Debswapna Bhattacharya

1. Introduction Click to copy section linkSection link copied! Proteins perform essential biological functions, from structural support to enzyme catalysis, making them central to therapeutic applications. Recent breakthroughs in structure prediction, exemplified by AlphaFold3, (1) RFDiffusion, (2) and Boltz-2 (3) have revolutionized structural modeling capabilities.

Abstract Despite the groundbreaking advances in deep learning-enabled methods for biomolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging owing to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. Here we introduce RNAbpFlow, a sequence- and base pair-conditioned SE(3)-equivariant flow-matching model for generating RNA 3D structural ensembles.

Abstract Motivation: Protein side-chain packing (PSCP), the problem of predicting side-chain conformation given a fixed backbone structure, has important implications in modeling of structures and interactions. However, despite the groundbreaking progress in protein structure prediction pioneered by AlphaFold, the existing PSCP methods still rely on experimental inputs, and do not leverage AlphaFold-predicted backbone coordinates to enable PSCP at scale.