Author:
Wang Chenran,Chen Yang,Zhang Yuan,Li Keqiao,Lin Menghan,Pan Feng,Wu Wei,Zhang Jinfeng
Abstract
AbstractProtein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu2+), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO42−), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.
Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference37 articles.
1. Zhang W, Bell EW, Yin M, Zhang Y. EDock: blind protein–ligand docking by replica-exchange monte carlo simulation. J Cheminform. 2020;12:1–17.
2. Zhang Y, Chen Y, Wang C, Lo CC, Liu X, Wu W, Zhang J. ProDCoNN: protein design using a convolutional neural network. Proteins Struct Funct Bioinform. 2020;88(7):819–29.
3. Bray S (2020). Protein-ligand docking (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/cheminformatics/tutorial.html
4. Grinter SZ, Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules. 2014;19(7):10150–76.
5. Fan J, Ailing F, Zhang L. Progress in molecular docking. Quant Biol. 2019;7(2):83–9. https://doi.org/10.1007/s40484-019-0172-y.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献