4.7 Review

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Journal

Publisher

MDPI
DOI: 10.3390/ijms24097842

Keywords

protein-protein interaction; synthetic combinatorial approaches; peptides; peptidomimetics

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Protein-protein interfaces play crucial roles in pathophysiological pathways and are important targets for therapeutic compounds. However, identifying binding sites on protein surfaces and developing modulators of protein-protein interactions remain challenging due to their dynamic nature. Various strategies, including structural, computational, and combinatorial approaches, have been developed to characterize PPIs. Peptides are particularly potent and selective for inhibiting PPIs. This review discusses the impact of chemical peptide libraries in medicinal chemistry, focusing on recent applications, and explores the role of this methodology in the era of predictive approaches based on artificial intelligence in structural biology.
Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

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