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The many faces of protease-protein inhibitor interaction.

ABSTRACT: Proteases and their natural protein inhibitors are among the most intensively studied protein-protein complexes. There are about 30 structurally distinct inhibitor families that are able to block serine, cysteine, metallo- and aspartyl proteases. The mechanisms of inhibition can be related to the catalytic mechanism of protease action or include a mechanism-unrelated steric blockage of the active site or its neighborhood. The structural elements that are responsible for the inhibition most often include the N- or the C-terminus or exposed loop(s) either separately or in combination of several such elements. During complex formation, no major conformational changes are usually observed, but sometimes structural transitions of the inhibitor and enzyme occur. In many cases, convergent evolution, with respect to the inhibitors' parts that are responsible for the inhibition, can be inferred from comparisons of their structures or sequences, strongly suggesting that there are only limited ways to inhibit proteases by proteins.

SUBMITTER: Otlewski J

PROVIDER: S-EPMC1142537 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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The many faces of protease-protein inhibitor interaction.

Otlewski Jacek J Jelen Filip F Zakrzewska Malgorzata M Oleksy Arkadiusz A

The EMBO journal 20050303 7

Proteases and their natural protein inhibitors are among the most intensively studied protein-protein complexes. There are about 30 structurally distinct inhibitor families that are able to block serine, cysteine, metallo- and aspartyl proteases. The mechanisms of inhibition can be related to the catalytic mechanism of protease action or include a mechanism-unrelated steric blockage of the active site or its neighborhood. The structural elements that are responsible for the inhibition most often ...[more]

PMID: 15775973

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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

The many faces of protease-protein inhibitor interaction.

Publications

The many faces of protease-protein inhibitor interaction.

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