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PMID: 14579347 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Predicting intrinsic disorder from amino acid sequence.

Proteins ·Vol. 53 Suppl 6 ·2003-00-00 ·Pages 566-72

Obradovic Z, Peng K, Vucetic S, Radivojac P, Brown CJ, Dunker AK

Abstract

Blind predictions of intrinsic order and disorder were made on 42 proteins subsequently revealed to contain 9,044 ordered residues, 284 disordered residues in 26 segments of length 30 residues or less, and 281 disordered residues in 2 disordered segments of length greater than 30 residues. The accuracies of the six predictors used in this experiment ranged from 77% to 91% for the ordered regions and from 56% to 78% for the disordered segments. The average of the order and disorder predictions ranged from 73% to 77%. The prediction of disorder in the shorter segments was poor, from 25% to 66% correct, while the prediction of disorder in the longer segments was better, from 75% to 95% correct. Four of the predictors were composed of ensembles of neural networks. This enabled them to deal more efficiently with the large asymmetry in the training data through diversified sampling from the significantly larger ordered set and achieve better accuracy on ordered and long disordered regions. The exclusive use of long disordered regions for predictor training likely contributed to the disparity of the predictions on long versus short disordered regions, while averaging the output values over 61-residue windows to eliminate short predictions of order or disorder probably contributed to the even greater disparity for three of the predictors. This experiment supports the predictability of intrinsic disorder from amino acid sequence.

MeSH Terms
Amino Acids/chemistry Computational Biology/methods,trends Protein Conformation Proteins/chemistry Reproducibility of Results Sensitivity and Specificity
Chemicals
Amino Acids Proteins
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Obradovic Zoran
Center for Information Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, USA. [email protected]
Peng Kang
Vucetic Slobodan
Radivojac Predrag
Brown Celeste J
Dunker A Keith
Article Info
Journal
Proteins
Abbr.
Proteins
ISSN
1097-0134
Published
2003-00-00
Pages
566-72
Language
English
Region
United States
NLM ID
8700181
Subset
IM
Grants
NLM NIH HHS · 1R01 LM06916 · United States
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