning model for generating new compounds in the 3D pocket of the SARS-CoV-2 Main protease ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands

Liu

Shi

et al. 2023

Nucleic Acids Research

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The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn.

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“…The task we address is that of extracting described representative conformational ensembles from datasets of protein structures. Extracted representative ensembles can be useful for many downstream tasks such as protein-ligand ensemble docking [24], analysis of mutational effects [25] and extensive Markov state modeling of protein dynamics [26,27]. It is important to provide sufficient analysis of the extracted ensemble to summarize important properties of each protein state (e.g., the distance between protein domains or the distance between important residues in the active site) and help derive more intuitive insights (e.g., whether a member of the ensemble represents the protein in its active or inactive form).…”

Section: Introductionmentioning

confidence: 99%

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Conev

Rigo

Devaurs

et al. 2023

Preprint

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View full text Add to dashboard Cite

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.

show abstract

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

Cited by 9 publications

References 64 publications

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

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