Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted inter-chain contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) to build protein complex structures using inter-chain contacts as distance constraints. We rigorously tested DRLComplex on two standard datasets of homodimeric and heterodimeric protein complexes (i.e., the CASP-CAPRI homodimer and Std_32 heterodimer datasets) using both true and predicted interchain contacts as inputs. Utilizing true contacts as input, DRLComplex achieved high average TM-scores of 0.9895 and 0.9881 and a low average interface RMSD (I_RMSD) of 0.2197 and 0.92 on the two datasets, respectively. When predicted contacts are used, the method achieves TM-scores of 0.73 and 0.76 for homodimers and heterodimers, respectively. Our experiments find that the accuracy of reconstructed quaternary structures depends on the accuracy of the contact predictions. Compared to other optimization methods for reconstructing quaternary structures from inter-chain contacts, DRLComplex performs similar to an advanced gradient descent method and better than a Markov Chain Monte Carlo simulation method and a simulated annealing-based method, validating the effectiveness of DRLComplex for quaternary reconstruction of protein complexes.
翻译:利用预测到的链际接触,可以建立蛋白质综合体的顶部结构。然而,仅开发了少量方法来重建蛋白顶部结构。在这里,我们展示了一种基于代理人的自学方法,其基础是深度强化学习(DRLComplex),以利用链际接触作为距离限制,建立蛋白复合结构。我们严格测试了两个标准数据集的DRLComplex,它们是同父异父和异正态蛋白综合体(即CASP-CAPRI同父体和Std_32 heterodimer数据集),利用真实和预测到的链际接触作为投入。利用真正的接触,DRRComplex实现了0.895和0.9881的高平均值TM-芯,而RMSD(I_RMSD)以0.2197和0.92为基于两个数据集的低平均值。当使用预测到使用预测的接触时,该方法可以实现TM-lexlical-Crealal-real-real-remodition refilations refilation 结构的0.73-一个更精确性方法,用来进行同正正正正正正正正正正地的比地的比方法。