发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
StabilityArc通过共享RoPE变换器将ESMC-600M嵌入映射为替换效应矩阵，在66次留一蛋白质评估中达0.7134 Spearman相关，并作为Kermut先验提升至0.8280，实现跨蛋白质稳定性预测。
AI 中文摘要
每种蛋白质都有其独特的稳定性景观，但突变的物理后果受制于重复出现的生化约束。我们测试了一个共享解码器，该解码器在来自不同蛋白质的测量数据上训练，能否解读未见过靶标中的这些约束，从而为初始实验轮次预筛选实现跨蛋白质转移。我们提出了StabilityArc，它将冻结的ESMC-600M残基表示通过共享的RoPE变换器映射为一个Lx20的替换效应矩阵；一个对称的、接触感知的残差有助于预测同时替换中的上位效应。在覆盖134,794个ProteinGym变体的66次严格留一蛋白质出评估中，StabilityArc实现了0.7134的Spearman相关性，比最强的零样本基线ProSST-2048（0.6526）高出0.0608。我们进一步探索了该方法的实用性，将得分作为Kermut的先验，在三种监督分割方案中实现了0.8280的Spearman相关性，优于Kermut报告的0.8167。
英文摘要
Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test whether a shared decoder, trained on measurements from diverse proteins, can interpret these constraints in an unseen target, enabling cross-protein transfer for initial experimental round prescreening. We present StabilityArc , which maps frozen ESMC-600M residue representations through a shared RoPE transformer to an Lx20 matrix of substitution effects; a symmetric, contact-aware residual aids in predicting epistasis in simultaneous substitutions. In 66 strict leave-one-protein-out evaluations covering 134,794 ProteinGym variants, StabilityArc achieves 0.7134 Spearman correlation, exceeding the strongest zero-shot baseline, ProSST-2048 (0.6526), by 0.0608. We further explore the utility of this method by providing the score as a prior for Kermut, achieving Spearman correlation of 0.8280 across three supervised split schemes, improving on Kermut's reported 0.8167.
CommentsAccepted to Representations for the Physical Sciences Workshop @ NeurIPS 2026; 9 pages, 1 figure, 2 tables
