Computational function prediction of bacteria and phage proteins

Susanna R. Grigson, George Bouras, Bas E. Dutilh, Robert D. Olson, Robert A. Edwards

Research output: Contribution to journalArticlepeer-review

Abstract

SUMMARY: Understanding protein functions is crucial for interpreting microbial life; however, reliable function annotation remains a major challenge in computational biology. Despite significant advances in bioinformatics methods, ~30% of all bacterial and ~65% of all bacteriophage (phage) protein sequences cannot be confidently annotated. In this review, we examine state-of-the-art bioinformatics tools and methodologies for annotating bacterial and phage proteins, particularly those of unknown or poorly characterized function. We describe the process of identifying protein-coding regions and the systems to classify protein functionalities. Additionally, we explore a range of protein annotation methods, from traditional homology-based methods to cutting-edge machine learning models. In doing so, we provide a toolbox for confidently annotating previously unknown bacterial and phage proteins, advancing the discovery of novel functions and our understanding of microbial systems.

Original languageEnglish
Article numbere0002225
Pages (from-to)1-30
Number of pages30
JournalMicrobiology and molecular biology reviews : MMBR
Volume89
Issue number3
DOIs
Publication statusPublished - Sept 2025

Keywords

  • bioinformatics
  • function prediction
  • machine learning
  • microbial proteins

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