A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes

Abstract Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identify...

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Autores principales:	R. Pravin Kumar, Naveen Kulkarni
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f36e23b772ba4b379bcd21f06a9c6fea
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spelling	oai:doaj.org-article:f36e23b772ba4b379bcd21f06a9c6fea2021-12-02T12:32:02ZA receptor dependent-4D QSAR approach to predict the activity of mutated enzymes10.1038/s41598-017-06625-x2045-2322https://doaj.org/article/f36e23b772ba4b379bcd21f06a9c6fea2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06625-xhttps://doaj.org/toc/2045-2322Abstract Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identifying the right enzyme. As a proof of concept, for the first time, receptor dependent – 4D Quantitative Structure Activity Relationship (RD-4D-QSAR) has been implemented to predict kinetic properties of an enzyme. The novelty of this study is that the mutated enzymes also form a part of the training data set. The mutations were modeled in a serine protease and molecular dynamics simulations were conducted to derive enzyme-substrate (E-S) conformations. The E-S conformations were enclosed in a high resolution grid consisting of 156,250 grid points that stores interaction energies to generate QSAR models to predict the enzyme activity. The QSAR predictions showed similar results as reported in the kinetic studies with >80% specificity and >50% sensitivity revealing that the top ranked models unambiguously differentiated enzymes with high and low activity. The interaction energy descriptors of the best QSAR model were used to identify residues responsible for enzymatic activity and substrate specificity.R. Pravin KumarNaveen KulkarniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q R. Pravin Kumar Naveen Kulkarni A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
description	Abstract Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identifying the right enzyme. As a proof of concept, for the first time, receptor dependent – 4D Quantitative Structure Activity Relationship (RD-4D-QSAR) has been implemented to predict kinetic properties of an enzyme. The novelty of this study is that the mutated enzymes also form a part of the training data set. The mutations were modeled in a serine protease and molecular dynamics simulations were conducted to derive enzyme-substrate (E-S) conformations. The E-S conformations were enclosed in a high resolution grid consisting of 156,250 grid points that stores interaction energies to generate QSAR models to predict the enzyme activity. The QSAR predictions showed similar results as reported in the kinetic studies with >80% specificity and >50% sensitivity revealing that the top ranked models unambiguously differentiated enzymes with high and low activity. The interaction energy descriptors of the best QSAR model were used to identify residues responsible for enzymatic activity and substrate specificity.
format	article
author	R. Pravin Kumar Naveen Kulkarni
author_facet	R. Pravin Kumar Naveen Kulkarni
author_sort	R. Pravin Kumar
title	A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
title_short	A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
title_full	A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
title_fullStr	A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
title_full_unstemmed	A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes
title_sort	receptor dependent-4d qsar approach to predict the activity of mutated enzymes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f36e23b772ba4b379bcd21f06a9c6fea
work_keys_str_mv	AT rpravinkumar areceptordependent4dqsarapproachtopredicttheactivityofmutatedenzymes AT naveenkulkarni areceptordependent4dqsarapproachtopredicttheactivityofmutatedenzymes AT rpravinkumar receptordependent4dqsarapproachtopredicttheactivityofmutatedenzymes AT naveenkulkarni receptordependent4dqsarapproachtopredicttheactivityofmutatedenzymes
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A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes

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