On the performance of combined dichotomic predictors of natively unfolded proteins

The performance of single folding predictors and combination scores is critically evaluated. We test mean packing, mean pairwise energy and the new index gVSL2 on a dataset of 743 folded proteins and 81 natively unfolded proteins. These predictors have an individual performance comparable or even better than other proposed methods. We introduce here a strictly unanimous score S_{SU} that combines them but leaves undecided those sequences differently classified by two single predictors. The performance of the single predictors on a dataset purged from the proteins left unclassified by S_{SU}, significantly increases, indicating that unclassified proteins are mainly false predictions. Amino acid composition is the main determinant considered by these predictors, therefore unclassified proteins have a composition compatible with both folded and unfolded status. This is why purging a dataset from these ambiguous proteins increases the performance of single predictors. The percentage of proteins predicted as natively unfolded by S_{SU} in the three kingdoms are: 4.1% for Bacteria, 1.0% for Archaea and 20.0% for Eukarya; compatible with previous determinations. Evidence is given of a scaling law relating the number of natively unfolded proteins with the total number of proteins in a genome; a first estimate of the critical exponent is 1.95 +- 0.21

• Publication year

  2008

• Venue

  arXiv: Biomolecules

• Publication date

  2008-06-30

• Fields of study

  Biology, Mathematics, Chemistry, Computer Science

• Identifiers
  arXiv 0806.4838
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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