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Identification of Moonlighting Proteins in Genomes Using Text Mining Techniques.
Aashish Jain1,2, Hareesh Gali2, Daisuke Kihara1,2,3
1Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA.
Proteomics
|September 28, 2018
Summary
This study introduces a computational method to systematically identify moonlighting proteins, which have multiple distinct functions. Researchers found 13 new moonlighting proteins in model organisms using this approach.
Area of Science:
- Proteomics
- Bioinformatics
- Genomics
Background:
- Moonlighting proteins possess multiple distinct functions, a concept gaining traction in biological research.
- Systematic identification of these proteins is challenging due to the often-serendipitous discovery of their secondary functions.
- Computational approaches are emerging to address this identification challenge using diverse data sources.
Purpose of the Study:
- To provide an overview of methods for identifying moonlighting proteins.
- To apply the DextMP literature-mining tool for discovering novel moonlighting proteins.
- To categorize and detail newly identified moonlighting proteins.
Main Methods:
- Literature mining using the DextMP algorithm.
- Application of DextMP to Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster genomes.
- Two-step manual literature review for validation of potential moonlighting proteins.
Main Results:
- The DextMP method successfully identified potential moonlighting proteins across the studied genomes.
- A rigorous manual curation process confirmed 13 novel moonlighting proteins.
- Identified proteins were classified based on the distinctness of their dual functions.
Conclusions:
- The DextMP algorithm, combined with manual validation, offers a viable strategy for systematic moonlighting protein discovery.
- The study expands the known repertoire of moonlighting proteins in key model organisms.
- Further refinement of the DextMP algorithm is proposed to enhance its predictive power.
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