Abstract
Here we introduce a quantitative structure-driven computational domain-fusion method, which we used to predict the structures of proteins believed to be involved in regulation of the subtilin pathway in Bacillus subtilis, and used to predict a protein-protein complex formed by interaction between the proteins. Homology modeling of SpaK and SpaR yielded preliminary structural models based on a best template for SpaK comprising a dimer of a histidine kinase, and for SpaR a response regulator protein. Our LGA code was used to identify multi-domain proteins with structure homology to both modeled structures, yielding a set of domain-fusion templates then used to model a hypothetical SpaK/SpaR complex. The models were used to identify putative functional residues and residues at the protein-protein interface, and bioinformatics was used to compare functionally and structurally relevant residues in corresponding positions among proteins with structural homology to the templates. Models of the complex were evaluated in light of known properties of the functional residues within two-component systems involving His-Asp phosphorelays. Based on this analysis, a phosphotransferase complexed with a beryllofluoride was selected as the optimal template for modeling a SpaK/SpaR complex conformation. In vitro phosphorylation studies performed using wild type and site-directed SpaK mutant proteins validated the predictions derived from application of the structure-driven domain-fusion method: SpaK was phosphorylated in the presence of 32P-ATP and the phosphate moiety was subsequently transferred to SpaR, supporting the hypothesis that SpaK and SpaR function as sensor and response regulator, respectively, in a two-component signal transduction system, and furthermore suggesting that the structure-driven domain-fusion approach correctly predicted a physical interaction between SpaK and SpaR. Our domain-fusion algorithm leverages quantitative structure information and provides a tool for generation of hypotheses regarding protein function, which can then be tested using empirical methods.
Highlights
Because proteins so frequently function in coordination with other proteins, identification and characterization of proteinprotein complexes are essential aspects of protein sequence annotation and function determination [1]
Methods that leverage structure information can overcome this limitation of sequencebased methods; the three-dimensional information provided by structure enables identification of related proteins even when their sequences are dissimilar
In this work we present a quantitative method for identification of protein interacting partners, and we demonstrate its use in modeling the structure of a hypothetical complex between two proteins that function in a bacterial signaling system
Summary
Because proteins so frequently function in coordination with other proteins, identification and characterization of proteinprotein complexes are essential aspects of protein sequence annotation and function determination [1]. Whereas sequence-based domain fusion methods can be highly successful in identifying putative functional relationships among proteins, the reliance on sequence homology limits detection to protein sequences with adequate levels of sequence identity. Another approach to identifying putative protein-protein interactions is described by Lu and coworkers [18], whereby sequence-based searches against the PDB database were performed in order to identify multidomain structures having at least one domain with good sequence identity to each putative interacting protein. It was determined that a structure-based protocol performed considerably better than did a sequence-based protocol in recovering known protein-protein interacting partners (86% recovery as opposed to 19%) in searches against a database of known
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