02131nas a2200217   4500008004100000245007200041210006900113300001200182490000700194520144000201653001201641653002101653653003601674100001601710700001701726700001401743700001301757700001301770700002401783856010601807       2006                        eng d00aA composite score for predicting errors in protein structure models0 acomposite score for predicting errors in protein structure model  a1653-660 v153 aReliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.10a*Models10aMolecular Models10aTheoretical Proteins/*chemistry1 aEramian, D.1 aShen, M., Y.1 aDevos, D.1 aMelo, F.1 aSali, A.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16751606