RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

TL;DR

This paper introduces RDesign, a hierarchical, data-efficient learning framework for RNA tertiary structure-based design, leveraging contrastive learning and secondary structure priors to improve design accuracy with limited data.

Contribution

The study presents a novel hierarchical contrastive learning approach for RNA design, incorporating secondary structure priors and a new benchmark dataset, addressing data scarcity and structural complexity.

Findings

Effective in leveraging limited data for RNA design

Outperforms existing methods in accuracy and reliability

Provides a new benchmark dataset for future research

Abstract

While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity. Moreover, direct transplantation of protein design methodologies into RNA design fails to achieve satisfactory outcomes although sharing similar structural components. In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. By constraining data representations within a limited hyperspherical space, the intrinsic relationships between data points could be explicitly imposed. Moreover, we incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks. The source code and benchmark dataset are available at https://github.com/A4Bio/RDesign.