Towards a phylogenetically informed approach to solving protein-protein interactions.

Chun Shen Lim, Peter Mace, Peter C Fineran, Paul P Gardner

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In one paragraph

Review in Biochemical Society transactions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chun Shen LimDepartment of Biochemistry, Faculty of Biomedical Sciences, University of Otago, Dunedin, 9016, New Zealand.ORCID 0000-0001-7015-0125

Peter MaceDepartment of Biochemistry, Faculty of Biomedical Sciences, University of Otago, Dunedin, 9016, New Zealand.ORCID 0000-0003-2175-9537

Peter C FineranMaurice Wilkins Centre for Molecular Biodiscovery, University of Otago, Dunedin, 9016, New Zealand.ORCID 0000-0002-4639-6704

Paul P GardnerDepartment of Biochemistry, Faculty of Biomedical Sciences, University of Otago, Dunedin, 9016, New Zealand.ORCID 0000-0002-7808-1213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions (PPIs) are critical to all cellular activities. Despite having a large number of proteins, cells have spatial and temporal control over PPIs to avoid dysregulation in cellular pathways. Considerable research efforts have aimed to find new PPIs, curate PPIs from the literature and build searchable PPI databases. These databases have been widely used by experimental and computational scientists. Here we find that the PPIs captured by these databases are highly heterogeneous and concentrated on a small number of species. These issues hamper researchers from capturing the full landscape of reliable PPIs, affecting the accuracy of machine-learning models and the effectiveness of experimental designs. However, there are opportunities to fill gaps computationally and experimentally. We suggest developing a phylogenetically informed approach to test PPIs experimentally and computationally.

Indexed as

PhylogenyProtein Interaction MappingProteinsAnimalsComputational BiologyDatabases, ProteinHumansMachine LearningProtein BindingProtein Interaction MapsProteinsartificial intelligenceevolutionary conserved protein–protein interactionsmachine learningprotein complex prediction

Identifiers

PMID41348431

DOI10.1042/BST20253031

PMCPMC12751078

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.