Protein model quality assessment prediction by combining fragment comparisons and a consensus C(alpha) contact potential.
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.
Zhou
Hongyi
H
Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA.
Skolnick
Jeffrey
J
D000595:Amino Acid Sequence; D053179:Caspase 7 / Q000737:chemistry; D003198:Computer Simulation; D016384:Consensus Sequence; D008958:Models, Molecular; D010446:Peptide Fragments / Q000737:chemistry*; D019151:Peptide Library; D011237:Predictive Value of Tests; D011487:Protein Conformation; D017510:Protein Folding
D010446:Peptide Fragments; D019151:Peptide Library; C505520:CASP7 protein, human; D053179:Caspase 7