Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.

ABSTRACT: Information derived from metagenome sequences through deep-learning techniques has significantly improved the accuracy of template free protein structure modeling. However, most of the deep learning-based modeling studies are based on blind sequence database searches and suffer from low efficiency in computational resource utilization and model construction, especially when the sequence library becomes prohibitively large. We proposed a MetaSource model built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil, and Fermentor) to decode the inherent linkage of microbial niches with protein homologous families. Large-scale protein family folding experiments on 8,700 unknown Pfam families showed that a microbiome targeted approach with multiple sequence alignment cons

SUBMITTER: Yang P 

PROVIDER: S-EPMC8670487 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature