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Open Access Research article

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

Renzhi Cao1, Zheng Wang4 and Jianlin Cheng123*

Author Affiliations

1 Computer Science Department, University of Missouri, Columbia, Missouri 65211, USA

2 Informatics Institute, University of Missouri, Columbia, Missouri 65211, USA

3 Christopher S. Bond Life Science Center, University of Missouri, Columbia, Missouri 65211, USA

4 School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406-0001, USA

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BMC Structural Biology 2014, 14:13  doi:10.1186/1472-6807-14-13

Published: 15 April 2014

Abstract

Background

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models.

Results

MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality.

Conclusions

Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Keywords:
Protein model quality assessment; Protein model quality assurance program; Protein structure prediction; Support vector machine; Clustering