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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
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Assigning protein function from domain-function associations using DomFun
Elena Rojano1,2, Fernando M Jabato1,2, James R Perkins3,4,5
1Department of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.
BMC Bioinformatics
|January 16, 2022
Summary
DomFun predicts protein function using domain associations. It achieves high accuracy using the Simpson index and Stouffer's method with FunFams, outperforming other methods in benchmarks.
Area of Science:
- Bioinformatics
- Computational Biology
Background:
- Protein function prediction is a significant challenge in bioinformatics.
- Protein domain composition is a key determinant of protein function.
- Existing methods for predicting protein function often struggle with accuracy and scope.
Purpose of the Study:
- To introduce DomFun, a novel Ruby gem for protein function prediction.
- To leverage tripartite network analysis for calculating domain-function associations.
- To combine these associations at the protein level for improved prediction accuracy.
Main Methods:
- Analyzed 16 tripartite networks linking CATH-Gene3D domains (superfamilies and FunFams) with functional annotations (Gene Ontology, KEGG, Reactome).
- Calculated domain-function associations using multiple indices, including the Simpson index.
- Combined associations using Stouffer's method and validated predictions using the CAFA 3 benchmark and a custom Pathway Prediction Performance (PPP) procedure.
Main Results:
- The combination of Simpson index for FunFam domain-function associations and Stouffer's method demonstrated superior performance across various evaluation scenarios.
- Using FunFams outperformed superfamilies, and predictions for Gene Ontology molecular function were more accurate than for biological process terms.
- DomFun achieved performance comparable to top methods in CAFA 3 evaluations and showed good accuracy for KEGG and Reactome annotations via the PPP benchmark.
Conclusions:
- DomFun offers competitive protein function prediction, particularly for Gene Ontology terms, with performance varying by evaluation procedure.
- The tool accurately predicts functions for KEGG and Reactome annotations, as validated by the PPP benchmark.
- Optimal performance is achieved using FunFams and combining domain-function associations via the Simpson index and Stouffer's method. The tool is extensible for incorporating additional protein features.
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