Co-evolving residue detection software tools | Protein structure data analysis
Coevolution (covariation/correlated mutation) is the change of a biological object triggered by the change of a related object. For example, the coding genes of some interacting proteins are preserved or eliminated together in new species, or have similar phylogenetic trees. At the amino acid level, some residues under physical or functional constraints exhibit correlated mutations.
Introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks.
A package for accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization of phyletic data. The tool has the capability to infer biologically meaningful interactions. It is suitable for analyzing various binary-coded data and has the potential to facilitate further biological understanding with the discovery of additional coevolutionary networks.
A computationally implementation of direct-coupling analysis (DCA), which allows to evaluate the accuracy of contact prediction by DCA for a large number of protein domains. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined.
Separates direct from indirect interactions in the context of protein sequences. plmDCA was applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins. It outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. plmDCA should provide a natural choice for analysts interested in applying state-of-the-art protein structure prediction (PSP) to their protein of interest, as well as for researchers looking to further extend the theory and practical applicability of direct-coupling analysis (DCA).
A complete web tool that aims to estimate the mutual coevolutionary relationship between two residues in a protein family using corrected mutual information (MI). Correlations between positions in a multiple sequence alignment may prove structurally or functionally important in a given fold or protein family, becoming interesting targets for study.
Assists users in coupling analysis. EVcouplings serves as a modular basis for data analysis and developments. It includes functionalities such as alignment generation, EC calculation, de novo structure and mutation effect prediction, visualization of results, and comparison of predictions to experimental structures. This method provides (i) an easy-to-use command-line application and (ii) a modular Python package containing all functions, data structures, and pipelines that comprise the application.
Calculates covariance scores for constrained regions of background conservation. McBASC can be utilized as a covarying or highly conserved filter. This software gives an equally high score to conserved or covarying alignments and allows, without a reduction in score, substitution of conserved pairs of residues for covarying ones.