Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior.

By Itay Mayrose, Dan-Ovidiu Graur, Nir Ben-Tal, Tal Pupko. Molecular biology and evolution, 2004

452 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/da8f0f7e2d4224638bedb75520fc456dfeac3e24

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In Molecular Biology

Citations in the corpus

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, methodology): “The same as b for the positional diversity calculated by Rate4site (μ, as in c) [65].”
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).”
Predicting functionally important residues from sequence conservation (2007, methodology): “In contrast to the methods described earlier, the R4S algorithm (Mayrose et al. , 2004) uses a statistical model of evolution to estimate the rate of evolution, and thus the conservation, at each site.”
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf (2023, background): “…estimates the evolutionary rate per site in the alignment by explicitly taking into account the phylogenetic relationships among the homologues, as reflected in the tree, and the exchangeability probability between any pair of amino acids or nucleotides (Mayrose et al. 2004; Pupko et al. 2002).”
AlphaFold2 fails to predict protein fold switching (2022, methodology): “Specifically, we ran Rate4Site 21 single-folding and fold-switching regions were narrowly within the realm of statistical similarity p .074 ( Epps-Singleton test, Section 2).”
PredictProtein—an open resource for online prediction of protein structural and functional features (2014, unknown): “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.”
Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach (2007, methodology): “The empirical Bayesian method is particularly superior to the ML method when the number of homologous sequences analyzed is small (30).”
IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era (2019, methodology): “Site-specific rates can be estimated by the empirical Bayesian method via the –rate option or by ML (Mayrose et al. 2004) via the –mlrate option.”
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.”
Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites (2014, methodology): “For each structure, we then used the respective sequence alignment and phylogenetic tree to infer site-specific substitution rates with Rate4Site, using the empirical Bayesian method and the amino-acid Jukes-Cantor mutational model (aaJC) (Mayrose et al. 2004).”