Predicting functionally important residues from sequence conservation

By John A. Capra, Mona Singh. 2007

698 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “Evolution provides a multitude of informative ’experiments’ on mutational landscapes.”
Protein sectors: evolutionary units of three-dimensional structure (2009, background): “…the importance of protein residues is sequence conservation—the degree to which the frequency of amino acids at a given position deviates from random expectation in a wellsampled multiple sequence alignment of the protein family (Capra and Singh, 2007; Ng and Henikoff, 2006; Zvelebil et al., 1987).”
Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein (2013, methodology): “To calculate functional and evolutionary conservations, we measured the property entropy for each site in the library sequences and UniRef90-based multiple sequence alignment that were used to create the logo plots by the Protein Residue Conservation Prediction tool (Capra and Singh 2007).”
Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement (2012, neutral): “Conserved residues in query sequence are then identified from the MSA based on their Jensen-Shannon divergence score (Capra and Singh, 2007).”
MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites (2015, methodology): “length distribution), the compositions of four charged residues (Arg, Lys, Asp, and Glu), and an ortholog multiple alignment based measure of the degree of evolutionary sequence conservation shown to be predictive of presequences in our earlier work (37) but this time adopting the Jensen-Shannon div”
COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning (2020, methodology): “The phylogenetic tree was constructed using PhyML 64 , and the amino acid conservation was estimated by the Jensen-Shannon Divergence (JSD) 65 .”
Log-odds sequence logos (2015, neutral): “Capra and Singh (2007) constructed a dataset of 660 multiple alignments of enzyme families in which the catalytic sites are known, and we have applied the measures U, R, A and B to this dataset, always first weighting the raw amino acid counts in each column.”
MTBP, the partner of Treslin, contains a novel DNA-binding domain that is essential for proper initiation of DNA replication (2017, methodology): “Homology scores were calculated by the method of Capra and Singh (2007).”
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, background): “Sequence-based methods are based on the assumption that residues belonging to a ligand binding site, being functionally important, are conserved in the evolution of a protein family [56] (Fig.”
Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors (2009, background): “The prediction of a cohort of functionally important, specificity determining, or coevolving residues, without involving MI, has also been addressed extensively [15,18,19,57,77,78,80–83,83–117] and a few of these strategies were summarized by those previously cited and by Ortiz and colleagues [118] ”
Structural and functional characterization of G protein–coupled receptors with deep mutational scanning (2019, methodology): “) displays the sequence conservation of each residue across 55 b2AR orthologs from the OMA database (Capra and Singh, 2007; Altenhoff et al., 2018).”