swMATH ID: 17012
Software Authors: David T. Jones, Tanya Singh, Tomasz Kosciolek, Stuart Tetchner
Description: MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. 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
Homepage: http://bioinformatics.oxfordjournals.org/content/31/7/999.short
Related Software: Ontobee; PlantTFDB; eggNOG; ELM; dbCAN; antiSMASH; dissectHMMER; HPMV; ANNIE; FreeContact; CCMpred; NNcon; PconsFold; PSICOV; APOLLO; CONFOLD; ncPred; STITCH; MIND-BEST; SPAdes
Cited in: 1 Publication

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