Deep Reinforcement Learning for Modelling Protein Complexes
Authors: Ziqi Gao,Tao Feng,Jiaxuan You,Chenyi Zi,Yan Zhou,Chen Zhang,Jia Li
Presentation: ICLR 2024,Poster

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