A method to predict functional residues in proteins

By G. Casari, Christopher Sander, Alfonso Valencia. 1995

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “However, the fundamental link we propose between functional sectors and small-eigenvalue modes of the covariance matrix is important, since large-eigenvalue modes of the covariance matrix also contain confounding information about subfamily-specific residues [56] and phylogeny [57], and consistently”
Evolution-Based Functional Decomposition of Proteins (2015, unknown): “As we show below, one generative mechanism for this architecture comes from the tree-like structure of the alignment in which sequences are partitioned into functional subfamilies along which portions of one sector can diverge (43; 44).”
A family of evolution-entropy hybrid methods for ranking protein residues by importance. (2004, methodology): “proposed an interesting method in their 1995 work: consider the whole sequence as a vector in number-of-residue-types times lengthdimensional space, and think of the alignment as a matrix of such vectors.(3) Its eigenvectors should then carry the information about residue preference for each subfami”
From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012, unknown): “Combined with clustering approaches, PCA was applied to identify functional residues in [8].”
Identification of functionally conserved residues with the use of entropy–variability plots (2003, methodology): “9–14 Other groups have predicted functional motifs from an analysis of protein interaction surfaces using principal component analysis, 15 analysis of physicochemical descriptors to score protein– protein interactions, 16 search for motifs in Blocks databases, 17 or alignment of hinge regions.”
Assessment of ligand binding residue predictions in CASP9 (2011, methodology): “The assessment of this category consisted of the following three steps: (1) identification of biologically relevant ligands in the target structures, (2) definition of binding site residues, and (3) assessment of the prediction performance.”
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].”
Predicting protein-protein interactions from primary structure (2001, background): “These patterns typically comprise a small number of functional residues in each protein (Casari et al., 1995).”
Network analysis of protein structures identifies functional residues. (2004, methodology): “Some approaches rely on sequence conservation deduced from multiple sequence alignments, 50 some on structural features such as electrostatic 17,18 or structural clefts 51 and some on a combination of such features.”
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] ”
AL2CO: calculation of positional conservation in a protein sequence alignment (2001, methodology): “A vectorial method was proposed in predicting functionally important residues (Casari et al., 1995).”
Emerging methods in protein co-evolution (2013, methodology): “These methods can identify residues that define functional properties of that subfamily, such as substrate binding specificity of a given enzyme 18,19 .”