DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning
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DOI：
10.48550/arxiv.2205.13594
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发表时间：
2022-05
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通讯作者：
Elham Soltanikazemi;Rajashree Roy;Farhan Quadir;Nabin Giri;Alex Morehead;Jianlin Cheng
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作者：
Elham Soltanikazemi;Rajashree Roy;Farhan Quadir;Nabin Giri;Alex Morehead;Jianlin Cheng
预测的链间残基-残基接触可用于从头开始构建蛋白质络合物的四级结构。然而，利用预测的链间接触来重建蛋白质四级结构的方法还很少。在这里，我们提出了一种基于深度强化学习的基于智能体的自学习方法(DRLComplex)来构建以链间接触为距离约束的蛋白质复杂结构。我们在两个标准的同源和异源二聚体蛋白质复合体数据集(即CASP-Capri同源二聚体和STD_32异二聚体数据集)上严格测试了DRLComplex，使用真实和预测的链间接触作为输入。以真实接触为输入，DRLComplex在两个数据集上分别获得了0.9895和0.9881的高TM平均得分和0.2197和0.92RMSD的低平均界面RMSD。当使用预测接触时，该方法对同二聚体和异二聚体的TM得分分别为0.73和0.76。我们的实验发现，重建的四元结构的精度取决于接触预测的精度。与其他从链间接触重构四元结构的优化方法相比，DRLComplex的性能与改进的梯度下降法相似，优于马尔可夫链蒙特卡罗模拟法和基于模拟退火法，验证了DRLComplex用于蛋白质复合体四元重构的有效性。
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.