Deep Reinforcement Learning for Modelling Protein Complexes

Structure prediction of large protein complexes (a.k.a., protein multimer mod-elling, PMM) can be achieved through the one-by-one assembly using provideddimer structures and predicted docking paths. However, existing PMM methodsstruggle with vast search spaces and generalization challenges: (1) The assemblyof a N -chain multimer can be depicted using graph structured data, with eachchain represented as a node and assembly actions as edges. Thus the assemblygraph can be arbitrary acyclic undirected connected graph, leading to the com-binatorial optimization space of N^(N −2) for the PMM problem. (2) Knowledgetransfer in the PMM task is non-trivial. The gradually limited data availability asthe chain number increases necessitates PMM models that can generalize acrossmultimers of various chains. To address these challenges, we propose GAPN, aGenerative Adversarial Policy Network powered by domain-specific rewards andadversarial loss through policy gradient for automatic PMM prediction. Specifi-cally, GAPN learns to efficiently search through the immense assembly space andoptimize the direct docking reward through policy gradient. Importantly, we de-sign a adversarial reward function to enhance the receptive field of our model. Inthis way, GAPN will simultaneously focus on a specific batch of multimers andthe global assembly rules learned from multimers with varying chain numbers.Empirically, we have achieved both significant accuracy (measured by RMSDand TM-Score) and efficiency improvements compared to leading complex mod-eling software. GAPN outperforms the state-of-the-art method (MoLPC) with upto 27% improvement in TM-Score, with a speed-up of 600×.