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Provides intuitive and user-friendly platform to query the patterns of conservation across 86 animal, fungal, plant and protist genomes. A protein query can be submitted either by selecting the name from whole-genome protein sets of the intensively studied species or by entering a protein sequence. The graphic output shows the profile of sequence conservation for the query and the most similar phylogenetic profiles for the proteins in the genome of choice. The user can also download this output in numerical form.
CoPAP / Co-evolution of Presence-Absence Patterns
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.
i-COMS / interprotein COrrelated Mutations Server
A web application to calculate correlated mutations between proteins. I-COMS allows to estimate covariation between residues of different proteins by four different covariation methods. It provides a graphical and interactive output that helps compare results obtained using different methods. I-COMS automatically builds the required MSA for the calculation and produces a rich visualization of either intraprotein and/or interprotein covariating positions in a circos representation. Furthermore, comparison between any two methods is available as well as the overlap between any or all four methodologies.
Enables coevolution analysis of protein residues with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods. A set of data preprocessing options are provided for improving the sensitivity and specificity of coevolution signal detection, including sequence weighting, residue grouping and the filtering of sequences, sites and site pairs. The system also provides facilities for studying the relationship between coevolution scores and inter-residue distances from a crystal structure if provided, which may help in understanding protein structures.
Predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PDB). It can also accept a user-generated alignment. Based on the alignment, co-evolving residues are then predicted using three different methods: Row and Column Weighing of Mutual Information, Mutual Information/Entropy and Dependency. Finally, InterMap3D generates high-quality images of the protein with the predicted co-evolving residues highlighted.
Identifies coevolving positions and their associated profile in DNA sequences while incorporating the underlying phylogenetic relationships. CoEv is able to discriminate between coevolving and non-coevolving positions and provides better specificity and specificity than other available approaches. The Coev model can help to distinguish coevolving from co-inherited pairs of positions, which are defined as combinations acquired once in the evolution of a lineage and inherited by all its descendants. This will help the community to classify highly divergent sequences and better interpret the function of new ones.
A method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. pMT largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information.
Performs simulations of the evolution of protein sequences under the constraints of a reconstructed phylogeny. CS-PSeq-Gen allows simulations to take into account the "root sequence" that initiates the simulation, or the rate heterogeneity among sites are specific on each particular protein family. The tool will allow some control on the simulated tree/branch lengths around an average value. It offers some facilities to generate sequences under such hypotheses, and proposes a basic scheme for their detection, that can be easily adapted by programmers.
plmDCA / pseudolikelihood maximization Direct-Coupling Analysis
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).
Detects coevolved amino acid networks in protein families of variable divergence. MaxSubTree captures the transition along the time scale evolution of a conserved position to a coevolved position, and provides a numerical evaluation of the degree of coevolution of pairs of coevolved residues in a protein. MaxSubTree drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed, and it can be applied with high accuracy to families of protein sequences with variable divergence.
QuatIdent / Quaternary Identifier
An ensemble identifier developed by fusing the functional domain and sequential evolution information. QuatIdent is a 2-layer predictor. The 1st layer is for identifying a query protein as belonging to which one of the following 10 main quaternary structural attributes ((1) monomer, (2) dimer, (3) trimer, (4) tetramer, (5) pentamer, (6) hexamer, (7) heptamer, (8) octamer, (9) decamer, and (10) dodecamer). If the result thus obtained turns out to be anything but monomer, the process will be automatically continued to further identify it as belonging to a homo-oligomer or heterooligomer. The overall success rate by QuatIdent for the 1st layer identification was 71.1% and that for the 2nd layer ranged from 84 to 96%.
BIS / Blocks In Sequences
Analyses the coevolution of amino-acid fragments in proteins and for detecting networks of fragments interaction. BIS allows detecting similarities in the evolutionary behavior of alignment positions within either small or conserved sets of protein sequences. It uses a counting formula that captures positional differences in aligned protein sequences and based on those it evaluates whether two or more positions underwent simultaneous mutations, that is whether they coevolved or not.
A web-based tool that provides a versatile analysis and visualization of pairwise coevolution of amino acid residues. CoeViz computes three covariance metrics: mutual information, chi-square statistic, Pearson correlation, and one conservation metric: joint Shannon entropy. Implemented adjustments of covariance scores include phylogeny correction, corrections for sequence dissimilarity and alignment gaps, and the average product correction. Visualization of residue relationships is enhanced by hierarchical cluster trees, heat maps, circular diagrams, and the residue highlighting in protein sequence and 3D structure. Unlike other existing tools, CoeViz is not limited to analyzing conserved domains or protein families and can process long, unstructured and multi-domain proteins thousands of residues long.
Assists users in visualizing and predicting correlated mutations/co-evolving residues in protein structures. visualCMAT is a web application that allows experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery. It can be used to understand the relationship between structure and function and to identify co-evolution patterns in protein superfamilies. It can also predict correlated substitutions in protein structures, classify them into physically interacting residues and long-range correlations, annotate and rank binding sites on the protein surface.
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