Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search.

ABSTRACT: RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and

SUBMITTER: Mao K 

PROVIDER: S-EPMC8347524 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature