دورية أكاديمية
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Mutual annotation-based prediction of protein domain functions with Domain2GO.

التفاصيل البيبلوغرافية
العنوان: Mutual annotation-based prediction of protein domain functions with Domain2GO.
المؤلفون: Ulusoy E; Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey.; Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey., Doğan T; Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey.; Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey.
المصدر: Protein science : a publication of the Protein Society [Protein Sci] 2024 Jun; Vol. 33 (6), pp. e4988.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Cold Spring Harbor Laboratory Press Country of Publication: United States NLM ID: 9211750 Publication Model: Print Cited Medium: Internet ISSN: 1469-896X (Electronic) Linking ISSN: 09618368 NLM ISO Abbreviation: Protein Sci Subsets: MEDLINE
أسماء مطبوعة: Publication: 2001- : Woodbury, NY : Cold Spring Harbor Laboratory Press
Original Publication: New York, N.Y. : Cambridge University Press, c1992-
مواضيع طبية MeSH: Protein Domains* , Proteins*/chemistry , Proteins*/metabolism , Proteins*/genetics , Molecular Sequence Annotation*, Databases, Protein ; Computational Biology/methods ; Gene Ontology ; Humans ; Software
مستخلص: Identifying unknown functional properties of proteins is essential 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 the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For 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 an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.
(© 2024 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)
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فهرسة مساهمة: Keywords: CAFA challenge; biomolecular function prediction; expectation maximization; gene ontology; protein domains
تواريخ الأحداث: Date Created: 20240517 Date Completed: 20240517 Latest Revision: 20240519
رمز التحديث: 20240519
مُعرف محوري في PubMed: PMC11099699
DOI: 10.1002/pro.4988
PMID: 38757367
قاعدة البيانات: MEDLINE
الوصف
تدمد:1469-896X
DOI:10.1002/pro.4988