Predicting Intrinsic Disorder From Amino Acid Sequence

Zoran Obradovic, Kang Peng, Slobodan Vucetic, Predrag Radivojac, Celeste J. Brown, A. Keith Dunker

Research output: Contribution to journalArticlepeer-review

321 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)566-572
Number of pages7
JournalProteins: Structure, Function and Genetics
Issue numberSUPPL. 6
StatePublished - Nov 12 2003


  • Intrinsically disordered
  • Machine learning
  • Natively unfolded
  • Neural networks
  • Ordinary least squares regression

ASJC Scopus subject areas

  • Genetics
  • Structural Biology
  • Biochemistry

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