Classification and prediction of protein–protein interaction interface using machine learning algorithm

Abstract Structural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimen...

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Autores principales:	Subhrangshu Das, Saikat Chakrabarti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea38e476f8c744c8aa3426234ef73a99
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spelling	oai:doaj.org-article:ea38e476f8c744c8aa3426234ef73a992021-12-02T13:48:41ZClassification and prediction of protein–protein interaction interface using machine learning algorithm10.1038/s41598-020-80900-22045-2322https://doaj.org/article/ea38e476f8c744c8aa3426234ef73a992021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80900-2https://doaj.org/toc/2045-2322Abstract Structural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .Subhrangshu DasSaikat ChakrabartiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Subhrangshu Das Saikat Chakrabarti Classification and prediction of protein–protein interaction interface using machine learning algorithm
description	Abstract Structural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .
format	article
author	Subhrangshu Das Saikat Chakrabarti
author_facet	Subhrangshu Das Saikat Chakrabarti
author_sort	Subhrangshu Das
title	Classification and prediction of protein–protein interaction interface using machine learning algorithm
title_short	Classification and prediction of protein–protein interaction interface using machine learning algorithm
title_full	Classification and prediction of protein–protein interaction interface using machine learning algorithm
title_fullStr	Classification and prediction of protein–protein interaction interface using machine learning algorithm
title_full_unstemmed	Classification and prediction of protein–protein interaction interface using machine learning algorithm
title_sort	classification and prediction of protein–protein interaction interface using machine learning algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ea38e476f8c744c8aa3426234ef73a99
work_keys_str_mv	AT subhrangshudas classificationandpredictionofproteinproteininteractioninterfaceusingmachinelearningalgorithm AT saikatchakrabarti classificationandpredictionofproteinproteininteractioninterfaceusingmachinelearningalgorithm
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Classification and prediction of protein–protein interaction interface using machine learning algorithm

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