Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract The prospect of identifying contacts in protein structures purely from aligned protein sequences has lured researchers for a long time, but progress has been modest until recently. Here, we reviewed the most successful methods for identifying structural contacts from sequence and how these methods differ and made an initial assessment of the overlap of predicted contacts by alternative approaches. We then discussed the limitations of these methods and possibilities for future development and highlighted the recent applications of contacts in tertiary structure prediction, identifying the residues at the interfaces of protein-protein interactions, and the use of these methods in disentangling alternative conformational states. Finally, we identified the current challenges in the field of contact prediction, concentrating on the limitations imposed by available data, dependencies on the sequence alignments, and possible future developments.

The sub-retinal pigment epithelial deposits that are a hallmark of age-related macular degeneration contain both C3b and millimolar levels of zinc. C3 is the central protein of complement, whereas C3u is formed by the spontaneous hydrolysis of the thioester bridge in C3. During activation, C3 is cleaved to form active C3b, then C3b is inactivated by Factor I and Factor H to form the C3c and C3d fragments. The interaction of zinc with C3 was quantified using analytical ultracentrifugation and x-ray scattering. C3, C3u, and C3b associated strongly in >100 μm zinc, whereas C3c and C3d showed weak association. With zinc, C3 forms soluble oligomers, whereas C3u and C3b precipitate. We conclude that the C3, C3u, and C3b association with zinc depended on the relative positions of C3d and C3c in each protein. Computational predictions showed that putative weak zinc binding sites with different capacities exist in all five proteins, in agreement with experiments. Factor H forms large oligomers in >10 μm zinc. In contrast to C3b or Factor H alone, the solubility of the central C3b-Factor H complex was much reduced at 60 μm zinc and even more so at >100 μm zinc. The removal of the C3b-Factor H complex by zinc explains the reduced C3u/C3b inactivation rates by zinc. Zinc-induced precipitation may contribute to the initial development of sub-retinal pigment epithelial deposits in the retina as well as reducing the progression to advanced age-related macular degeneration in higher risk patients. Background: Sub-retinal pigment epithelial deposits contain complement proteins and bioavailable zinc. Results: Ultracentrifugation and x-ray scattering show that >100 μm zinc induces oligomer formation in each of C3, C3u, and C3b, in analogy to Factor H. Conclusion: Factor H-C3b complexes are precipitated by zinc, which inhibits complement activation. Significance: A potential molecular mechanism for zinc-induced sub-retinal deposit formation is clarified.

Background: Sub-retinal pigment epithelial deposits contain complement proteins and bioavailable zinc. Results: Ultracentrifugation and x-ray scattering show that >100 μm zinc induces oligomer formation in each of C3, C3u, and C3b, in analogy to Factor H. Conclusion: Factor H-C3b complexes are precipitated by zinc, which inhibits complement activation. Significance: A potential molecular mechanism for zinc-induced sub-retinal deposit formation is clarified. The sub-retinal pigment epithelial deposits that are a hallmark of age-related macular degeneration contain both C3b and millimolar levels of zinc. C3 is the central protein of complement, whereas C3u is formed by the spontaneous hydrolysis of the thioester bridge in C3. During activation, C3 is cleaved to form active C3b, then C3b is inactivated by Factor I and Factor H to form the C3c and C3d fragments. The interaction of zinc with C3 was quantified using analytical ultracentrifugation and x-ray scattering. C3, C3u, and C3b associated strongly in >100 μm zinc, whereas C3c and C3d showed weak association. With zinc, C3 forms soluble oligomers, whereas C3u and C3b precipitate. We conclude that the C3, C3u, and C3b association with zinc depended on the relative positions of C3d and C3c in each protein. Computational predictions showed that putative weak zinc binding sites with different capacities exist in all five proteins, in agreement with experiments. Factor H forms large oligomers in >10 μm zinc. In contrast to C3b or Factor H alone, the solubility of the central C3b-Factor H complex was much reduced at 60 μm zinc and even more so at >100 μm zinc. The removal of the C3b-Factor H complex by zinc explains the reduced C3u/C3b inactivation rates by zinc. Zinc-induced precipitation may contribute to the initial development of sub-retinal pigment epithelial deposits in the retina as well as reducing the progression to advanced age-related macular degeneration in higher risk patients.

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

Background The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9. Results The FunFOLD method shows a significant improvement over the best available servers and is shown to be competitive with the top manual prediction groups that were tested at CASP8. The FunFOLD method is also competitive with both the top server and manual methods tested at CASP9. When tested using common subsets of targets, the predictions from FunFOLD are shown to achieve a significantly higher mean Matthews Correlation Coefficient (MCC) scores and Binding-site Distance Test (BDT) scores than all server methods that were tested at CASP8. Testing on the CASP9 set showed no statistically significant separation in performance between FunFOLD and the other top server groups tested. Conclusions The FunFOLD software is freely available as both a standalone package and a prediction server, providing competitive ligand binding site residue predictions for expert and non-expert users alike. The software provides a new fully automated approach for structure based function prediction using 3D models of proteins.

MOTIVATION:We propose a novel method for scoring the accuracy of protein binding site predictions-the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community-wide prediction experiment-CASP8. While being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of non-binding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores while also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions.AVAILABILITY:http://www.reading.ac.uk/bioinf/downloads/.