首页|A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

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The identification of protein-protein interaction(PPI)sites is essential in the research of protein function and the discovery of new drugs.So far,a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites.However,existing methods suffer from the low predictive accuracy or the limited scope of application.Specifically,some methods learned only global or local sequential features,leading to low predictive accuracy,while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information.There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites.Herein,a novel ensemble framework for PPI sites prediction,EnsemPPIS,was therefore proposed based on transformer and gated convolutional networks.EnsemPPIS can effectively capture not only global and local patterns but also residue interactions.Specifically,EnsemPPIS was unique in(a)extracting residue interactions from protein sequences with transformer and(b)further integrating global and local sequential features with the ensemble learning strategy.Compared with various existing methods,EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks.Moreover,pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information.The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

Minjie Mou、Ziqi Pan、Zhimeng Zhou、Lingyan Zheng、Hanyu Zhang、Shuiyang Shi、Fengcheng Li、Xiuna Sun、Feng Zhu

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College of Pharmaceutical Sciences,The Second Affiliated Hospital,Zhejiang University School of Medicine,National Key Laboratory of Advanced Drug Delivery and Release Systems,Zhejiang University,Hangzhou 310058,China

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare,Hangzhou 330110,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Zhejiang ProvinceLeading Talent of the"Ten Thousand Plan"—National High-Level Talents Special Supports Plan of ChinaNational Key R&D Program of ChinaKey R&D Program of Zhejiang Province"Double Top-Class"Universities ProjectsFundamental Research Funds for Central UniversityAlibaba-Zhejiang University Joint Research Center Future Digital Healthcare,Westlake Laboratory(Westlake Laboratory of Life ScieAlibaba Cloud,and Information Technology Center of Zhejiang UniversityFunds for the open access chargeNatural Science Foundation of Zhejiang Province

82373790U19092082222010200181872798LR21H3000012022YFC34005012020C03010181201*1942321012018QNA7023LR21H300001

2024

10.34133/research.0240

研究(英文)

研究(英文)

CSTPCD
ISSN：
年,卷(期)：2024.2024(2)
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