Pharmacy Faculty, University of Barcelona, Av. de Joan XXIII 27-31, 08028 Barcelona, Spain.
Baker Heart and Diabetes Institute, Melbourne 3004, Australia.
Int J Mol Sci. 2022 Apr 26;23(9):4756. doi: 10.3390/ijms23094756.
The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline.
配体与靶标结合的预测是基于结构的药物设计 (SBDD) 方法的重要步骤。分子对接是预测配体与生物大分子受体结合模式并量化它们相互互补性的标准工具，在药物设计中有多种应用。然而，对接程序并不总是能找到正确的解决方案，要么是因为它们没有被采样，要么是因为评分函数不准确。在实际情况下量化对接性能对于理解其局限性、管理预期和指导未来的发展至关重要。在这里，我们展示了一个经过连续评估配体构象预测 (CELPP) 挑战赛验证的全自动构象预测流水线。鉴于对接方法的固有局限性，我们设计了一种自动挖掘和利用现有数据的策略，尽可能定义经验约束来指导对接过程。我们证明，该流水线能够为大多数提出的靶标生成预测，并且与晶体结构相比，生成的构象具有较低的 RMSD 值。综上所述，我们的流水线突出了蛋白质-配体复合物自动预测中的一些主要挑战，这些挑战将在流水线的未来版本中得到解决。
