Structure-aware Multi-task Collaborative Learning: a multi-task collaborative learning framework for peptide-protein interaction prediction based on structure-aware protein language models

He, Siyi

doi:10.1093/bib/bbag178

2026-03-01

DOI：10.1093/bib/bbag178

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肽类药物指向一种有前景的新疗法。精确预测肽与蛋白质之间的相互作用对于功能肽的发现和设计至关重要。尽管已提出多种计算方法用于此目的，但构建准确且稳健的预测模型仍具挑战性。本研究引入了一种结构感知的多任务协同学习（SaMCL）框架，用于检测肽与蛋白质之间的相互作用。据我们所知，SaMCL是首个能够对肽和蛋白质同时进行多层级二元相互作用和结合域预测的方法。实验结果表明，SaMCL在预测准确性和泛化能力方面优于几种当前先进方法，为生物分子相互作用建模提供了新范式。

Keyword:

protein-peptide interaction

structure-aware protein language model

collaborative learning framework

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Briefings in Bioinformatics

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7.7

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5.8K

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shenzhen university

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4.6W

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Shenzhen Technology University

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guangdong university of technology

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