Natively unfolded proteins: scalar predictors
Year: 2008
Type: preprint
Abstract: This work revisits ab-initio methods to identify natively unfolded proteins. Single predictors and combined score indexes are considered and their performance is critically evaluated against other methods already present in the literature. We consider mean packing (< P>), mean contact energy(< Ec>) and a new index of folding status, based on VSL2 (gV SL2), a predictor of single disordered amino acids. We use a new dataset made of 743 folded proteins and 81 natively unfolded proteins. Individual use of these predictors has a performance comparable or even better than other proposed methods: gV SL2 reaches a sensitivity (Sn) of 0.81, a specificity (Sp) of 0.89 and a level of false predictions (fp) of 0.11. The performance of these single predictors is significantly improved if used in combination. We introduce a strictly unanimous combination score SSU and a new score S0, combining 10 dichotomic predictors. The former score leaves some sequences undecided, whereas the latter classifies with no exceptions all the sequences in a dataset. Through the combined use of both scores we get: Sn=0.79, Sp=0.94 and fp=0.06, with less than 6 % of proteins left unpredicted. The combined use of SSU and S0 applied to the problem of finding the frequency of occurrence of natively unfolded proteins in genomes from Nature’s three kingdoms gives the following figures: the percentage of natively unfolded proteins predicted by SSU are 4.1 % for Bacteria, 1.0 % for Archaea and 20.0 % for Eukarya; comparable, but not coincident with similar 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. 2
Source: arXiv (Cornell University)
Authors: Antonio Deiana, Andrea Giansanti
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2802688195