首页|Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations

Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations

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  • NETL
  • NSTL
  • 万方数据
Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases.Limited by the availability of experimental data on the consequences of protein interaction,most existing methods focus on building models to predict changes in protein binding affinity.Here,we introduced MIPPI,an end-to-end,interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx.MIPPI was specifically trained to determine the types of variant impact(increasing,decreasing,disrupting,and no effect)on protein-protein interactions.We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights,which exhibit correlations with the amino acids interacting with the variant.Moreover,we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations.Finally,we experimentally validated the functional impact of several variants identified in patients with such disorders.Overall,MIPPI emerges as a versatile,robust,and interpretable model,capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

Zhe Liu、Wei Qian、Wenxiang Cai、Weichen Song、Weidi Wang、Dhruba Tara Maharjan、Wenhong Cheng、Jue Chen、Han Wang、Dong Xu、Guan Ning Lin

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Shanghai Mental Health Center,Shanghai Jiao Tong University School of Medicine,School of Biomedical Engineering,Shanghai Jiao Tong University,Shanghai,China

Shanghai Key Laboratory of Psychotic Disorders,Shanghai,China

School of Information Science and Technology,Institute of Computational Biology,Northeast Normal University,Changchun,China

Department of Electrical Engineering and Computer Science,University of Missouri,Columbia,MO 65211,USA

Christopher S.Bond Life Sciences Center,University of Missouri,Columbia,MO 65211,USA

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STI 2030-Major ProjectsNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of ShanghaiMedical-Engineering Cross Foundation of Shanghai Jiao Tong UniversityMedical-Engineering Cross Foundation of Shanghai Jiao Tong UniversitySJTU Transmed Awards ResearchPaul K.and Diane Shumaker Endowment Fund at University of Missouri

2022ZD0209100819712928215061050621ZR1428600YG2022ZD026YG2023ZD2720220103

2024

10.34133/research.0219

研究(英文)

研究(英文)

CSTPCD
ISSN：
年,卷(期)：2024.2024(2)
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