Rationalized All-Atom Protein Design with Unified Multi-modal Bayesian Flow
Hanlin Wu, Yuxuan Song, Zhe Zhang, Zhilong Zhang, Hao Zhou, Wei-Ying Ma, Jingjing Liu
Designing functional proteins is a critical yet challenging problem due to the intricate interplay between backbone structures, sequences, and side-chains. Current approaches often decompose protein design into separate tasks, which can lead to accumulated errors, while recent efforts increasingly focus on all-atom protein design. However, we observe that existing all-atom generation approaches suffering from an information shortcut issue, where models inadvertently infer sequences from side-chain information, compromising their ability to accurately learn sequence distributions. To address this, we introduce a novel rationalized information flow strategy to eliminate the information shortcut. Furthermore, motivated by the advantages of Bayesian flows over differential equation–based methods, we propose the first Bayesian flow formulation for protein backbone orientations by recasting orientation modeling as an equivalent hyperspherical generation problem with antipodal symmetry. To validate, our method delivers consistently exceptional performance in both peptide and antibody design tasks.