Evolutionary information for specifying a protein fold

By M. Socolich, S. W. Lockless, W. P. Russ, Heather Lee, Kevin Gardner, Rama Ranganathan. 2005

423 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “In particular, analyses of large alignments of protein sequences have identified “sectors” of collectively correlated amino acids [1–6], which has enabled successful design of new functional sequences [3].”
Protein sectors: evolutionary units of three-dimensional structure (2009, background): “…August 21, 2009 ª2009 Elsevier Inc. 781 782 Cell 138, 774–786, August 21, 2009 ª2009 Elsevier Inc. SCA matrix alone to enable the design of artificial proteins that recapitulate the atomic structure and biochemical activity of the WW domain family (Russ et al., 2005; Socolich et al., 2005).”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Early work looked at the correlations as a measure of proximity [87, 92, 129, 207].”
Finding and Leveraging Structure in Learning Problems (2012, methodology): “The pioneering work of Lockless and Ranganathan [133] used an approach to determine probabilistic dependencies that they called SCA and observed that analyzing such patterns could provide insights into the allosteric behavior of the proteins and be used to design new sequences [174].”
Graphical models of protein–protein interaction specificity from correlated mutations and interaction data (2009, background): “The PR curve for a perfect classifier would go through the point (1, 1).”
Emerging methods in protein co-evolution (2013, methodology): “…analysis (SCA)‑like approaches have successfully been used to explore the implication of networks of co‑evolving residues in protein folding 50 and allosteric communication 51,53 (see the lower panel of the figure), and the functional importance of these sites has been experimentally…”
Are Biological Systems Poised at Criticality? (2010, background): “The results from [55, 56] generated a lot of interest, and since then several studies have tried to explore the collective properties of proteins using similar ideas.”
Graphical models of residue coupling in protein families (2005, background): “These and similar ideas have been given a significant boost with the promising results of Ranganathan and colleagues [32], [35].”
Probing the Mutational Interplay between Primary and Promiscuous Protein Functions: A Computational-Experimental Approach (2012, methodology): “SCA has been successfully applied to the design of allosteric communication in proteins [25] and to the design of artificial sequences able to fold to target structures [26].”
Graphical Models of Residue Coupling in Protein Families (2008, background): “demonstrated that coupling information enables the design of new, stably folded [35], and functional [32] WW domains.”
Consensus protein design (2016, methodology): “…additional statistical analysis that identifies coupling or covariation (Göbel et al., 1994; Lockless and Ranganathan, 1999; Atchley et al., 2000; Socolich et al., 2005; Talavera et al., 2015) and have generally been very successful in the engineering of stability (Magliery and Regan, 2004; Ozer…”
News from the protein mutability landscape. (2013, background): “[79] Socolich M, Lockless SW, Russ WP, Lee H, Gardner KH, Ranganathan R.”