On the performance of combined dichotomic predictors of natively unfolded proteins
Abstract
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 SSU 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 SSU, 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 SSU 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