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Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose

In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et...

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Autores principales: Díaz, Lucía, Soler, Daniel, Tresadern, Gary, Buyck, Christophe, Perez-Benito, Laura, Saen-Oon, Suwipa, Guallar, Victor, Soliva, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049779/
https://www.ncbi.nlm.nih.gov/pubmed/35493910
http://dx.doi.org/10.1039/d0ra01127d
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author Díaz, Lucía
Soler, Daniel
Tresadern, Gary
Buyck, Christophe
Perez-Benito, Laura
Saen-Oon, Suwipa
Guallar, Victor
Soliva, Robert
author_facet Díaz, Lucía
Soler, Daniel
Tresadern, Gary
Buyck, Christophe
Perez-Benito, Laura
Saen-Oon, Suwipa
Guallar, Victor
Soliva, Robert
author_sort Díaz, Lucía
collection PubMed
description In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein–protein disruptors.
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spelling pubmed-90497792022-04-29 Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose Díaz, Lucía Soler, Daniel Tresadern, Gary Buyck, Christophe Perez-Benito, Laura Saen-Oon, Suwipa Guallar, Victor Soliva, Robert RSC Adv Chemistry In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein–protein disruptors. The Royal Society of Chemistry 2020-02-17 /pmc/articles/PMC9049779/ /pubmed/35493910 http://dx.doi.org/10.1039/d0ra01127d Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Díaz, Lucía
Soler, Daniel
Tresadern, Gary
Buyck, Christophe
Perez-Benito, Laura
Saen-Oon, Suwipa
Guallar, Victor
Soliva, Robert
Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title_full Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title_fullStr Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title_full_unstemmed Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title_short Monte Carlo simulations using PELE to identify a protein–protein inhibitor binding site and pose
title_sort monte carlo simulations using pele to identify a protein–protein inhibitor binding site and pose
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049779/
https://www.ncbi.nlm.nih.gov/pubmed/35493910
http://dx.doi.org/10.1039/d0ra01127d
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