Using Sequence Alignments to Predict Protein Structure and Stability With High Accuracy

By A. Lapedes, B. Giraud, Christopher Jarzynski. 2012

79 citations recorded by the source corpus.

We present a sequence-based probabilistic formalism that directly addresses co-operative effects in networks of interacting positions in proteins, providing significantly improved contact prediction, as well as accurate quantitative prediction of free energy changes due to non-additive effects of multiple mutations. In addition to these practical considerations, the agreement of our sequence-based calculations with experimental data for both structure and stability demonstrates a strong relation between the statistical distribution of protein sequences produced by natural evolutionary processes, and the thermodynamic stability of the structures to which these sequences fold.

Subfields: Molecular Biology

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Citations in the corpus

Protein structure prediction from sequence variation (2012, methodology): “For example, if residues A and B contact each other, as do residues B and C, then there is in general, a transitive influence observed between residues A and C (‘chain-ing effect’ 17,27 ).”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “…model ( Thomas et al., 2008 ) to distinguish between direct and indirect couplings ( Marks et al., 2011 ; Morcos et al., 2011 ; Hopf et al., 2012 ; Nugent and Jones, 2012 ; Jones et al., 2012 ; Lapedes et al., 2012 ; Marks et al., 2012 ; Sułkowska et al., 2012 ; Kamisetty et al., 2013 ).”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “The introduction of predictors using global models, inferring couplings between residues from the observed correlations, significantly increases the accuracy of co-variation based methods [12-16].”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, background): “Proposed initially more than a decade ago [21,22], it was not until very recently that the first successful MaxEnt approaches to the study of co-evolution were published [8,23].”
Disease variant prediction with deep generative models of evolutionary data (2021, background): “By contrast, unsupervised probabilistic models of evolutionary sequences alone have been notably successful at predicting the effects of variants on protein function and stability 23–26 and are fundamentally generalizable as they avoid learning from labels.”
Elucidating modes of activation and herbicide resistance by sequence assembly and molecular modelling of the Acetolactate synthase complex in sugarcane. (2016, background): “…a global statistical model (Thomas et al., 2008) to distinguish between direct and indirect couplings (Marks et al., 2011; Morcos et al., 2011; Hopf et al., 2012; Nugent and Jones, 2012; Jones et al., 2012; Lapedes et al., 2012; Marks et al., 2012; Sułkowska et al., 2012; Kamisetty et al., 2013).”
Protein Modelling: What Happened to the “Protein Structure Gap”? (2013, unknown): “Although similar approaches had been proposed before, recent breakthroughs in the handling of phylogenetic information and in disentangling indirect relationships have resulted in an improved capacity to predict contacts between different protein residues (Burger and van Nimwegen, 2010; de Juan et a”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “coupling parameters [8], including susceptibility propagation [9], mean-field approximation (mfDCA and in the original EVFold) [10–12], multivariate Gaussian modeling [13], pseudolikelihood maximization (implemented in plmDCA, CCMpred, the new EVFold, and GREMLIN) [14–17], Boltzmann network formalis”