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...

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SummaryProtein homology studies identified five kinases potentially capable of phosphorylating the Spo0F response regulator and initiating sporulation in Bacillus subtilis. Two of these kinases, KinA and KinB, were known from previous studies to be responsible for sporulation in laboratory media. In vivo studies of the activity of four of the kinases, KinA, KinC, KinD (ykvD) and KinE (ykrQ), using abrB transcription as an indicator of Spo0A,P level, revealed that KinC and KinD were responsible for Spo0A,P production during the exponential phase of growth in the absence of KinA and KinB. In vitro, all four kinases dephosphorylated Spo0F,P with the production of ATP at approximately the same rate, indicating that they possess approximately equal affinity for Spo0F. All the kinases were expressed during growth and early stationary phase, suggesting that the differential activity observed in growth and sporulation results from differential activation by signal ligands unique to each kinase.

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