PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Motivation: The accurate prediction of residue–residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. Results: PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets, the L/5 (i.e. top-L/5 predictions for a protein of length L) precision for long-range contacts (sequence separation >23) was ≥0.5, which represents an improvement sufficient to be of significant benefit in protein structure prediction or model quality assessment. Availability: The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
- Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
- Ekeberg, Magnus; Hartonen, Tuomo; Aurell, Erik: Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences (2014)
- Marquez-Chamorro, Alfonso Eduardo; Asencio-Cortes, Gualberto; Divina, Federico; Aguilar-Ruiz, Jesus Salvador: Evolutionary decision rules for predicting protein contact maps (2014)