时间：2026-04-07
本文（VenusMutHub—A benchmark for protein mutation effect prediction）原载Acta Pharmaceutica Sinica B，由四川大学李国菠教授等科研人员创作，系四川智慧社会智能治理重点实验室智慧法治研究所的系列学术成果。本网站后续将持续分享实验室的系列学术成果，欢迎大家阅读。
Protein engineering has become a cornerstone in numerous fields, from biocatalysis to biological drug development, offering innovative solutions with enhanced or novel protein functions. One of the critical challenges in this field is the prediction of protein mutation effects, a task of great importance for both drug development and precision medicine. The large mutational sequence space poses a significant challenge for traditional experimental approaches, underscoring the importance of computational strategies in protein engineering. Recent advancements in zero-shot computational methods, such as physics-based approaches and machine learning models, have substantially improved the prediction of protein mutation effects. However, while these models perform well on large-scale mutation datasets, they face limitations when applied to real-world scenarios where highthroughput screening is not feasible or when specific biochemical properties need to be considered. The practical challenge lies in the small-scale, experimentally validated datasets often used in protein engineering, particularly in directed evolution efforts. These datasets are more reflective of real-world constraints, where experimental data is sparse and derived from a limited set of mutations. Thus, predicting mutation effects in these small-scale datasets, which more closely resemble the conditions protein engineers face, becomes a crucial task.
Junlin Yu, Guobo Li. VenusMutHub—A benchmark for protein mutation effect prediction. Acta Pharmaceutica Sinica B, 2025,15(5): 2805-2807. （论文下载）
