CIR-DDG：与主干无关的抗体-抗原亲和力变化的残差校正，带有显式跨链几何结构
CIR-DDG: backbone-agnostic residual correction of antibody-antigen affinity changes with explicit cross-chain geometry
AI总结：
本文提出与主干无关的CIR-DDG残差适配器，结合显式跨链几何描述符校正抗体-抗原亲和力变化预测，在SKEMPI 2.0及SARS-CoV-2 RBD-ACE2基准上均实现性能提升，且泛化能力良好。
AI中文摘要：
研究背景：准确预测突变诱导的蛋白质-蛋白质结合自由能变化对抗体亲和力成熟至关重要，但稀缺的标签与复杂的界面几何结构限制了模型的泛化能力；异构预测器处理三维复合物时，可能无法在最终标量输出中保留与突变最相关的跨链信号。研究方法：本文提出CIR-DDG，一种轻量型残差适配器，将固定的基础预测值与22种可解释的描述符结合，这些描述符涉及跨链距离、接触密度及位点-伴侣上下文。实验设置与结果：在SKEMPI 2.0的343个复合物测量数据上进行的五折交叉验证实验中，CIR-DDG在所有6种测试主干模型上均提升了抗体-抗原界面突变的预测性能：斯皮尔曼相关系数提升0.0346至0.1296，均方根误差（RMSE）降低0.0074至0.0408 kcal/mol；交叉验证探测、等容量对照实验及特征消融实验均支持显式几何结构的互补作用；在包含3669个替换的独立SARS-CoV-2 RBD-ACE2深度突变扫描基准上，经折叠特定适配器无需重新训练即可迁移，所有4种可评估主干模型的绝对界面斯皮尔曼相关系数提升0.026至0.081，表明所学几何校正可泛化至SKEMPI热力学测量之外的场景。可用性：CIR-DDG可通过指定网址获取。
英文摘要：
Motivation: Accurate prediction of mutation-induced protein--protein binding free-energy changes is important for antibody affinity maturation, yet scarce labels and complex interface geometry limit generalization. Heterogeneous predictors may process three-dimensional complexes without preserving the cross-chain signals most relevant to a mutation in their final scalar output. Results: We introduce CIR-DDG, a lightweight residual adapter that combines a fixed base prediction with 22 interpretable descriptors of cross-chain distance, contact density and site--partner context. In complex-level five-fold evaluation on SKEMPI 2.0 measurements from 343 complexes, CIR-DDG improved all six tested backbones on antibody--antigen interface mutations: Spearman correlation increased by 0.0346--0.1296, while RMSE decreased by 0.0074--0.0408\kcalmol. Cross-validated probing, equal-capacity controls and feature ablations support the complementarity of explicit geometry. On an independent SARS-CoV-2 RBD--ACE2 deep-mutational-scan benchmark of 3669 substitutions, the fold-specific adapters transferred without any retraining: the absolute interface Spearman correlation increased by 0.026--0.081 for all four evaluable backbones, showing that the learned geometric correction generalizes beyond SKEMPI thermodynamic measurements. Availability and implementation: CIR-DDG is available at https://github.com/ecnuabmlab/CIR-ddG.
