PSICOV protocols

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PSICOV specifications


Unique identifier OMICS_03521
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes


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  • person_outline David Jones <>

Publication for PSICOV

PSICOV in pipelines

PMCID: 5862659
PMID: 29198957
DOI: 10.1016/j.jmb.2017.11.014

[…] with prosa-web (table s2). top scoring models were used for further analyses. structures were aligned using either dali or the “cealign” command in pymol., covariation analysis was performed using psicov and plmc script from evmutation package . initial sequence alignment was generated by jackhmmer after 10 iterations against nr70 database using polptn2 primase domain as a query. sequences […]

PMCID: 5506539
PMID: 28751967
DOI: 10.5256/f1000research.12138.r21733

[…] of a representative protein (pdb code: 1dj0) from the pseudouridine synthase domain family (cdd code: cd01291). 8, 30, 20 and 46 coevolutionary connections were predicted by mip , mcbasc , dca and psicov23 methods, respectively. interestingly, in this family, the average conservation score (al2co score: 0.65) for all sites are quite low (as shown by color coding), despite having higher […]

PMCID: 4642947
PMID: 26561852
DOI: 10.1371/journal.pone.0142407

[…] to the pfam msa with mafft-add ( residue positions absent from, or not resolved in, the structure sequence were eliminated with a custom script. the psicov algorithm was modified to output residue type together with residue position. the match for residue type ensured the high-scoring correlations were mapped correctly onto structure. physical […]

PMCID: 3956894
PMID: 24637808
DOI: 10.1371/journal.pone.0092197

[…] a significant boost in the quality of de novo structure predictions. here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm – fragfold, with psicov, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. using a comprehensive set of 150 diverse globular […]

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PSICOV in publications

PMCID: 5859286
PMID: 29556064
DOI: 10.1038/s41467-018-03503-6

[…] of new regulation in proteins through local modifications,. while distinct co-evolution-based approaches have also been developed to identify direct structural contacts in proteins (e.g., dca, psicov, and gremlin), sca uniquely focuses on discovering the collective networks of residues that underlie long-range coupling within a protein., a key feature of sca is the ability […]

PMCID: 5854445
PMID: 29505618
DOI: 10.1371/journal.ppat.1006908

[…] such as viral genotype sequences [–]. consistently, we failed to detect coevolution signals within our sets of e1e2 sequences when employing other existing coevolution methods (such as dca, psicov and evcouplings [–]), methods that we previously discussed in []., in strong contrast to our previous work [] that was critical to understand the computational power of coevolution analysis […]

PMCID: 5862659
PMID: 29198957
DOI: 10.1016/j.jmb.2017.11.014

[…] be compensated by the conserved arg in motif iv. to test this hypothesis, we analyzed covariation between the corresponding residues from 1427 sequences of polptn2 primase domain homologs using psicov (see ). it turned out that covariation between his (motif ii) and arg (motif iv) residue positions (polptn2, 179, and 231, respectively) is very low (covariation score of 0.03; c). however, […]

PMCID: 5713875
PMID: 29199978
DOI: 10.1107/S2059798317016436

[…] model can be found in applications such as gremlin (ovchinnikov et al., 2014) and ccmpred (seemayer et al., 2014). other implementations include sparse covariance matrix-inversion models such as psicov (seemayer et al., 2014; jones et al., 2012) or mean-field direct coupling analysis models such as evfold–mfdca (kaján et al., 2014)., supervised machine-learning algorithms […]

PMCID: 5836854
PMID: 28901583
DOI: 10.1002/prot.25379

[…] contacts (jones et al. ). at its core metapsicov is a meta‐predictor based on different covariation prediction algorithms, including mfdca (kaján et al. ), ccmpred (seemayer et al. ) and psicov (jones et al. ). when there isn't sufficient sequence data available to allow effective covariation analysis, the neural network is able to exploit information from additional machine […]

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PSICOV institution(s)
Department of Computer Science, Bioinformatics Group and Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, Malet Place, London, UK
PSICOV funding source(s)
UK Biotechnology and Biological Sciences Research Council (Grant reference: BB/F010451/1); UK Engineering and Physical Sciences Research Council (Grant reference: EP/H027203/1); Marie Curie Intra European Fellowship within the 7th European Community Framework Programme (Grant Agreement Number: PIEF-GA-2009-237292)

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