Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model t...

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Autores principales:	Hege Beard, Anuradha Cholleti, David Pearlman, Woody Sherman, Kathryn A Loving
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d236eef087cb446ba55c7e0112ce530e
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spelling	oai:doaj.org-article:d236eef087cb446ba55c7e0112ce530e2021-11-18T08:42:38ZApplying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.1932-620310.1371/journal.pone.0082849https://doaj.org/article/d236eef087cb446ba55c7e0112ce530e2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24340062/?tool=EBIhttps://doaj.org/toc/1932-6203Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.Hege BeardAnuradha CholletiDavid PearlmanWoody ShermanKathryn A LovingPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82849 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hege Beard Anuradha Cholleti David Pearlman Woody Sherman Kathryn A Loving Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
description	Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.
format	article
author	Hege Beard Anuradha Cholleti David Pearlman Woody Sherman Kathryn A Loving
author_facet	Hege Beard Anuradha Cholleti David Pearlman Woody Sherman Kathryn A Loving
author_sort	Hege Beard
title	Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
title_short	Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
title_full	Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
title_fullStr	Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
title_full_unstemmed	Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
title_sort	applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/d236eef087cb446ba55c7e0112ce530e
work_keys_str_mv	AT hegebeard applyingphysicsbasedscoringtocalculatefreeenergiesofbindingforsingleaminoacidmutationsinproteinproteincomplexes AT anuradhacholleti applyingphysicsbasedscoringtocalculatefreeenergiesofbindingforsingleaminoacidmutationsinproteinproteincomplexes AT davidpearlman applyingphysicsbasedscoringtocalculatefreeenergiesofbindingforsingleaminoacidmutationsinproteinproteincomplexes AT woodysherman applyingphysicsbasedscoringtocalculatefreeenergiesofbindingforsingleaminoacidmutationsinproteinproteincomplexes AT kathrynaloving applyingphysicsbasedscoringtocalculatefreeenergiesofbindingforsingleaminoacidmutationsinproteinproteincomplexes
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Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

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