基于序列预测蛋白质互作，助力新药靶点发现与设计
Sequence-based protein-protein interaction prediction and its applications in drug discovery
- 利用序列数据与Transformer模型预测蛋白质相互作用
- 可辅助识别疾病相关靶点并指导治疗性多肽/抗体设计
- 适合药物研发、系统生物学研究者参考
异常的蛋白质-蛋白质相互作用（PPI）是多种人类疾病的根源，破坏这些有害互作成为极具前景的治疗策略。近年来，随着深度学习和自然语言处理的发展，基于序列的PPI预测方法取得显著进展。本文综述了当前最先进的序列型PPI预测方法，探讨其在靶点识别和药物发现中的应用。首先介绍常用训练数据来源及数据质量提升技术；随后系统梳理传统相似性方法与深度学习方法，尤其关注Transformer架构的应用；最后展示其在系统级蛋白组学分析、靶点发现以及治疗性多肽和抗体设计中的实际案例，并强调具备PPI感知能力的药物发现模型在加速新药开发方面的潜力。
原文摘要 · Abstract (English)
Aberrant protein-protein interactions (PPIs) underpin a plethora of human diseases, and disruption of these harmful interactions constitute a compelling treatment avenue. Advances in computational approaches to PPI prediction have closely followed progress in deep learning and natural language processing. In this review, we outline the state-of the-art for sequence-based PPI prediction methods and explore their impact on target identification and drug discovery. We begin with an overview of commonly used training data sources and techniques used to curate these data to enhance the quality of the training set. Subsequently, we survey various PPI predictor types, including traditional similarity-based approaches, and deep learning-based approaches with a particular emphasis on the transformer architecture. Finally, we provide examples of PPI prediction in systems-level proteomics analyses, target identification, and design of therapeutic peptides and antibodies. We also take the opportunity to showcase the potential of PPI-aware drug discovery models in accelerating therapeutic development.
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