Mutual Annotation-Based Prediction of Protein Domain Functions with Domain2GO

Abstract

MotivationIdentifying unknown functional properties of proteins is an important task for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting biomolecular functions. Biological ontologies, such as the Gene Ontology (GO), which provide a standardized vocabulary of information about biological entities, are frequently employed in protein function prediction. ResultsIn this study, we proposed a new method called Domain2GO that predicts associations between protein domains and GO terms, thus redefining the problem as domain function prediction, using documented protein-level GO annotations together with proteins domain content. To obtain reliable associations, co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling. An ablation study was conducted to compare the predictive performance of various implementations of Domain2GO, differing from each other by the utilized statistical measure (e.g., information theory inspired similarity measures and the ones calculated by the expectation-maximization algorithm). As a use-case study, examples selected from the finalized domain-GO term mappings were evaluated for their biological relevance via a literature review. Then, we applied the proposed method to predict presently unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For protein function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having exceptionally low computational costs. The approach presented here can be extended to other ontologies and biological entities in order to investigate unknown relationships in complex and large-scale biological data. Availability and implementationThe source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO.