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Iterative Optimization of the Cyclic Peptide SFTI-1 Yields Potent Inhibitors of Neutrophil Proteinase 3.

ABSTRACT: Neutrophils produce at least four serine proteases that are packaged within azurophilic granules. These enzymes contribute to antimicrobial defense and inflammation but can be destructive if their activities are not properly regulated. Accordingly, they represent therapeutic targets for several diseases, including chronic obstructive pulmonary disease, cystic fibrosis, and rheumatoid arthritis. In this study, we focused on proteinase 3 (PR3), a neutrophil protease with elastase-like specificity, and engineered potent PR3 inhibitors based on the cyclic peptide sunflower trypsin inhibitor-1 (SFTI-1). We used an iterative optimization approach to screen targeted substitutions at the P1, P2, P2', and P4 positions of SFTI-1, and generated several new inhibitors with K i values in the low nanomolar range. These SFTI-variants show high stability in human serum and are attractive leads for further optimization.

SUBMITTER: Tian S

PROVIDER: S-EPMC6691563 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Iterative Optimization of the Cyclic Peptide SFTI-1 Yields Potent Inhibitors of Neutrophil Proteinase 3.

Tian Sixin S Swedberg Joakim E JE Li Choi Yi CY Craik David J DJ de Veer Simon J SJ

ACS medicinal chemistry letters 20190719 8

Neutrophils produce at least four serine proteases that are packaged within azurophilic granules. These enzymes contribute to antimicrobial defense and inflammation but can be destructive if their activities are not properly regulated. Accordingly, they represent therapeutic targets for several diseases, including chronic obstructive pulmonary disease, cystic fibrosis, and rheumatoid arthritis. In this study, we focused on proteinase 3 (PR3), a neutrophil protease with elastase-like specificity, ...[more]

PMID: 31413811

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Project description:BACKGROUND: Sunflower trypsin inhibitor-1 (SFTI-1) and Momordica cochinchinensis trypsin inhibitor-II (MCoTI-II) are potent protease inhibitors comprising a cyclic backbone. RESULTS: Elucidation of structure-activity relationships for SFTI-1 and MCoTI-II was used to design inhibitors with enhanced inhibitory activity. CONCLUSION: An analog of MCoTI-II is one of the most potent inhibitors of matriptase. SIGNIFICANCE: These results provide a solid basis for the design of selective peptide inhibitors of matriptase with therapeutic potential. The type II transmembrane serine protease matriptase is a key activator of multiple signaling pathways associated with cell proliferation and modification of the extracellular matrix. Deregulated matriptase activity correlates with a number of diseases, including cancer and hence highly selective matriptase inhibitors may have therapeutic potential. The plant-derived cyclic peptide, sunflower trypsin inhibitor-1 (SFTI-1), is a promising drug scaffold with potent matriptase inhibitory activity. In the current study we have analyzed the structure-activity relationships of SFTI-1 and Momordica cochinchinensis trypsin inhibitor-II (MCoTI-II), a structurally divergent trypsin inhibitor from Momordica cochinchinensis that also contains a cyclic backbone. We show that MCoTI-II is a significantly more potent matriptase inhibitor than SFTI-1 and that all alanine mutants of both peptides, generated using positional scanning mutagenesis, have decreased trypsin affinity, whereas several mutations either maintain or result in enhanced matriptase inhibitory activity. These intriguing results were used to design one of the most potent matriptase inhibitors known to date with a 290 pm equilibrium dissociation constant, and provide the first indication on how to modulate affinity for matriptase over trypsin in cyclic peptides. This information might be useful for the design of more selective and therapeutically relevant inhibitors of matriptase.

Project description:BACKGROUND: We introduce Iterative Feature Removal (IFR) as an unbiased approach for selecting features with diagnostic capacity from large data sets. The algorithm is based on recently developed tools in machine learning that are driven by sparse feature selection goals. When applied to genomic data, our method is designed to identify genes that can provide deeper insight into complex interactions while remaining directly connected to diagnostic utility. We contrast this approach with the search for a minimal best set of discriminative genes, which can provide only an incomplete picture of the biological complexity. RESULTS: Microarray data sets typically contain far more features (genes) than samples. For this type of data, we demonstrate that there are many equivalently-predictive subsets of genes. We iteratively train a classifier using features identified via a sparse support vector machine. At each iteration, we remove all the features that were previously selected. We found that we could iterate many times before a sustained drop in accuracy occurs, with each iteration removing approximately 30 genes from consideration. The classification accuracy on test data remains essentially flat even as hundreds of top-genes are removed.Our method identifies sets of genes that are highly predictive, even when comprised of genes that individually are not. Through automated and manual analysis of the selected genes, we demonstrate that the selected features expose relevant pathways that other approaches would have missed. CONCLUSIONS: Our results challenge the paradigm of using feature selection techniques to design parsimonious classifiers from microarray and similar high-dimensional, small-sample-size data sets. The fact that there are many subsets of genes that work equally well to classify the data provides a strong counter-result to the notion that there is a small number of "top genes" that should be used to build classifiers. In our results, the best classifiers were formed using genes with limited univariate power, thus illustrating that deeper mining of features using multivariate techniques is important.

Dataset Information

Iterative Optimization of the Cyclic Peptide SFTI-1 Yields Potent Inhibitors of Neutrophil Proteinase 3.

Publications

Iterative Optimization of the Cyclic Peptide SFTI-1 Yields Potent Inhibitors of Neutrophil Proteinase 3.

Similar Datasets

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