Compensating changes in protein multiple sequence alignments.

By William R. Taylor, K. Hatrick. 1994

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “alignments can be used to solve this problem [2,5,6,32,33].”
All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences (2015, methodology): “17 ) Realistic all-atom 3D models generated by EVfold_bb (full protein including long loops).”
3D Protein Structure Predicted from Sequence (2011, unknown): “In this paper we explore the idea that information gleaned from statistical analysis of multiple sequence alignments can be used to solve this problem [2,5,6,30,31].”
Correlated mutations contain information about protein-protein interaction. (1997, background): “Several groups have studied correlated mutations: technical differences between different approaches have led to con¯icting conclusions about the nature and intensity of this phenomena (Altschuh et al., 1987, 1988; Go È bel et al., 1994; Neher, 1994; Shindyalov et al., 1994; Taylor & Hatrick, 1994).”
Co-evolving residues in membrane proteins (2007, methodology): “…most commonly used relies on the calculation of a Pearson correlation coefficient to detect alignment positions with similar patterns of amino acid change (Gobel et al., 1994; Kundrotas and Alexov, 2006; Neher, 1994; Olmea and Valencia, 1997; Pollock and Taylor, 1997; Taylor and Hatrick, 1994).”
Adaptive computation and machine learning (1998, background): “Several other types of long-range regularities have been investigated, for example, the preference for identical or similar residue partners in beta-sheets [543, 570, 268, 45] and in close contact pairs [273], the long- and short-distance periodicity in packing density [175], and whether mutations i”
Emerging methods in protein co-evolution (2013, background): “Residue co‑evolution was originally assessed through detecting pairs of positions (two columns of the MSA) that have interdependent amino acid frequencies 23 or similar patterns of amino acid substitutions 7,9,10 .”
Prediction of contact maps with neural networks and correlated mutations. (2001, methodology): “Other methods more specifically address the problem of residue contact prediction using the information derived from the occurrence of correlated mutations in similar proteins (Göbel et al., 1994; Shidyalov et al., 1994; Taylor and Hatrick, 1994; Thomas et al., 1996).”
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] ”