Evolutionarily conserved pathways of energetic connectivity in protein families.

By S. W. Lockless, Rama Ranganathan. 1999

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “ ~, variance tends to increase in orthogonal directions involving the same sites. To illustrate this effect, let L= 3 and ~ = ( 1;1;0), and consider the sequences S~satisfying ~~S= 0 (namely (0;0;0); (1;1;0); (0;0;1); (1;1;1)). The last principal component is ~, with zero variance, and the first pri”
Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, neutral): “;ALÞ 1⁄4 1 Z exp ∑ i<j eijðAi;AjÞ þ∑ i hiðAiÞ : [5] The model parameters have to be fitted such that [4] is satisfied.”
Learning generative models for protein fold families (2011, background): “(8) is the sum of the L2 norms of each block.”
Protein structure prediction from sequence variation (2012, positive): “…principle by demonstrating that the predicted active sites of trypsin and Ras were particularly accurate relative to the accuracy of the rest of the protein when compared with the crystal structures, following the spirit of earlier work that used a weighted local mutual information method 50,51 .”
Protein sectors: evolutionary units of three-dimensional structure (2009, methodology): “The blue sector is connected through peptide ligand (Lockless and Ranganathan, 1999) and defines an allosteric mechanism for regulating binding affinity at the a2-b2 groove through molecular interactions at a distant surface site on the a1 helix (Peterson et al., 2004), and the red sector…”
The spatial architecture of protein function and adaptation (2012, methodology): “2a); (2) a fluorescence-activated cell sorting (FACS) step, in which bacterial populations carrying large libraries of mutations in the protein are selected for those cells displaying”
A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human (2016, background): “It is known that evolution in protein folds is essentially the result of many random mutation events (Lockless and Ranganathan, 1999).”
Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. (2003, methodology): “Our method for identifying pathways of energetic connectivity in proteins differs from that described by Freire and coworkers104 (where solvent-accessible surface areadependent empirical parameterizations yield a free energy function to evaluate conformational states) and from that described by Lock”
Origins of coevolution between residues distant in protein 3D structures (2017, background): “For example, residues distant from the active site in dihydrofolate reductase (DHFR) influence enzyme catalysis, and statistical coupling analysis (32) has suggested these residues are strongly correlated with a sector of evolutionary coupled sites within the protein (33).”
Directed Polymerase Evolution (2013, methodology): “By combining saturation mutagenesis based on statistical correlation analysis (SCA) [78] and ELISA-like screening (CST may not be used for this activity as non-natural nucleic acids cannot be included in the template without selecting for amplification), they also evolved TgoT mutant that can revers”
Protein Sectors: Statistical Coupling Analysis versus Conservation (2014, methodology): “[25], obtained there with a different alignment constructed using a structural alignment algorithm [13].”
Graphical Models of Residue Coupling in Protein Families (2008, methodology): “We adopt the basic idea of Lockless and Ranganathan but preserve the symmetry of the traditional MIðvi;vjÞ definition by employing the notion of conditional mutual information: " X XX MIðvi;vjjvkÞ¼ Pðvk¼cÞPðvi¼a;vj¼bjvk¼cÞc2A* a2A b2A # Pðvi¼a;vj¼bjvk¼cÞ log ; Pðvi¼ajvk¼cÞ Pðvj¼bjvk¼cÞ ð2Þ where ”
Co-evolving residues in membrane proteins (2007, methodology): “A P-value was determined based on the hypergeometric distribution and the probability to pick a residue pair in contact by random to estimate the significance of the obtained prediction.”
Single Layers of Attention Suffice to Predict Protein Contacts (2020, methodology): “…in MSAs can be used to predict couplings has been widely used to infer structure and function from protein families (Korber et al., 1993; G¨obel et al., 1994; Lapedes et al., 1999; Lockless & Ranganathan, 1999; Fodor & Aldrich, 2004; Thomas et al., 2008; Weigt et al., 2009; Fatakia et al., 2009).”