Steering Protein Family Design through Profile Bayesian Flow
Jingjing Gong Yu Pei Siyu Long Yuxuan Song Zhe Zhang Wenhao Huang Ziyao Cao Shuyi Zhang Hao Zhou Wei-Ying Ma
Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose ProfileBFN, the Profile Bayesian Flow Networks, for specifically generative modeling of protein families. ProfileBFN extends the discrete Bayesian Flow Network from an MSA profile perspective, which can be trained on single protein sequences by regarding it as a degenerate profile, thereby achieving efficient protein family design by avoiding large-scale MSA data construction and training. Empirical results show that ProfileBFN has a profound understanding of proteins. When generating diverse and novel family proteins, it can accurately capture the structural characteristics of the family. The enzyme produced by this method is more likely than the previous approach to have the corresponding function, offering better odds of generating diverse proteins with the desired functionality.
protein family generation, homologous protein generation, protein design, bayesian flow