4.5 Article

Toward the Complete Functional Characterization of a Minimal Bacterial Proteome

期刊

JOURNAL OF PHYSICAL CHEMISTRY B
卷 126, 期 36, 页码 6820-6834

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.2c04188

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资金

  1. NSF [MCB-1840320, MCB-1818344]
  2. NSF Physics Frontier Center [PHY1430124]
  3. NSF Physics of Living Systems (PoLS) Initiative NSF [PHY-2014027]
  4. International Human Frontier Science Program Organization (HFSPO) CrossDisciplinary Fellowship [LT000901/2021-C]

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In this study, a whole-cell kinetic model of JCVI-syn3A was presented, and computational analyses were applied to elucidate the functions of several previously uncharacterized genes. Characterization of these genes can enhance the predictive power of the cell model and deepen our understanding of biophysical processes and pathways at the cell scale.
Recently, we presented a whole-cell kinetic model of the genetically minimal bacterium JCVI-syn3A that described the coupled metabolic and genetic information processes and predicted behaviors emerging from the interactions among these networks. JCVI-syn3A is a genetically reduced bacterial cell that has the fewest number and smallest fraction of genes of unclear function, with approximately 90 of its 452 protein-coding genes (that is less than 20%) unannotated. Further characterization of unclear JCVI-syn3A genes strengthens the robustness and predictive power of cell modeling efforts and can lead to a deeper understanding of biophysical processes and pathways at the cell scale. Here, we apply computational analyses to elucidate the functions of the products of several essential but previously uncharacterized genes involved in integral cellular processes, particularly those directly affecting cell growth, division, and morphology. We also suggest directed wet-lab experiments informed by our analyses to further understand these missing puzzle pieces that are an essential part of the mosaic of biological interactions present in JCVI-syn3A. Our workflow leverages evolutionary sequence analysis, protein structure prediction, interactomics, and genome architecture to determine upgraded annotations. Additionally, we apply the structure prediction analysis component of our work to all 452 protein coding genes in JCVI-syn3A to expedite future functional annotation studies as well as the inverse mapping of the cell state to more physical models requiring all-atom or coarse-grained representations for all JCVI-syn3A proteins.

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