Correlated mutations contain information about protein-protein interaction.

By Florencio Pazos, Manuela Helmer-Citterich, G. Ausiello, Alfonso Valencia. 1997

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Citations in the corpus

SWISS-MODEL: homology modelling of protein structures and complexes (2018, positive): “Co-evolution methods, based on correlated amino acid mutations in deep multiple sequence alignments (MSA), are efficiently used to identify interacting proteins based on sequence information alone (6,7).”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, methodology): “Amino acid residue coevolution has been used to predict residue–residue interactions across interfaces with local statistical models ( Pazos et al., 1997 ; Halperin et al., 2006 ).”
Sequence co-evolution gives 3D contacts and structures of protein complexes (2014, positive): “, 1994; Pazos and Valencia, 2001), and subsequently used also to identify protein interactions (Pazos et al., 1997; Pazos and Valencia, 2002).”
Similarity of phylogenetic trees as indicator of protein-protein interaction. (2001, background): “The first data set was composed of 13 proteins of known structure for which two structural domains in close interaction are clearly visible (Pazos et al., 1997).”
Co-evolving residues in membrane proteins (2007, methodology): “The significance of each prediction was evaluated using a 2-test.”
From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012, unknown): “sidues is the following. From the covariance matrix C given in Eq. (3), we construct the Pearson correlation matrix Γ through the relationship Γij(a,b) = qX−1 c,d=1 (Di)−1(a,c) Cij(c,d) (Dj)−1(d,b) , (10) where the matrices Di are the square roots of the single-site correlation matrices, i.e. Cii(a,”
Determinants of protein function revealed by combinatorial entropy optimization (2007, background): “In another interesting application, joint specificity analysis across two protein families of potential interaction partners may lead to successful prediction of matched residues sets that are involved in protein-protein interactions [7,28].”
Graphical models of protein–protein interaction specificity from correlated mutations and interaction data (2009, background): “guiding docking,13 another study tested a number of typical coupling metrics and found them not to be indicative of intermolecular contacts.14 A recent large-scale study did find co-evolving pairs to be in general closer than other pairs.17 Another indirect manifestation of cross-coupling is in…”
The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem (2003, background): “A second type of input consideration is the use of profiles and correlated mutation (Gobel et al., 1994, Pazos et al., 1997, Olmea et al., 1999, Fariselli et al., 2001).”
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] ”
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, methodology): “…protein contact maps have been also used in a number of other structural tasks, for instance as protein fingerprints for rapid prediction of protein structures similarity [23-25], in the prediction of protein folding rates [26], protein disorder [27] and inter-domain contact regions [28].”
Comparative protein structure modeling by iterative alignment, model building and model assessment. (2003, background): “The following ®ve scores contribute to the composite score: pair (Pp) and surface (Ps) statistical potential values (step 10) (16); structural compactness (Sc in step 5) (6); harmonic average distance score (Ha) (34); the alignment score (As) in step 3.”
Recent progress and future directions in protein-protein docking. (2008, methodology): “Using a linear programming technique, Tobi and Bahar [115] developed a protein docking potential (PDP) based on 3 interaction centres (the side chain centroid, and backbone amide N and carbonyl O atoms) for each residue type.”
Predicting protein-protein interactions from primary structure (2001, methodology): “Previous research groups have presented predictive methodologies based on various principles, including correlated changes in amino acid sequence between interacting protein domains (Pazos et al., 1997); using genomic context to infer functional protein interactions between the gene products (Huynen”