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

Abstract

Understanding the molecular determinants of specificity in proteinprotein 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.T he large majority of cellular functions are executed and controlled by interacting proteins. With up to several thousand types of proteins expressed in a typical bacterial cell at a given time, their concerted specific interactions regulate the interplay of biochemical processes that are the essence of life. Many protein interactions are transient, allowing proteins to mate with several partners or travel in cellular space to perform their functions. Understanding these transient interactions is one of the outstanding challenges of systems biology (reviewed in ref. 1). The characterization of the molecular details of the interface formed between known interacting proteins is a requirement for understanding the molecular determinants of protein-protein interaction, the knowledge of which may be important for a variety of applications including synthetic biology, e.g., designing new specific interaction between proteins (reviewed in ref.2), and pharmaceutics, e.g., protein interaction surfaces as drug targets (reviewed in ref.3).Experimental approaches to identify surfaces of interaction between proteins such as surface-scanning mutagenesis and cocrystal structure generation are arduous and/or serendipitous. Cocrystal structures provide the best molecular resolution but are particularly challenging to obtain for transient interactio...

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

“…Phylogenetic trees of drug-naive and drugtreated HIV-1-infected patients have been shown to exhibit star-like phylogenies (Keele et al 2008;Gupta and Adami 2016), and thus phylogenetic corrections are not needed. Further, phylogenetic corrections based on pairwise sequence similarity cut-offs of 40% of sequence length or more which are common in studies utilizing direct coupling analysis (DCA) (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014 of protein families would drastically reduce the number of effective sequences in our MSA and would lead to mischaracterization of the true underlying mutational landscape. We note that Potts models of other HIV-1 protein sequences under immune pressure have been parameterized with no phylogenetic corrections Mann et al 2014;Barton et al 2016b).…”

“…Due to the constraints on relationship between bivariate marginals P ij ; P ik ; P jk and the fact that the univariate marginals can be derived entirely from the bivariate marginals, only LðQ À 1Þ þð L 2 ÞðQ À 1Þ 2 of these LQ þ ð L 2 ÞQ 2 parameters are independent. Several schemes have been developed and used by others to fully constrain the Hamiltonian (e.g., see Weigt et al 2009;Morcos et al 2011). Further, the fully constrained Potts Hamiltonian is "gauge invariant" such that the probability P m ðr k Þ is unchanged by (a) a global bias added to the fields, h i ðr i Þh i ðr i Þ þ b, (b) a per-site bias added to the fields h i ðr i Þ !…”

“…Recently, probabilistic models, called Potts models, have been used to assign scores to individual protein sequences which correlate with experimental measures of fitness (Haq et al 2012;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017). These advances build upon previous and ongoing work in which Potts models have been used to extract information from sequence data regarding tertiary and quaternary structure of protein families (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014Marks et al 2012;Sulkowska et al 2012;Sutto et al 2015;Barton et al 2016a;Haldane et al 2016;Jacquin et al 2016) and sequencespecific quantitative predictions of viral protein stability and fitness (Haq et al 2012;Shekhar et al 2013;Barton et al 2016b;Butler et al 2016).…”

“…Several schemes have been developed to solve the Inverse Ising problem, from very fast but very approximate mean field solutions and messagepassing algorithms (Mézard and Mora 2009;Weigt et al 2009;Morcos et al 2011), fast and less approximate pseudolikelihood maximization solutions (Ekeberg et al 2013), to computationally demanding Monte Carlo algorithms (Mora and Bialek 2011;Shekhar et al 2013;Sutto et al 2015;Haldane et al 2016) and cluster expansion methods (Barton et al 2016a). More information regarding specifics of different inference methodologies can be found in the following reviews and the references within (Marks et al 2012;Levy et al 2017).…”

Determinants of specificity in two-component signal transduction

“…These specificity-determining residues were identified initially through computational analyses of amino acid covariation in large sets of cognate, co-operonic two-component proteins [12,26,27] (Fig. 3a).…”