Mining the Protein Data Bank to improve prediction of changes in protein-protein binding

Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full...

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Autores principales:	Samuel Coulbourn Flores, Athanasios Alexiou, Anastasios Glaros
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7eb2f390541948dc851c051afd3272da
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spelling	oai:doaj.org-article:7eb2f390541948dc851c051afd3272da2021-11-11T06:44:18ZMining the Protein Data Bank to improve prediction of changes in protein-protein binding1932-6203https://doaj.org/article/7eb2f390541948dc851c051afd3272da2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562805/?tool=EBIhttps://doaj.org/toc/1932-6203Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each. The method increases precision and Positive Predictive Value, and decreases Root Mean Square Error, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.Samuel Coulbourn FloresAthanasios AlexiouAnastasios GlarosPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samuel Coulbourn Flores Athanasios Alexiou Anastasios Glaros Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
description	Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each. The method increases precision and Positive Predictive Value, and decreases Root Mean Square Error, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.
format	article
author	Samuel Coulbourn Flores Athanasios Alexiou Anastasios Glaros
author_facet	Samuel Coulbourn Flores Athanasios Alexiou Anastasios Glaros
author_sort	Samuel Coulbourn Flores
title	Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
title_short	Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
title_full	Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
title_fullStr	Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
title_full_unstemmed	Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
title_sort	mining the protein data bank to improve prediction of changes in protein-protein binding
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7eb2f390541948dc851c051afd3272da
work_keys_str_mv	AT samuelcoulbournflores miningtheproteindatabanktoimprovepredictionofchangesinproteinproteinbinding AT athanasiosalexiou miningtheproteindatabanktoimprovepredictionofchangesinproteinproteinbinding AT anastasiosglaros miningtheproteindatabanktoimprovepredictionofchangesinproteinproteinbinding
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Mining the Protein Data Bank to improve prediction of changes in protein-protein binding

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