Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial ‘low-resolution’ docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10–15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the ‘high-resolution’ full atom refinement step.

RosettaLigand已被成功应用于预测蛋白质-小分子复合物的结合位姿（binding poses）。然而，RosettaLigand的对接流程在为小分子（配体，ligand）确定初始起始位姿时速度相对较慢，使其无法适用于虚拟高通量筛选（virtual High Throughput Screening，vHTS）。为克服这一局限，我们开发了一种全新的采样方法，用于在初始“低分辨率”对接步骤中将配体放置于蛋白质结合位点。该方法将配体位姿的平移与旋转调整整合至单个变换步骤中。新算法兼具更高的准确性与更优的时间效率。在包含43个蛋白质/配体复合物的基准数据集（benchmark set）中，对接成功率提升了10%~15%，将常规所需生成的模型数量从1000个缩减至150个。生成单个模型的平均耗时从50秒缩短至10秒。经测试，我们实现了高达30倍的有效速度提升，使得RosettaLigand可适用于中型配体文库的对接任务。我们证实，这种经过优化的配体初始放置方式，对于“高分辨率”全原子精化步骤中准确结合位点的成功预测至关重要。