ESMRank：用深度突变扫描数据学习排序预测错义变异效应
ESMRank: learning-to-rank prediction of missense variant effects from deep mutational scanning data
研究提出 ESMRank，一种基于序列的学习排序预测器，用于预测错义变异的功能效应。作者先提出“variant soundness”这一重叠感知的排序融合分数，利用部分重叠的深度突变扫描实验之间的一致性，在蛋白内得到不依赖具体实验的突变耐受排序；在约 1100 个 MAVE、超过 200 万个错义变异上，该信号富集了结构稳定性约束。ESMRank 结合 ESM2 表示与理化描述符，在严格的蛋白层面划分下，于 ProteinGym 和 Domainome 中与丰度、折叠和稳定性相关的实验上优于常用预测器。
来源：Molecular Systems Biology · doi.org
Predicting the functional effects of missense variants is limited both by predictor performance and by the lack of principled strategies to integrate heterogeneous multiplexed assays of variant effect (MAVEs), including deep mutational scanning, into transferable sequence-function models. Here, we show that redundancy among partially overlapping deep mutational scanning experiments, often treated as noise, yields a consensus landscape of mutational constraint. We introduce “variant soundness”, an overlap-aware rank-fusion score that uses cross-experiment consistency to derive an assay-agnostic ordering of mutational tolerance within proteins. Applied to ~1100 MAVEs spanning over two million missense variants, this approach reveals a mutational landscape enriched for structural stability constraints. Guided by this integrated signal, we develop ESMRank, a sequence-based learning-to-rank predictor combining ESM2 representations with physicochemical descriptors. Under strict protein-level partitioning, ESMRank outperforms widely used predictors across abundance-, folding-, and stability-related assays in ProteinGym and Domainome. Predicted deleterious variants are enriched for ClinVar pathogenicity and, in CFTR, correlate with folding efficiency, channel activity, and pharmacological responsiveness. Together, these results turn assay redundancy into a scalable resource for interpretable missense variant prediction.