Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. Availability: The 3D-Jury system is available via the Structure Prediction Meta Server

doi:10.1093/bioinformatics/bti702

Meta-BASIC

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. High accuracy is achieved by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique is rooted in machine learning that shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure, using the joint hypothesis we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily- , and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to those of the individual component classifiers. In particular, we achieve error rates that are 3 to 12 times less than the individual classifiers' error rates at the family level, 1.5 to 4.5 times less at the superfamily level, and 1.1 to 2.4 times less at the fold level.

Here we introduce EVAcon, an automated web service that evaluates the performance of contact prediction servers. Currently, EVAcon is monitoring nine servers, four of which are specialized in contact prediction and five are general structure prediction servers. Results are compared for all newly determined experimental structures deposited into PDB ( 5–50 per week). EVAcon allows for a precise comparison of the results based on a system of common protein subsets and the commonly accepted evaluation criteria that are also used in the corresponding category of the CASP assessment. EVAcon is a new service added to the functionality of the EVA system for the continuous evaluation of protein structure prediction servers. The new service is accesible from any of the

Prediction of partial membrane protein topologies using a

The Pcons.net Meta Server

All are not equal: A benchmark of different homology modeling

Background: The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results: In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE) which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion: In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the

Glycosyltransferases (GTs) are diverse enzymes organized into 65 families. X-ray crystallography and in silico studies have shown many of these to belong to two structural superfamilies: GT-A and GT-B. Through application of fold recognition and iterated sequence searches, we demonstrate that families 60, 62, and 64 may also be grouped into the GT-A fold superfamily. Analysis of conserved acidic residues suggests that catalytic sites are better conserved in superfamily GT-B than in GT-A. Although 26 % and 29 % of GT families may now be confidently placed in superfamilies GT-A and GT-B, respectively, the remaining 45 % of families bear no discernible resemblance to either superfamily, which, given the sensitivity of modern fold recognition methods, suggests the existence of novel structural scaffolds associated with GT activity. Furthermore, bioinformatics studies indicate the apparent ease with which mechanismÐinverting or retainingÐmay change during evolution. Key words: evolutionary relationships/fold recognition/ glycosyltransferases/MurG/SpsA