| Literature DB >> 33393786 |
Chengxin Zhang1, Wei Zheng1, Micah Cheng2, Gilbert S Omenn1,3, Peter L Freddolino1,4, Yang Zhang1,4.
Abstract
When the JCVI-syn3.0 genome was designed and implemented in 2016 as the minimal genome of a free-living organism, approximately one-third of the 438 protein-coding genes had no known function. Subsequent refinement into JCVI-syn3A led to inclusion of 16 additional protein-coding genes, including several unknown functions, resulting in an improved growth phenotype. Here, we seek to unveil the biological roles and protein-protein interaction (PPI) networks for these poorly characterized proteins using state-of-the-art deep learning contact-assisted structure prediction, followed by structure-based annotation of functions and PPI predictions. Our pipeline is able to confidently assign functions for many previously unannotated proteins such as putative vitamin transporters, which suggest the importance of nutrient uptake even in a minimized genome. Remarkably, despite the artificial selection of genes in the minimal syn3 genome, our reconstructed PPI network still shows a power law distribution of node degrees typical of naturally evolved bacterial PPI networks. Making use of our framework for combined structure/function/interaction modeling, we are able to identify both fundamental aspects of network biology that are retained in a minimal proteome and additional essential functions not yet recognized among the poorly annotated components of the syn3.0 and syn3A proteomes.Entities:
Keywords: JCVI-syn3.0 minimal genome; JCVI-syn3A; computational function annotation; deep learning; essential proteins; protein−protein interaction; structure prediction
Year: 2021 PMID: 33393786 PMCID: PMC7867644 DOI: 10.1021/acs.jproteome.0c00359
Source DB: PubMed Journal: J Proteome Res ISSN: 1535-3893 Impact factor: 4.466