The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. Nonetheless, remarkable changes in associated atomic sites and binding pose can provide vital information in understanding the process of drug binding. Based on this mechanism, molecular dynamics (MD) simulations were invented as a useful tool for investigating the dynamic properties of a molecular system. However, the computational expenditure limits the growth and application of protein trajectory-related studies, thus hindering the possibility of supervised learning. To tackle this obstacle, we present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two specially designed self-supervised learning tasks: an atom-level prompt-based denoising generative task and a conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen, i.e., the binding affinity prediction and the ligand efficacy prediction, to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. We observe a huge improvement from current state-of-the-art methods, with a decrease of 4.3\% in RMSE for the binding affinity problem and an average increase of 13.8\% in AUROC and AUPRC for the ligand efficacy problem. The results demonstrate valuable insight into a strong correlation between the magnitude of conformation's motion in the 3D space (i.e., flexibility) and the strength with which the ligand binds with its receptor.
翻译:最新的生物研究结果表明,传统的运动性“锁和钥匙”理论并不普遍适用,因为受体和螺旋在不断移动。然而,相关原子点和约束面的显著变化可以提供理解药物约束过程的重要信息。基于这一机制,分子动态模拟被发明为调查分子系统动态特性的有用工具。然而,计算支出限制了蛋白轨相关研究的增长和应用,从而阻碍了监督学习的可能性。为了克服这一障碍,我们根据修改的变异性图表匹配网络(EQMN), 相关原子点和约束面的装配面的显著变化为理解药物约束过程提供了至关重要的信息。基于此机制,分子动态模拟(MDM)的模拟过程模拟(MD)是一个用于调查分子系统动态特性的快速脱色任务,一个符合要求的剪辑任务,一个具有非常精细分辨率的MDRT轨的灵活信息。 ProtalmMD(Protreality)可以让电算网络能够捕捉到与变异性变异性平的当前动态的精确性运动,一个在MDL的精确度和直径直径比值的精确度测试中, 。