A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.
BACKGROUND
With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.


METHODS
We developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking.


RESULTS
The method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors.


CONCLUSIONS
The good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/.
Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. jl482@mail.missouri.edu.
Jilong
J
Li
Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. rcrg4@mail.missouri.edu.
Renzhi
R
Cao
Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. chengji@missouri.edu.|Informatics Institute, University of Missouri-Columbia, Columbia, MO, 65211, USA. chengji@missouri.edu.|C. Bond Life Science Center, University of Missouri-Columbia, Columbia, MO, 65211, USA. chengji@missouri.edu.
Jianlin
J
Cheng
D000595:Amino Acid Sequence; D023281:Genomics / Q000379:methods*; D008956:Models, Chemical; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins