ThermoRL:Structure-Aware Reinforcement Learning for Protein Mutation Design to Enhance Thermostability

TL;DR

ThermoRL is a reinforcement learning framework utilizing graph neural networks to design protein mutations that improve thermostability, surpassing traditional methods in efficiency and accuracy, with strong generalizability to unseen proteins.

Contribution

The paper introduces ThermoRL, a novel RL-based approach combining GNNs and hierarchical Q-learning for protein mutation design targeting thermostability enhancement.

Findings

ThermoRL achieves higher or comparable rewards than baseline methods.

It effectively filters destabilizing mutations and identifies stabilizing ones.

ThermoRL generalizes well to unseen proteins, detecting key mutation sites.

Abstract

Designing mutations to optimize protein thermostability remains challenging due to the complex relationship between sequence variations, structural dynamics, and thermostability, often assessed by \delta\delta G (the change in free energy of unfolding). Existing methods rely on experimental random mutagenesis or prediction models tested with pre-defined datasets, using sequence-based heuristics and treating enzyme design as a one-step process without iterative refinement, which limits design space exploration and restricts discoveries beyond known variations. We present ThermoRL, a framework based on reinforcement learning (RL) that leverages graph neural networks (GNN) to design mutations with enhanced thermostability. It combines a pre-trained GNN-based encoder with a hierarchical Q-learning network and employs a surrogate model for reward feedback, guiding the RL agent on where (the position) and which (mutant amino acid) to apply for enhanced thermostability. Experimental results show that ThermoRL achieves higher or comparable rewards than baselines while maintaining computational efficiency. It filters out destabilizing mutations and identifies stabilizing mutations aligned with experimental data. Moreover, ThermoRL accurately detects key mutation sites in unseen proteins, highlighting its strong generalizability. This RL-guided approach powered by GNN embeddings offers a robust alternative to traditional protein mutation design.