FunPredCATH: An ensemble method for predicting protein function using CATH.

Autor: Bonello J; Department of Structural and Molecular Biology, University College London, Gower Street, London WC1E 6BT, United Kingdom; Department of Computer Information Systems, University of Malta, Faculty of ICT, Msida, MSD 2080, Malta. Electronic address: joseph.bonello.15@ucl.ac.uk., Orengo C; Department of Structural and Molecular Biology, University College London, Gower Street, London WC1E 6BT, United Kingdom.
Jazyk: angličtina
Zdroj: Biochimica et biophysica acta. Proteins and proteomics [Biochim Biophys Acta Proteins Proteom] 2024 Feb 01; Vol. 1872 (2), pp. 140985. Date of Electronic Publication: 2023 Dec 19.
DOI: 10.1016/j.bbapap.2023.140985
Abstrakt: Motivation: The growth of unannotated proteins in UniProt increases at a very high rate every year due to more efficient sequencing methods. However, the experimental annotation of proteins is a lengthy and expensive process. Using computational techniques to narrow the search can speed up the process by providing highly specific Gene Ontology (GO) terms.
Methodology: We propose an ensemble approach that combines three generic base predictors that predict Gene Ontology (BP, CC and MF) terms from sequences across different species. We train our models on UniProtGOA annotation data and use the CATH domain resources to identify the protein families. We then calculate a score based on the prevalence of individual GO terms in the functional families that is then used as an indicator of confidence when assigning the GO term to an uncharacterised protein.
Methods: In the ensemble, we use a statistics-based method that scores the occurrence of GO terms in a CATH FunFam against a background set of proteins annotated by the same GO term. We also developed a set-based method that uses Set Intersection and Set Union to score the occurrence of GO terms within the same CATH FunFam. Finally, we also use FunFams-Plus, a predictor method developed by the Orengo Group at UCL to predict GO terms for uncharacterised proteins in the CAFA3 challenge.
Evaluation: We evaluated the methods against the CAFA3 benchmark and DomFun. We used the Precision, Recall and F max metrics and the benchmark datasets that are used in CAFA3 to evaluate our models and compare them to the CAFA3 results. Our results show that FunPredCATH compares well with top CAFA methods in the different ontologies and benchmarks.
Contributions: FunPredCATH compares well with other prediction methods on CAFA3, and the ensemble approach outperforms the base methods. We show that non-IEA models obtain higher F max scores than the IEA counterparts, while the models including IEA annotations have higher coverage at the expense of a lower F max score.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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