Toward the Complete Functional Characterization of a Minimal Bacterial Proteome

Area of Science:

• Synthetic biology
• Genomics
• Computational biology

Background:

• JCVI-syn3A is a genetically minimal bacterium with a high proportion of unannotated genes (approx. 20%).
• Accurate gene function annotation is crucial for refining whole-cell kinetic models and understanding cellular processes.
• Previous work established a whole-cell kinetic model for JCVI-syn3A, highlighting the need for further gene characterization.

Purpose of the Study:

• To computationally elucidate the functions of essential, uncharacterized genes in JCVI-syn3A.
• To improve the predictive accuracy of whole-cell models by enhancing gene annotations.
• To guide future experimental studies on JCVI-syn3A's core biological mechanisms.

Main Methods:

• Leveraged evolutionary sequence analysis, protein structure prediction, interactomics, and genome architecture.
• Applied structure prediction to all 452 protein-coding genes for expedited future annotation.
• Integrated computational findings to propose directed wet-lab experiments.

Main Results:

• Successfully assigned putative functions to several essential, previously uncharacterized genes in JCVI-syn3A.
• Enhanced understanding of genes directly impacting cell growth, division, and morphology.
• Developed a comprehensive computational workflow applicable to all JCVI-syn3A proteins for future studies.

Conclusions:

• Computational analyses significantly contribute to annotating essential genes in minimal bacterial genomes.
• Improved gene annotations strengthen the JCVI-syn3A whole-cell model, enhancing its predictive power.
• This work provides a foundation for deeper insights into fundamental biophysical processes at the cellular level.