Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues

By Tal Pupko, R. E. Bell, Itay Mayrose, Fabian Glaser, Nir Ben-Tal. Intelligent Systems in Molecular Biology, 2002

632 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6643ce8b883bf53989015951d0a32f701ec6e495

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Citations in the corpus

PredictProtein—an open resource for online prediction of protein structural and functional features (2014, neutral): “Evolutionary rates of amino acids are estimated based on evolutionary relatedness between the protein and its homologues using either empirical Bayesian (56) or maximum likelihood (57) methods.”
ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules (2016, background): “…to other popular alternatives based on consensus and relative entropy approaches, is that the evolutionary rates are estimated based on the phylogenetic relationships among the homologues and the specific dynamics of the analysed sequences using advanced probabilistic evolutionary models (16,17).”
ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids (2010, methodology): “(5) A phylogenetic tree is reconstructed based on the MSA, using the neighbor-joining algorithm as implemented in the Rate4Site program (4,5).”
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf (2023, methodology): “These are multiply aligned, and the MSA is used as input to a Rate4Site calculation of the evolutionary rate per site (Pupko et al. 2002).”
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation (2006, methodology): “Conservation has been widely used for function site prediction [26-28] and protein-protein interaction interface prediction [29-32], combined with other physiochemical properties.”
ConSeq: the identification of functionally and structurally important residues in protein sequences (2004, methodology): “We have developed Rate4Site, an algorithmic tool for the identification of functionally important regions in proteins by estimating the rate of amino acid substitutions at each position in a Multiple Sequence Alignment (MSA) of homologous proteins (Pupko et al., 2002).”
MSAViewer: interactive JavaScript visualization of multiple sequence alignments (2016, neutral): “MSAs can be used to understand genomic rearrangements (Darling et al. , 2004), to derive sequence homology (Altschul et al. , 1990) and to identify evolutionary rates (Pupko et al. , 2002).”
Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. (2004, methodology): “Using the same ML methodology, Pupko et al. (2002) developed the Rate4Site tool for the identiﬁcation of functional regions in proteins.”
An alignment confidence score capturing robustness to guide tree uncertainty. (2010, methodology): “Each data set of genuine protein sequences was used to reconstruct a phylogenetic tree using NJ. Site-speciﬁc evolutionary rates were estimated using rate4site (Pupko et al. 2002).”
Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach (2007, background): “We note that in general, more accurate tree topologies lead to better estimate of parameters (36,37).”
Sustainability and Stewardship of Glyphosate and Glyphosate-resistant Crops (2007, background): “Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues.”
ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures (2005, methodology): “(v) Position-specific conservation scores are computed using the empirical Bayesian (2) or maximum-likelihood (3) algorithms.”