利用AlphaProteo从头设计高亲和力蛋白结合剂
De novo design of high-affinity protein binders with AlphaProteo
AI总结：
针对无需多轮实验的高亲和力蛋白结合剂从头设计难题，该研究提出机器学习模型家族AlphaProteo，在7种靶蛋白上实现比现有最优方法高3-300倍的结合亲和力与更高成功率，可经一轮筛选生成即用型结合剂。
摘要：
蛋白结合蛋白的计算设计是生物医学研究和生物技术领域具有广泛应用价值的基础能力。近期方法在部分靶蛋白上取得进展，但无需多轮实验测试即可按需创建高亲和力结合剂仍是未解决的难题。本技术报告介绍了蛋白设计机器学习模型家族AlphaProteo，并详述其在从头结合剂设计问题上的性能。在7种靶蛋白上，AlphaProteo的结合亲和力比现有最优方法提升3至300倍，实验成功率也更高。结果表明，仅需一轮中等通量筛选且无需后续优化，AlphaProteo就能生成可直接用于诸多研究场景的“即用型”结合剂。
英文摘要：
Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.
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