Dataset Information

Protein structure prediction using residue- and fragment-environment potentials in CASP11.

ABSTRACT: An accurate scoring function that can select near-native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse-grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluates the native-likeness of local structural environment of residues in a structure decoy considering positions and the depth of side-chains of spatially neighboring residues. We also introduced a helix interaction potential as an additional scoring function for selecting decoys. The best models selected by PRESCO and the helix interaction potential underwent structure refinement, which includes side-chain modeling and relaxation with a short molecular dynamics simulation. Our protocol was successful, achieving the top rank in the free modeling category with a significant margin of the accumulated Z-score to the subsequent groups when the top 1 models were considered. Proteins 2016; 84(Suppl 1):105-117. © 2015 Wiley Periodicals, Inc.

SUBMITTER: Kim H

PROVIDER: S-EPMC4781684 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Protein structure prediction using residue- and fragment-environment potentials in CASP11.

Kim Hyungrae H Kihara Daisuke D

Proteins 20150922

An accurate scoring function that can select near-native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse-grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluat ...[more]

PMID: 26344195

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Project description:BACKGROUND:Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small proteins. However, inaccuracy of their energy functions and their stochastic nature imposes generation of a large number of decoys to explore adequately the solution space, limiting their usage to small proteins. Taking advantage of the uneven complexity of the sequence-structure relationship of short fragments, we adjusted the fragment insertion process by customising the number of available fragment templates according to the expected complexity of the predicted local secondary structure. Whereas the number of fragments is kept to its default value for coil regions, important and dramatic reductions are proposed for beta sheet and alpha helical regions, respectively. RESULTS:The evaluation of our fragment selection approach was conducted using an enhanced version of the popular Rosetta fragment-based protein structure prediction tool. It was modified so that the number of fragment candidates used in Rosetta could be adjusted based on the local secondary structure. Compared to Rosetta's standard predictions, our strategy delivered improved first models, +?24% and?+?6% in terms of GDT, when using 2000 and 20,000 decoys, respectively, while reducing significantly the number of fragment candidates. Furthermore, our enhanced version of Rosetta is able to deliver with 2000 decoys a performance equivalent to that produced by standard Rosetta while using 20,000 decoys. We hypothesise that, as the fragment insertion process focuses on the most challenging regions, such as coils, fewer decoys are needed to explore satisfactorily conformation spaces. CONCLUSIONS:Taking advantage of the high accuracy of sequence-based secondary structure predictions, we showed the value of that information to customise the number of candidates used during the fragment insertion process of fragment-based protein structure prediction. Experimentations conducted using standard Rosetta showed that, when using the recommended number of decoys, i.e. 20,000, our strategy produces better results. Alternatively, similar results can be achieved using only 2000 decoys. Consequently, we recommend the adoption of this strategy to either improve significantly model quality or reduce processing times by a factor 10.

Dataset Information

Protein structure prediction using residue- and fragment-environment potentials in CASP11.

Publications

Protein structure prediction using residue- and fragment-environment potentials in CASP11.

Similar Datasets

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