Computational function prediction of bacteria and phage proteins

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dc.contributor.authorGrigson, S.R.
dc.contributor.authorBouras, G.
dc.contributor.authorDutilh, B.E.
dc.contributor.authorOlson, R.D.
dc.contributor.authorEdwards, R.A.
dc.contributor.editorvan der Meer, J.R.
dc.date.issued2025
dc.description.abstractUnderstanding 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.
dc.description.statementofresponsibilitySusanna R. Grigson, George Bouras, Bas E. Dutilh, Robert D. Olson, Robert A. Edwards
dc.identifier.citationMicrobiology and Molecular Biology Reviews, 2025; 89(3):e0002225-1-e0002225-30
dc.identifier.doi10.1128/mmbr.00022-25
dc.identifier.issn1092-2172
dc.identifier.issn1098-5557
dc.identifier.orcidBouras, G. [0000-0002-5885-4186]
dc.identifier.urihttps://hdl.handle.net/2440/147928
dc.language.isoen
dc.publisherAmerican Society for Microbiology
dc.relation.granthttp://purl.org/au-research/grants/arc/DP250103825
dc.relation.granthttp://purl.org/au-research/grants/arc/DP220102915
dc.relation.granthttp://purl.org/au-research/grants/arc/FL250100019
dc.rights© 2025 American Society for Microbiology. All Rights Reserved.
dc.source.urihttps://doi.org/10.1128/mmbr.00022-25
dc.subjectbioinformatics; function prediction; microbial proteins; machine learning
dc.subject.meshBacteria
dc.subject.meshBacteriophages
dc.subject.meshBacterial Proteins
dc.subject.meshViral Proteins
dc.subject.meshComputational Biology
dc.subject.meshMolecular Sequence Annotation
dc.subject.meshMachine Learning
dc.titleComputational function prediction of bacteria and phage proteins
dc.typeJournal article
pubs.publication-statusPublished online

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