Dataset Information

Iterative Nonproteinogenic Residue Incorporation Yields ?/?-Peptides with a Helix-Loop-Helix Tertiary Structure and High Affinity for VEGF.

ABSTRACT: Inhibition of specific protein-protein interactions is attractive for a range of therapeutic applications, but the large and irregularly shaped contact surfaces involved in many such interactions make it challenging to design synthetic antagonists. Here, we describe the development of backbone-modified peptides containing both ?- and ?-amino acid residues (?/?-peptides) that target the receptor-binding surface of vascular endothelial growth factor (VEGF). Our approach is based on the Z-domain, which adopts a three-helix bundle tertiary structure. We show how a two-helix "mini-Z-domain" can be modified to contain ? and other nonproteinogenic residues while retaining the target-binding epitope by using iterative unnatural residue incorporation. The resulting ?/?-peptides are less susceptible to proteolysis than is their parent ?-peptide, and some of these ?/?-peptides match the full-length Z-domain in terms of affinity for receptor-recognition surfaces on the VEGF homodimer.

SUBMITTER: Checco JW

PROVIDER: S-EPMC5384266 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Iterative Nonproteinogenic Residue Incorporation Yields α/β-Peptides with a Helix-Loop-Helix Tertiary Structure and High Affinity for VEGF.

Checco James W JW Gellman Samuel H SH

Chembiochem : a European journal of chemical biology 20170109 3

Inhibition of specific protein-protein interactions is attractive for a range of therapeutic applications, but the large and irregularly shaped contact surfaces involved in many such interactions make it challenging to design synthetic antagonists. Here, we describe the development of backbone-modified peptides containing both α- and β-amino acid residues (α/β-peptides) that target the receptor-binding surface of vascular endothelial growth factor (VEGF). Our approach is based on the Z-domain, w ...[more]

PMID: 27897370

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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 Nonproteinogenic Residue Incorporation Yields ?/?-Peptides with a Helix-Loop-Helix Tertiary Structure and High Affinity for VEGF.

Publications

Iterative Nonproteinogenic Residue Incorporation Yields α/β-Peptides with a Helix-Loop-Helix Tertiary Structure and High Affinity for VEGF.

Similar Datasets

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