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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Identification of putative domain linkers by a neural network - application to a large sequence database
Satoshi Miyazaki1, Yutaka Kuroda, Shigeyuki Yokoyama
1Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
BMC Bioinformatics
|June 28, 2006
Summary
This study uses neural networks to identify protein domain linkers, improving structural domain prediction. Combining linker and low-complexity region analysis enhances discovery of novel protein domains for large-scale studies.
Area of Science:
- Structural biology
- Computational biology
- Bioinformatics
Background:
- Dissecting large proteins into structural domains is crucial for structural genomics and proteomics.
- A practical method is needed to reliably identify domain boundaries.
Purpose of the Study:
- To test the efficacy of a neural network in identifying domain linkers within protein sequences.
- To explore novel structural domains using computational predictions.
Main Methods:
- Applied a neural network to identify domain linkers in the SWISSPROT database.
- Compared domain linker predictions with low-complexity regions (LCRs).
- Analyzed linker location relative to known protein domains (PDB, CDD).
Main Results:
- Identified 3009 putative domain linkers, with 75% correctly located near domain termini.
- Predicted 5124 putative linkers in un-annotated regions, suggesting novel domains.
- Found domain linkers and LCRs identify distinct boundary regions, with only 32% overlap.
Conclusions:
- Neural network-based domain linker prediction is effective.
- Combining domain linker and LCR analysis improves domain boundary prediction sensitivity.
- This approach facilitates the discovery of novel structural domains for further research.
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