Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

AlphaFold2 and RoseTTAfold impress with their high accuracy
in
protein structure prediction. However, for structure-based virtual
screenings, not only the overall structure but especially the binding
sites need to be accurately predicted. In this work, the docking performance
for 66 targets with known ligands but without experimental structures
available in the protein data bank was elucidated. The results suggest
that using an experimental surrogate–ligand complex is often
superior over homology models, and only at low sequence identity to
the closest homologue AlphaFold2 structures show an equal performance.
The generally high fluctuation of receiver operating characteristic
area under the curve values obtained for different homology models
suggests that multiple combinations of docking programs and homology
models should be tested prior to prospective virtual screenings, and
in some cases post-processing of crude models might be necessary.

AlphaFold2与RoseTTAfold在蛋白质结构预测（protein structure prediction）领域凭借超高精度表现亮眼。然而在基于结构的虚拟筛选（structure-based virtual screening）任务中，不仅需要精准预测蛋白质的整体结构，结合位点的精准预测更为关键。本研究针对66个已知配体、但蛋白质数据银行（Protein Data Bank）中暂无实验结构的蛋白质靶点，对其分子对接性能（docking performance）进行了系统剖析。研究结果显示，采用实验获得的替代物-配体复合物建模，通常优于同源建模结构（homology models）；仅当目标序列与最近同源物的序列一致性较低时，AlphaFold2预测的结构方能达到与之相当的对接性能。不同同源建模方法所得的受试者工作特征曲线下面积（Receiver Operating Characteristic Area Under the Curve, ROC-AUC）值普遍波动较大，这提示在开展前瞻性虚拟筛选（prospective virtual screenings）前，应当对分子对接程序与同源建模方案的多种组合进行测试；在部分场景中，还需对粗建模结果进行后处理。