Prediction of global and local quality of CASP8 models by MULTICOM series

Jianlin Cheng, Zheng Wang, Allison N. Tegge, Jesse Eickholt

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


Evaluating the quality of protein structure models is important for selecting and using models. Here, we describe the MULTICOM series of model quality predictors which contains three predictors tested in the CASP8 experiments. We evaluated these predictors on 120 CASP8 targets. The average correlations between predicted and real GDT-TS scores of the two semi-clustering methods (MULTICOM and MULTICOM-CLUSTER) and the one single-model ab initio method (MULTICOM-CMFR) are 0.90, 0.89, and 0.74, respectively; and their average difference (or GDT-TS loss) between the global GDT-TS scores of the top-ranked models and the best models are 0.05, 0.06, and 0.07, respectively. The average correlation between predicted and real local quality scores of the semi-clustering methods is above 0.64. Our results show that the novel semi-clustering approach that compares a model with top ranked reference models can improve initial quality scores generated by the ab initio method and a simple meta approach.

Original languageEnglish
Pages (from-to)181-184
Number of pages4
JournalProteins: Structure, Function and Bioinformatics
Issue numberSUPPL. 9
StatePublished - 2009


  • Clustering
  • Model quality assurance program
  • Protein model quality assessment
  • Protein structure prediction


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