用Transformer模型设计能精准结合线性表位的肽段，助力蛋白药物研发。
Computational design of target-specific linear peptide binders with TransformerBeta
- 将肽段设计类比为语言翻译，用Transformer架构预测β折叠相互作用
- 构建大规模稳定二级结构肽对库，实现高精度序列生成与结合预测
- 输出具有可解释物理化学特性的候选肽段，适合实验验证
针对特定线性表位的肽段结合物的计算预测与设计在生物医学研究中至关重要，但因表位高度动态且实验解析的结合数据稀缺而极具挑战。为此，我们利用新发布的AlphaFold预测结构，构建了前所未有的大规模稳定二级结构（β折叠）肽对库。基于Transformer架构开发出名为TransformerBeta的机器学习方法，将肽段设计类比为语言翻译任务。该方法能准确预测特定β链相互作用，生成具有β折叠样分子特性的序列，并捕捉可解释的理化作用模式。由此可提出针对性候选结合物，供实验验证以指导蛋白质设计。
原文摘要 · Abstract (English)
The computational prediction and design of peptide binders targeting specific linear epitopes is crucial in biological and biomedical research, yet it remains challenging due to their highly dynamic nature and the scarcity of experimentally solved binding data. To address this problem, we built an unprecedentedly large-scale library of peptide pairs within stable secondary structures (beta sheets), leveraging newly available AlphaFold predicted structures. We then developed a machine learning method based on the Transformer architecture for the design of specific linear binders, in analogy to a language translation task. Our method, TransformerBeta, accurately predicts specific beta strand interactions and samples sequences with beta sheet-like molecular properties, while capturing interpretable physico-chemical interaction patterns. As such, it can propose specific candidate binders targeting linear epitope for experimental validation to inform protein design.
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