Bio-knowledge-based filters improve residue-residue contact prediction accuracy.

Motivation: Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. Results: We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. Availability and implementation: All data and scripts are available at http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. Supplementary information: Supplementary data are available at Bioinformatics online.

Forecasting residue-residue contact prediction accuracy.

MOTIVATION: Apart from meta-predictors, most of today's methods for residue-residue contact prediction are based entirely on Direct Coupling Analysis (DCA) of correlated mutations in multiple sequence alignments (MSAs). These methods are on average ∼40% correct for the 100 strongest predicted contacts in each protein. The end-user who works on a single protein of interest will not know if predictions are either much more or much less correct than 40%, which is especially a problem if contacts are predicted to steer experimental research on that protein. RESULTS: We designed a regression model that forecasts the accuracy of residue-residue contact prediction for individual proteins with an average error of 7 percentage points. Contacts were predicted with two DCA methods (gplmDCA and PSICOV). The models were built on parameters that describe the MSA, the predicted secondary structure, the predicted solvent accessibility and the contact prediction scores for the target protein. Results show that our models can be also applied to the meta-methods, which was tested on RaptorX. AVAILABILITY AND IMPLEMENTATION: All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/dcaQ/. CONTACT: malgorzata.kotulska@pwr.edu.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correlated mutations select misfolded from properly folded proteins.

MOTIVATION: The recently developed direct coupling analysis (DCA) method has greatly improved the accuracy with which residue-residue contacts can be predicted from multiple sequence alignments. Contact prediction accuracy, though, is still often not sufficient for complete ab initio protein structure prediction. DCA can, however, support protein structure studies in several ways. RESULTS: We show that DCA can select the better structure from among properly folded and misfolded variants. This idea was tested by comparing obsolete PDB files with their more correctly folded successors and by the comparison of structures with deliberately misfolded decoy models from the Decoys 'R' Us database. The DCA method systematically predicts more contacts for properly folded structures than for misfolded ones. The method works much better for X-ray structures than for NMR structures. AVAILABILITY AND IMPLEMENTATION: All data are available from http://comprec-lin.iiar.pwr.edu.pl/dcaVSmisfolds/ and http://swift.cmbi.ru.nl/dcaVSmisfolds/ . CONTACT: malgorzata.kotulska@pwr.edu.pl . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.