On Evolutionary Conservation of Thermodynamic Coupling in Proteins*

By Anthony Fodor, Richard W. Aldrich. 2004

85 citations recorded by the source corpus.

The inherent complexity of thermodynamic coupling in proteins presents a major challenge in understanding and engineering protein function. Recent work has argued that the study of proteins can be simplified by the use of correlated mutations in the evolutionary record to locate a small subset of thermodynamically coupled residues that participate in functionally important, evolutionarily conserved energetic pathways. To test this hypothesis, we examined the predictions of correlated mutation algorithms for a number of proteins for which coupling between residues has been determined by analysis of double mutant cycles. We find that correlated mutation algorithms can find residue pairs that are physically close and that physically close residue pairs tend to be thermodynamically coupled. We find little evidence, however, for the hypothesis that thermodynamic coupling is limited to the subset of evolutionarily constrained residue positions.

Subfields: Molecular Biology

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Citations in the corpus

Protein sectors: evolutionary units of three-dimensional structure (2009, neutral): “One possible interpretation of this result is that covariation analyses fail to capture the essential design of proteins (Fodor and Aldrich, 2004), but, consistent with other studies (Kass and Horovitz, 2002; Lapedes et al., 1999; Lichtarge et al., 1996), the sector hypothesis suggests an alternate…”
Positive and Negative Design in Stability and Thermal Adaptation of Natural Proteins (2006, background): “Indeed, statistical analysis shows that overall correlation between distance between amino acids and degree of correlation in multiple sequence alignments does exist [37].”
Graphical models of residue coupling in protein families (2005, background): “Others compare the constraints to known energetic couplings inferred from double mutant experiments [7].”
Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments (2010, background): “However, the connection between thermodynamic coupling and covariation is still under debate as there is little evidence that thermodynamic coupling of residues is limited to covarying positions [26].”
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).”
Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction (2008, methodology): “First, we measured the fraction of pairs in contact (non-hydrogen atom separations (cid:4) 6 A˚) as a function of rank order using a variety of methods: MI , MIr (Martin et al ., 2005), MIp , MIa , OMES (Fodor and Aldrich, 2004a) or McBASC (Fodor and Aldrich, 2004a).”
Finding and Leveraging Structure in Learning Problems (2012, methodology): “Others, [72, 76, 78], have since developed similar methods.”
Using inferred residue contacts to distinguish between correct and incorrect protein models (2008, methodology): “However, more sophisticated energy functions have ∗ To whom correspondence should be addressed. not removed the computationally limiting task of exploring and evaluating the multitude of ways a linear peptide sequence can fold (Schueler-Furman et al., 2005).”
On the accuracy of inferring energetic coupling between distant sites in protein families from evolutionary imprints: Illustrations using lattice model (2009, background): “…that have given credence to the SCA,16,17,20 others have cast doubt on its efficacy in obtaining physically meaningful covariation in residues.14 ,21,22 A basic difficulty is that SCA attempts to glean meaningful coupling using evolutionary signals whereas structure-based methods, including…”
Graphical Models of Residue Coupling in Protein Families (2008, result): “Others compare the constraints to known energetic couplings inferred from double mutant experiments [ 7 ].”