2024/06/17 by Luca Alessandro Silva, Silva, Luca Alessandro, Barthelemy Meynard-Piganeau +5 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Genetics, Bioinformatics, and Biomedical Research #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM) #RNA and protein synthesis mechanisms #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2406.11975
openalex publication_date 2024/06/17 · arxiv published 2024/06/17 · openalex created_date 2024/06/20 · arxiv updated 2025/05/29 · openalex updated_date 2026/07/28
We present InvMSAFold, an inverse folding method for generating protein sequences that is optimized for diversity and speed. For a given structure, InvMSAFold generates the parameters of a probability distribution over the space of sequences with pairwise interactions, capturing the amino acid covariances observed in Multiple Sequence Alignments (MSA) of homologous proteins. This allows for the efficient generation of highly diverse protein sequences while preserving structural and functional integrity. We show that this increased diversity in sampled sequences translates into greater variability in biochemical properties, highlighting the exciting potential of our method for applications such as protein design. The orders of magnitude improvement in sampling speed compared to existing methods unlocks new possibilities for high-throughput virtual screening.