Integrated sequence and -omic features reveal novel small proteome of Mycobacterium tuberculosis

Abstract

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Bioinformatic studies on small proteins are under-represented due to difficulties in annotation posed by their small size. However, recent discoveries emphasize the functional significance of small proteins in cellular processes including cell signaling, metabolism, and adaptation to stress. In this study, we utilized a Random Forest classifier trained on sequence features, RNA-Seq, and Ribo-Seq data to uncover small proteins (smORFs) in M. tuberculosis. Independent predictions for the exponential and starvation conditions resulted in 695 potential smORFs. We examined the functional implications of these smORFs using homology searches, LC-MS/MS, and ChIP-seq data, testing their expression in diverse growth conditions, and identifying protein domains. We provide evidence that some of these smORFs could be part of operons, or exist as upstream ORFs. This expanded data resource for the proteins of M. tuberculosis would aid in fine-tuning the existing protein and gene regulatory networks, thereby improving system-wide studies. The primary goal of this study was to uncover and characterize smORFs in M. tuberculosis through bioinformatic analysis, shedding light on their functional roles and genomic organization. Further investigation of these potential smORFs would provide valuable insights into the genome organization and functional diversity of the M. tuberculosis proteome.

Keywords

• small proteins
• RNA-Seq
• Ribo-Seq
• coupled ORFs
• Random Forest