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Improved mutation tagging with gene identifiers applied to membrane protein stability prediction
Rainer Winnenburg1, Conrad Plake, Michael Schroeder
1Biotechnology Center, Technische Universität Dresden, Tatzberg, Germany. rainer.winnenburg@biotec.tu-dresden.de
BMC Bioinformatics
|September 18, 2009
Summary
We developed a pipeline to automatically extract protein mutations from scientific abstracts, achieving 87% accuracy. This method aids in studying protein structure-function relationships and predicting mutation effects on stability.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Automated retrieval of protein mutation data is crucial for understanding structure-function relationships.
- Integrating mutation data with structural and interaction information enhances biomedical data analysis.
Purpose of the Study:
- To develop and validate a reliable pipeline for retrieving protein point mutations from PubMed abstracts.
- To demonstrate the utility of extracted mutation data in evaluating protein stability prediction models.
Main Methods:
- Implemented a rule- and regular expression-based pipeline for protein mutation extraction from PubMed abstracts.
- Utilized named entity recognition for gene identification and sequence checks for mutation-protein linking.
- Applied mutation data to a solvation energy model for predicting stabilizing regions in membrane proteins.
Main Results:
- Achieved an 87% F-measure for mutation retrieval from PubMed abstracts.
- Improved gene recognition F-measure from 77% to 91% by incorporating mutation information.
- Identified 35 relevant mutations in G protein-coupled receptors, with 71% compliance with stability model predictions.
Conclusions:
- Presented a dependable method for retrieving protein mutations from literature for specific genes or proteins.
- Demonstrated the application of text-mined amino acid substitution data for protein structure stability studies using a novel energy model.
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