Predicting disordered regions in proteins using the profiles of amino acid indices.
AuthorHan, P; Zhang, X; Feng, Z-P
Source TitleBMC Bioinformatics
PublisherSpringer Science and Business Media LLC
University of Melbourne Author/sFeng, Zhi-Ping
AffiliationMedical Biology (W.E.H.I.)
Document TypeConference Paper
CitationsHan, P., Zhang, X. & Feng, Z. -P. (2009). Predicting disordered regions in proteins using the profiles of amino acid indices.. BMC Bioinformatics, 10 Suppl 1, (SUPPL. 1), pp.S42-. Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-10-S1-S42.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2648739
BACKGROUND: Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures. RESULTS: In this paper, algorithms are presented to predict long and short disordered regions in proteins, namely the long disordered region prediction algorithm DRaai-L and the short disordered region prediction algorithm DRaai-S. These algorithms are developed based on the Random Forest machine learning model and the profiles of amino acid indices representing various physiochemical and biochemical properties of the 20 amino acids. CONCLUSION: Experiments on DisProt3.6 and CASP7 demonstrate that some sets of the amino acid indices have strong association with the ordered and disordered status of residues. Our algorithms based on the profiles of these amino acid indices as input features to predict disordered regions in proteins outperform that based on amino acid composition and reduced amino acid composition, and also outperform many existing algorithms. Our studies suggest that the profiles of amino acid indices combined with the Random Forest learning model is an important complementary method for pinpointing disordered regions in proteins.
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References