Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational...

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• Small open reading frames (smORFs) encoding microproteins (proteins < 100 amino acids) have been historically excluded from genome annotation due to limitations in traditional genomics pipelines.
• This exclusion has led to a significant underrepresentation of potentially functional small proteins in genomic databases.

Purpose of the Study:

• To present μProteInS, a comprehensive bioinformatics pipeline designed for the identification of unannotated microproteins encoded by smORFs in bacterial genomes.
• To demonstrate the utility of proteogenomics in overcoming the challenges of identifying short protein sequences.

Main Methods:

• The μProteInS pipeline integrates genomics, transcriptomics, and proteomics data.
• It encompasses quality control, transcriptome assembly, and mass spectrometry data analysis, including scoring and post-processing.
• A machine learning component is included for high-confidence spectral identification.

Main Results:

• The pipeline enables accurate identification of microproteins by overlaying multi-omics evidence.
• It provides a systematic workflow for discovering novel small proteins from smORFs.
• The machine learning approach enhances the reliability of identifying microproteins in large datasets.

Conclusions:

• Proteogenomics, facilitated by pipelines like μProteInS, is crucial for the accurate annotation of microproteins.
• This approach significantly expands the scope of genome annotation to include previously neglected small proteins.
• μProteInS offers a robust solution for identifying and validating microproteins in bacterial systems.