BAnG: Bidirectional Anchored Generation for Conditional RNA Design
This addresses a critical problem in computational biology for researchers by enabling RNA design without extensive prior data, though it appears incremental as it builds on generative approaches.
The paper tackled the challenge of designing RNA molecules that interact with specific proteins without requiring known interacting sequences or detailed structural knowledge, and developed RNA-BAnG, a deep learning model that generated RNA sequences for protein interactions, demonstrating effectiveness in biological evaluations.
Designing RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Existing computational approaches require a substantial amount of previously known interacting RNA sequences for each specific protein or a detailed knowledge of RNA structure, restricting their utility in practice. To address this limitation, we develop RNA-BAnG, a deep learning-based model designed to generate RNA sequences for protein interactions without these requirements. Central to our approach is a novel generative method, Bidirectional Anchored Generation (BAnG), which leverages the observation that protein-binding RNA sequences often contain functional binding motifs embedded within broader sequence contexts. We first validate our method on generic synthetic tasks involving similar localized motifs to those appearing in RNAs, demonstrating its benefits over existing generative approaches. We then evaluate our model on biological sequences, showing its effectiveness for conditional RNA sequence design given a binding protein.