Dataset Information

Vorescore--fold recognition improved by rescoring of protein structure models.

ABSTRACT: SUMMARY: The identification of good protein structure models and their appropriate ranking is a crucial problem in structure prediction and fold recognition. For many alignment methods, rescoring of alignment-induced models using structural information can improve the separation of useful and less useful models as compared with the alignment score. Vorescore, a template-based protein structure model rescoring system is introduced. The method scores the model structure against the template used for the modeling using Vorolign. The method works on models from different alignment methods and incorporates both knowledge from the prediction method and the rescoring. RESULTS: The performance of Vorescore is evaluated in a large-scale and difficult protein structure prediction context. We use different threading methods to create models for 410 targets, in three scenarios: (i) family members are contained in the template set; (ii) superfamily members (but no family members); and (iii) only fold members (but no family or superfamily members). In all cases Vorescore improves significantly (e.g. 40% on both Gotoh and HHalign at the fold level) on the model quality, and clearly outperforms the state-of-the-art physics-based model scoring system Rosetta. Moreover, Vorescore improves on other successful rescoring approaches such as Pcons and ProQ. In an additional experiment we add high-quality models based on structural alignments to the set, which allows Vorescore to improve the fold recognition rate by another 50%. AVAILABILITY: All models of the test set (about 2 million, 44 GB gzipped) are available upon request.

SUBMITTER: Csaba G

PROVIDER: S-EPMC2935407 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Vorescore--fold recognition improved by rescoring of protein structure models.

Csaba Gergely G Zimmer Ralf R

Bioinformatics (Oxford, England) 20100901 18

<h4>Summary</h4>The identification of good protein structure models and their appropriate ranking is a crucial problem in structure prediction and fold recognition. For many alignment methods, rescoring of alignment-induced models using structural information can improve the separation of useful and less useful models as compared with the alignment score. Vorescore, a template-based protein structure model rescoring system is introduced. The method scores the model structure against the template ...[more]

PMID: 20823310

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Project description:BACKGROUND: Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server. RESULTS: In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset. CONCLUSIONS: The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://202.112.170.199/DescFold/index.html.

Dataset Information

Vorescore--fold recognition improved by rescoring of protein structure models.

Publications

Vorescore--fold recognition improved by rescoring of protein structure models.

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