Abstract
BackgroundWith 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0775-x) contains supplementary material, which is available to authorized users.
Highlights
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
Computational protein structure prediction methods can be broadly classified into two categories: templatebased modeling (TBM) [9,10,11,12,13,14,15,16,17] and template-free modeling (FM) [15, 18, 19]
In order to predict the structure of a target protein, template-based modeling tries to find a target’s homologous protein with known structure and use it as a template, transfer the structure of the template to the target based on their sequence alignment, and adjust the structure to account for the variation from the template sequence to the target sequence [22]
Summary
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. Template-based modeling is the most widely used and most accurate technique for protein structure prediction. It cannot work when no good template is found. Even though template-based modeling and template-free modeling use very different techniques for protein structure prediction, they are in common in sampling protein conformations in a huge conformation space for a target. The former is just a more focused, targeted sampling based on known, related structural points in the space, whereas the latter is a more unbiased, random sampling to explore a large conformation space
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