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plmDCA specifications


Unique identifier OMICS_13943
Name plmDCA
Alternative name pseudolikelihood maximization Direct-Coupling Analysis
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Multiple Sequence Alignment
Output data Scores for pairwise (direct) interactions
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Version 2.0
Stability Stable
Maintained Yes


No version available

Publications for pseudolikelihood maximization Direct-Coupling Analysis

plmDCA citations


Inferring repeat protein energetics from evolutionary information

PLoS Comput Biol
PMCID: 5491312
PMID: 28617812
DOI: 10.1371/journal.pcbi.1005584

[…] local potentials, simplifying the energetic description of complex natural systems [].In the last years new methods to analyze correlated mutations across a family of proteins have arisen (mfDCA [], plmDCA [, ], Gremlin [] to name a few). The main hypothesis behind these methods is that biochemical changes produced by a point mutation should be compensated by other mutations (along evolutionary t […]


A Biologically validated HCV E1E2 Heterodimer Structural Model

Sci Rep
PMCID: 5428263
PMID: 28303031
DOI: 10.1038/s41598-017-00320-7

[…] Six EC algorithms (metaPSICOV, ccmPRED, PConsC2, plmDCA, EPC-MAP and RaptorX) were selected to predict all possible E1E2 residue couples’ propensity of interaction, –. When possible, the algorithm was fed with the refined E1E2 alignment (plmDCA), wh […]


A combination of mutational and computational scanning guides the design of an artificial ligand binding controlled lipase

Sci Rep
PMCID: 5316958
PMID: 28218303
DOI: 10.1038/srep42592
call_split See protocol

[…] using the same E-value cutoff. This search produced an alignment containing 20.000 sequences covering 176 out of 181 residues of the query sequence. Covariation information was inferred employing the plmDCA (pseudolikelihood maximization for Potts models with direct coupling analysis algorithm), implemented in the EVcouplings webserver. Evolutionary constraints (EC) values were mapped onto the B-f […]


Interacting networks of resistance, virulence and core machinery genes identified by genome wide epistasis analysis

PLoS Genet
PMCID: 5312804
PMID: 28207813
DOI: 10.1371/journal.pgen.1006508

[…] sparse in the statistical sense as is the case with residue interaction matrices.The pseudolikelihood method allows an efficient correction for population structure by the reweighting scheme used in plmDCA for an MSA in protein analysis[], which ensures that highly similar sequences are not artificially inflating the support for direct dependence between alleles. We used the default reweighting s […]


The evolution of logic circuits for the purpose of protein contact map prediction

PMCID: 5398280
PMID: 28439455
DOI: 10.7717/peerj.3139

[…] nformation (MI) except that it deals only with direct correlations. This work also uses a mean-field heuristic to speed up the regular DCA computation.Further direct coupling analysis methods include plmDCA (), which uses a pseudolikelihood maximization method based on a 21-state Potts model of the amino acids in a multiple sequence alignment. The DCA method GREMLIN () extends the concept of pseud […]


Observation selection bias in contact prediction and its implications for structural bioinformatics

Sci Rep
PMCID: 5114557
PMID: 27857150
DOI: 10.1038/srep36679

[…] PSICOV performances (r = 0.70) on 150 proteins sampled from the STRUCT dataset. This confirms the previously determined correlation between available evolutionary information and CP performances for plmDCA, PSICOV, PconsC and PconsC2 on the PSICOV dataset.These results question the consistency of the accuracy that CP methods claim, since their published performances are calculated on protein data […]


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plmDCA institution(s)
Engineering Physics Program, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Computational Biology, AlbaNova University Centre, Stockholm, Sweden; Department of Information and Computer Science, Aalto University, Aalto, Finland; The Master’s Degree Programme in Translational Medicine, Biomedicum Helsinki, University of Helsinki, Finland; Aalto Science Institute, Aalto, Finland
plmDCA funding source(s)
This work was supported by the COIN (Centre of Excellence in Computational Inference), Academy of Finland (grant number 251170), and through the Finland Distinguished Professorship program, project 129024/Aurell.

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