发表机构
Pfizer（辉瑞）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可审计代数计数场ACF，从Apo结构预测隐秘口袋残基，无需序列搜索或语言模型，在外部集合上超过P2Rank，并提供可检查的预测路径。
AI 中文摘要
隐秘配体结合口袋在实验确定的Apo结构中并不明显，这使得从非结合受体几何结构中识别它们变得困难。一个补充性的挑战是使每次预测背后的结构测量和学习到的证据能够直接检查。我们引入了一种监督式代数计数场（ACF），用于从Apo结构预测隐秘口袋残基。ACF将显式的几何、物理化学和拓扑特征编译成紧凑的整数加权查找表。每个预测分数都可以从特征值、训练计数、表权重和空间聚合中重建，无需序列搜索、结构模板转移或推理时的蛋白质语言模型。我们在CryptoBench和两个锁定的外部集合上评估ACF，将排序性能与残基调用预算的影响分开。在一个包含57个后CryptoBench apo-holo单元的外部集合上，ACF的平均配对ROC-AUC比P2Rank高出+0.044（多重性调整后的95%置信区间[+0.010, +0.079]）。该优势依赖于数据集：官方折叠ROC-AUC和匹配预算的F1差异与P2Rank相比仍未解决，第二个外部评估未确认添加结构特征带来的增益。因此，ACF提供了一个紧凑的预测器，具有外部验证的信号和从结构测量与训练计数到残基分数的可检查路径。
英文摘要
Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry. A complementary challenge is to make the structural measurements and learned evidence behind each prediction directly inspectable. We introduce a supervised algebraic counting field (ACF) for predicting cryptic-pocket residues from apo structures. ACF compiles explicit geometric, physicochemical, and topological features into compact, integer-weighted lookup tables. Each prediction score can be reconstructed from feature values, training counts, table weights, and spatial aggregation, without sequence search, structural-template transfer, or a protein language model at inference. We evaluate ACF on CryptoBench and two locked external collections, separating ranking performance from the effects of residue-calling budgets. On an external set of 57 post-CryptoBench apo-holo units, ACF exceeded P2Rank by +0.044 in mean paired ROC-AUC (multiplicity-adjusted 95% CI [+0.010, +0.079]). The advantage was dataset-dependent: official-fold ROC-AUC and matched-budget F1 differences against P2Rank remained unresolved, and a second external evaluation did not confirm gains from added structural features. ACF thus provides a compact predictor with externally validated signal and an inspectable path from structural measurements and training counts to residue scores.
CommentsAccepted for publication in Pacific Symposium on Biocomputing (PSB) 2027
