Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2025-01-27 Epub Date: 2025-01-07 DOI:10.1021/acs.jcim.4c01607
Yang Yue, Yihua Cheng, Céline Marquet, Chenguang Xiao, Jingjing Guo, Shu Li, Shan He
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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.

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元学习使复杂的簇特异性蛋白-蛋白相互作用的少量结合亲和力预测成为可能。
预测看不见的蛋白质复合物簇中的蛋白质-蛋白质相互作用(PPI)结合亲和力对于阐明复杂蛋白质相互作用和靶向筛选基于肽或蛋白质的药物至关重要。我们引入MCGLPPI++,这是一个元学习框架,旨在提高预训练几何模型在这种情况下的适应性。为了通过注入先验样本间分布知识有效地促进元学习优化,引入了三种专门设计的基于蛋白质交互界面的训练样本聚类分割模式。此外,MCGLPPI++还配备了一个独立的能量分量,明确地模拟了与ppi强度密切相关的界面非键相互作用能。为了验证我们的方法,我们整理了一个新的数据集,其中包括t细胞受体与抗原肽- mhc分子(TCR-pMHC)结合的具有挑战性的测试簇。实验结果表明,与普通模型相比,MCGLPPI++框架增强的几何模型在对该新聚类的几个样本进行微调后,获得了更强大的结合亲和力预测,这证明了框架的有效性。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
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. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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Issue Publication Information Issue Editorial Masthead Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery. Comprehensive Evaluation of End-Point Free Energy Methods in DNA-Ligand Interaction Predictions. Computational Modeling of the Enzymatic Achmatowicz Rearrangement by Heme-Dependent Chloroperoxidase: Reaction Mechanism, Enantiopreference, Regioselectivity, and Substrate Specificity.
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