Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

Abstract Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signa...

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Autores principales:	Yang Li, Zheng Wang, Li-Ping Li, Zhu-Hong You, Wen-Zhun Huang, Xin-Ke Zhan, Yan-Bin Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63b30bfdcc1648f492e40523cdd36dde
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spelling	oai:doaj.org-article:63b30bfdcc1648f492e40523cdd36dde2021-12-02T17:08:36ZRobust and accurate prediction of protein–protein interactions by exploiting evolutionary information10.1038/s41598-021-96265-z2045-2322https://doaj.org/article/63b30bfdcc1648f492e40523cdd36dde2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96265-zhttps://doaj.org/toc/2045-2322Abstract Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.Yang LiZheng WangLi-Ping LiZhu-Hong YouWen-Zhun HuangXin-Ke ZhanYan-Bin WangNature 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 Yang Li Zheng Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Xin-Ke Zhan Yan-Bin Wang Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
description	Abstract Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.
format	article
author	Yang Li Zheng Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Xin-Ke Zhan Yan-Bin Wang
author_facet	Yang Li Zheng Wang Li-Ping Li Zhu-Hong You Wen-Zhun Huang Xin-Ke Zhan Yan-Bin Wang
author_sort	Yang Li
title	Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
title_short	Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
title_full	Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
title_fullStr	Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
title_full_unstemmed	Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
title_sort	robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/63b30bfdcc1648f492e40523cdd36dde
work_keys_str_mv	AT yangli robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT zhengwang robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT lipingli robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT zhuhongyou robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT wenzhunhuang robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT xinkezhan robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation AT yanbinwang robustandaccuratepredictionofproteinproteininteractionsbyexploitingevolutionaryinformation
_version_	1718381555601113088

Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

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