Ugurlu Sadettin Y
Novexus Ltd, Antalya, 07058, Turkey.
Department of Materials Science and Engineering, Faculty of Engineering, Akdeniz University, Dumlupinar Bulvari, Antalya, 07058, Turkey.
J Comput Aided Mol Des. 2025 Jul 13;39(1):48. doi: 10.1007/s10822-025-00629-w.
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection-guided docking improves accuracy by narrowing focus to predicted pockets, but its effectiveness depends heavily on the quality of cavity detection tools. To overcome these limitations, we developed Consensus Blind Dock (CoBDock), a machine learning-based blind docking method that integrates molecular docking and cavity detection results to enhance binding site and pose prediction. Building on this, CoBDock-2 replaces traditional docking tools by extracting 1D numerical representations from protein, ligand, and interaction structural features, and applying advanced ensemble feature selection techniques. By evaluating 21 feature selection methods across 9,598 features, CoBDock-2 identifies key molecular characteristics of orthosteric binding sites. CoBDock-2 demonstrates consistent improvements over the original CoBDock across benchmark datasets (PDBBind v2020-general, MTi, ADS, DUD-E, CASF-2016), achieving 77% binding site identification accuracy (within 8 Å), 55% ligand pose prediction accuracy (RMSD 2 Å), a 19% reduction in the mean distance to ground truth ligands within the binding site, and an 18.5% decrease in the mean pose RMSD. Statistical analysis across the combined benchmark set confirms the significance of these improvements ( ). Notably, the Weighted Hybrid Feature Selection variant in CoBDock-2 further increases binding site accuracy to 79.8%, demonstrating the benefit of combining multimodel and ensemble feature selection strategies. Variability in predictions also decreased significantly, highlighting enhanced reliability and generalizability. Also, a low-bias hypothetical comparison with a state-of-the-art DiffDock + NMDN method was conducted to position CoBDock-2 relative to modern deep learning-based docking strategies.
识别正构结合位点并预测小分子亲和力仍然是虚拟筛选中的一项关键挑战。虽然盲目对接会探索整个蛋白质表面，但其精度受到巨大搜索空间的阻碍。腔检测引导对接通过将重点缩小到预测的口袋来提高准确性，但其有效性在很大程度上取决于腔检测工具的质量。为了克服这些限制，我们开发了共识盲目对接（CoBDock），这是一种基于机器学习的盲目对接方法，它整合了分子对接和腔检测结果，以增强结合位点和构象预测。在此基础上，CoBDock-2通过从蛋白质、配体和相互作用结构特征中提取一维数值表示，并应用先进的集成特征选择技术，取代了传统的对接工具。通过评估9598个特征上的21种特征选择方法，CoBDock-2识别了正构结合位点的关键分子特征。CoBDock-2在基准数据集（PDBBind v2020-general、MTi、ADS、DUD-E、CASF-2016）上相对于原始CoBDock表现出持续的改进，实现了77%的结合位点识别准确率（在8 Å范围内）、55%的配体构象预测准确率（RMSD ≤ 2 Å），结合位点内到真实配体的平均距离减少了19%，平均构象RMSD降低了18.5%。对组合基准集的统计分析证实了这些改进的显著性（ ）。值得注意的是，CoBDock-2中的加权混合特征选择变体将结合位点准确率进一步提高到79.8%，证明了结合多模型和集成特征选择策略的好处。预测的变异性也显著降低，突出了更高的可靠性和通用性。此外，还与一种先进的DiffDock + NMDN方法进行了低偏差假设比较，以将CoBDock-2相对于现代基于深度学习的对接策略进行定位。
