Identification of direct residue contacts in protein–protein interaction by message passing

By Martin Weigt, Robert A. H. White, H. Szurmant, J. Hoch, Terence Hwa. 2009

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Understanding the molecular determinants of specificity in protein–protein interaction is an outstanding challenge of postgenome biology. The availability of large protein databases generated from sequences of hundreds of bacterial genomes enables various statistical approaches to this problem. In this context covariance-based methods have been used to identify correlation between amino acid positions in interacting proteins. However, these methods have an important shortcoming, in that they cannot distinguish between directly and indirectly correlated residues. We developed a method that combines covariance analysis with global inference analysis, adopted from use in statistical physics. Applied to a set of >2,500 representatives of the bacterial two-component signal transduction system, the combination of covariance with global inference successfully and robustly identified residue pairs that are proximal in space without resorting to ad hoc tuning parameters, both for heterointeractions between sensor kinase (SK) and response regulator (RR) proteins and for homointeractions between RR proteins. The spectacular success of this approach illustrates the effectiveness of the global inference approach in identifying direct interaction based on sequence information alone. We expect this method to be applicable soon to interaction surfaces between proteins present in only 1 copy per genome as the number of sequenced genomes continues to expand. Use of this method could significantly increase the potential targets for therapeutic intervention, shed light on the mechanism of protein–protein interaction, and establish the foundation for the accurate prediction of interacting protein partners.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “Raw covariance, correlation, and mutual information have confounding effects from indirect couplings (Weigt et al., 2009).”
Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “…very successfully employed via Direct Coupling Analysis (DCA) to identify strongly coupled residues that are in contact within a folded protein [36–38], to investigate folding [39], and to predict fitness [33, 40–45] and conformational changes [46, 47], as well as protein-protein interactions…”
Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, methodology): “An example of this approach was introduced in (16), however, the current mathematical formulation of the method as well as its computational implementation allows an analysis to a much larger scale.”
Learning generative models for protein fold families (2011, background): “More recently, [32] use a similar approach to determine residue contacts at a protein-protein interface.”
Protein structure prediction from sequence variation (2012, methodology): “Several groups have demonstrated that extracting covariation information from sequences is sufficient not only to estimate which pairs of residues are close in three-dimensional space 15–21 but also to fold a protein to reasonable accuracy 15,22–25 ( Table 1 ).”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “As noted above, the accuracy of these models is reduced by the confounding of direct and indirect correlations ( Lapedes et al., 1999 ; Weigt et al., 2009 ); the application of global statistical models to coevolution-based contact prediction across interfaces has been limited to the case of the…”