MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein–Protein Network Mapping
Open accessAbstract
Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/.
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Inclusion of protein structural information, even computationally predicted structures, can substantially enhance function prediction accuracy for difficult annotation targets.
“We have recently shown that the inclusion of protein structural information, even from computationally predicted structures, can substantially enhance the accuracy of function predictions for difficult annotation targets.22,23”
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Sarker, B., Khare, N., Devignes, M., & Aridhi, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45385-5_24 · Cited by 2 · see citing works
Enhancing Protein Subcellular Localization Prediction Through Multi-Feature Fusion
Zhao, K., Liang, W., Bi, X., Yu, G., Quan, N., & Zhang, L. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394073 · Cited by 1 · see citing works
A Novel Graphlet-Based Community Detection Algorithm
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Machine Learning for Protein Function Prediction
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- Authors
- Zhang, C., Zheng, W., Freddolino, L., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 430
- Issue
- 15
- Pages
- 2256-2265
- ISSN
- 0022-2836 (ISSN-L)
- PMC
- PMC6014880
- OpenAlex
- W2789559913
- Cited by
- 80
- References
- 40
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 9 · 2023: 8 · 2022: 13 · 2021: 13 · 2020: 9 · 2019: 9 · 2018: 4
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 353 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Molecular Biology (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DBI1564756)
- National Institute of General Medical Sciences (GM083107, GM116960)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- gene ontology annotation, protein function prediction, non-homologous proteins, protein-protein interaction network, structural alignment
- MeSH terms
- Algorithms, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Software, Reproducibility of Results, Sequence Homology, Amino Acid, Computational Biology, Internet, Sequence Analysis, Protein, Protein Interaction Mapping, Protein Interaction Maps, Gene Ontology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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