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Investigation of protein functions through data-mining on integrated human transcriptome database, H-Invitational
Chisato Yamasaki1, Kanako O Koyanagi, Yasuyuki Fujii
1Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, AIST Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan.
Gene
|September 28, 2005
Summary
The H-Invitational Database (H-InvDB) enhances understanding of human genes by functionally annotating hypothetical proteins. Structural prediction methods successfully assigned functions to previously unknown proteins, aiding human biology and pathology research.
Area of Science:
- Genomics
- Proteomics
- Bioinformatics
Background:
- The H-Invitational Database (H-InvDB) contains comprehensive annotations for human full-length cDNA clones.
- A significant portion (40.4%) of proteins in H-InvDB are classified as hypothetical with unknown functions.
- Accurate functional annotation is crucial for understanding human biology and disease.
Purpose of the Study:
- To assign advanced functional annotations to hypothetical proteins within H-InvDB.
- To leverage structural prediction tools for the functional characterization of unannotated proteins.
- To enhance the utility of H-InvDB as a resource for biological and pathological research.
Main Methods:
- Data-mining of the H-InvDB version of GTOP to identify SCOP domains in hypothetical proteins.
- Utilizing SOSUI and TMHMM for predicting subcellular localization and identifying transmembrane proteins.
- Employing similarity searches against protein databases and motif prediction (InterProScan).
Main Results:
- Identified 337 SCOP domains within 7865 hypothetical proteins.
- Discovered 1032 transmembrane proteins among the hypothetical protein set.
- Demonstrated the effectiveness of structural prediction for functional annotation of unknown proteins.
Conclusions:
- Structural prediction is an effective strategy for assigning functions to hypothetical proteins.
- H-InvDB provides an integrative platform for in silico data-mining to explore human biology.
- These findings contribute valuable resources for advancing human biology and pathology research.