Protein model quality assessment prediction by combining fragment comparisons and a consensus C(alpha) contact potential

TitleProtein model quality assessment prediction by combining fragment comparisons and a consensus C(alpha) contact potential
Publication TypeJournal Article
Year of Publication2008
AuthorsZhou, H, Skolnick J
JournalProteins
Volume71
Pagination1211-8
Date Published2008 May 15
Abstract

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus C(alpha) contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets, which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models.

PDFhttp://cssb.biology.gatech.edu/skolnick/publications/pdffiles/281.pdf
Pub Med Link

http://www.ncbi.nlm.nih.gov/pubmed/18004783?dopt=Abstract

Alternate JournalProteins
Citekey281