Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model

TL;DR

This paper introduces SOLD, a reinforcement learning framework that enhances RNA inverse folding by optimizing a latent diffusion model with multiple structural objectives, outperforming existing methods in accuracy and efficiency.

Contribution

We develop SOLD, a novel RL-based approach that refines latent diffusion models for RNA design, effectively handling complex, non-differentiable structural objectives.

Findings

SOLD outperforms baseline and state-of-the-art methods across all metrics.

The approach effectively balances multiple structural objectives.

Experimental results demonstrate improved structural accuracy in RNA design.

Abstract

RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.