发表机构
Harvard University; Johns Hopkins University（哈佛大学; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出利用LambdaLoss损失函数微调DFMDock以改进蛋白质-蛋白质对接姿态排序，新模型LambdaDockScore在CAPRI基准上优于EuDockScore，并提升抗体-抗原及大小结合界面复合物的排序性能。
AI 中文摘要
建模蛋白质-蛋白质相互作用需要能够对蛋白质-蛋白质复合物的潜在姿态（构象）进行排序的准确评分函数，以区分近天然姿态与错误姿态。在此，我们提出了一个通用框架，利用学习排序领域的LambdaLoss损失函数来改进蛋白质-蛋白质姿态排序及其他生物分子相互作用模型。我们通过使用LambdaLoss对DFMDock的能量预测头在从DIPS数据集衍生的290万个诱饵姿态的增强数据集上进行微调来测试该框架。在来自CAPRI评分集基准的目标上，我们微调后的排序模型LambdaDockScore在top-1和top-5预测中识别正确姿态的能力优于最先进的方法EuDockScore。LambdaDockScore还在对具有非常大或非常小结合界面的抗体-抗原复合物和蛋白质-蛋白质复合物的评分中，改进了基线DFMDock的排序性能。
英文摘要
Modeling protein-protein interactions requires accurate scoring functions that can rank potential poses (conformations) of a protein-protein complex to differentiate near-native poses from incorrect ones. Here, we propose a general framework for improving protein-protein pose ranking and other biomolecular interaction models using the LambdaLoss loss function from the Learning-to-Rank field. We test this framework by fine-tuning the energy prediction head of DFMDock with the LambdaLoss on an augmented dataset of 2.9M decoy poses derived from the DIPS dataset. On targets from the CAPRI score set benchmark, our fine-tuned ranking model LambdaDockScore is better at identifying correct poses in its top-1 and top-5 predictions compared to EuDockScore, a state-of-the-art method. LambdaDockScore also improves upon baseline DFMDock ranking performance for scoring antibody-antigen complexes and protein-protein complexes with very large or small binding interfaces.
