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Optimization of protease-inhibitor interactions by randomizing adventitious contacts.

ABSTRACT: Polypeptide protease inhibitors are often found to inhibit targets with which they did not coevolve, as in the case of high-affinity inhibition of bacterial subtilisin by the leech inhibitor eglin c. Two kinds of contacts exist in such complexes: (i) reactive site loop-active site contacts and (ii) interactions outside of these that form the broader enzyme-inhibitor interface. We hypothesized that the second class of "adventitious" contacts could be optimized to generate significant increases in affinity for a target enzyme or discrimination of an inhibitor for closely related target proteases. We began with a modified eglin c, Arg-42-Arg-45-eglin, in which the reactive site loop had been optimized for subtilisin-related processing proteases of the Kex2/furin family. We randomized 10 potential adventitious contact residues and screened for inhibition of soluble human furin. Substitutions at one of these sites, Y49, were also screened against yeast Kex2 and human PC7. These screens identified not only variants that exhibited increased affinity (up to 20-fold), but also species that exhibited enhanced selectivity, that is, increased discrimination between the target enzymes (up to 41-fold for furin versus PC7 and 20-fold for PC7 versus furin). One variant, Asp-49-Arg-42-Arg-45-eglin, exhibited a Ki of 310 pM for furin and blocked furin-dependent processing of von Willebrand factor in COS-1 cells when added to the culture medium of the cells. The exploitation of adventitious contact sites may provide a versatile technique for developing potent, selective inhibitors for newly discovered proteases and could in principle be applied to optimize numerous protein-protein interactions.

SUBMITTER: Komiyama T

PROVIDER: S-EPMC166207 | biostudies-literature | 2003 Jul

REPOSITORIES: biostudies-literature

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Optimization of protease-inhibitor interactions by randomizing adventitious contacts.

Komiyama Tomoko T VanderLugt Bryan B Fugère Martin M Day Robert R Kaufman Randal J RJ Fuller Robert S RS

Proceedings of the National Academy of Sciences of the United States of America 20030627 14

Polypeptide protease inhibitors are often found to inhibit targets with which they did not coevolve, as in the case of high-affinity inhibition of bacterial subtilisin by the leech inhibitor eglin c. Two kinds of contacts exist in such complexes: (i) reactive site loop-active site contacts and (ii) interactions outside of these that form the broader enzyme-inhibitor interface. We hypothesized that the second class of "adventitious" contacts could be optimized to generate significant increases in ...[more]

PMID: 12832612

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Project description:Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.

Dataset Information

Optimization of protease-inhibitor interactions by randomizing adventitious contacts.

Publications

Optimization of protease-inhibitor interactions by randomizing adventitious contacts.

Similar Datasets

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