Yang Yue, Yihua Cheng, Céline Marquet, Chenguang Xiao, Jingjing Guo, Shu Li, Shan He
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引用次数: 0
Abstract
Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a meta-learning framework designed to improve the adaptability of pretrained geometric models in such scenarios. To effectively boost the meta-learning optimization by injecting prior intersample distribution knowledge, three specially designed training sample cluster splitting patterns based on protein interaction interfaces are introduced. Additionally, MCGLPPI++ is equipped with an independent energy component which explicitly models interface nonbonded interaction energies closely related to the strengths of PPIs. To validate our approach, we curate a new data set featuring a challenging test cluster of T-cell receptors binding to antigenic peptide-MHC molecules (TCR-pMHC). Experimental results show that geometric models enhanced by the MCGLPPI++ framework achieve significantly more robust binding affinity predictions after fine-tuning on a few samples from this novel cluster compared to their vanilla counterparts, which demonstrates the effectiveness of the framework.
IF 6.3 4区 医学Systematic ReviewsPub Date : 2022-10-26DOI: 10.1186/s13643-022-02099-9
Alexandria Bennett, Andrew Beck, Nicole Shaver, Roland Grad, Allana LeBlanc, Heather Limburg, Casey Gray, Ahmed Abou-Setta, Scott Klarenbach, Navindra Persaud, Guylène Thériault, Brett D Thombs, Keith J Todd, Neil Bell, Philipp Dahm, Andrew Loblaw, Lisa Del Giudice, Xiaomei Yao, Becky Skidmore, Elizabeth Rolland-Harris, Melissa Brouwers, Julian Little, David Moher
IF 0 medRxiv - OncologyPub Date : 2024-05-31DOI: 10.1101/2024.05.29.24308154
Alexandria Bennett, Nicole Shaver, Niyati Vyas, Faris Almoli, Robert Pap, Andrea Douglas, Taddele Kibret, Becky Skidmore, Martin Yaffe, Anna Wilkinson, Jean M. Seely, Julian Little, David Moher
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
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