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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Development of a Backbone Cyclic Peptide Library as Potential Antiparasitic Therapeutics Using Microwave Irradiation
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Virtual screening using combinatorial cyclic peptide libraries reveals protein interfaces readily targetable by

Fergal J Duffy1,2, Darragh O'Donovan1,2, Marc Devocelle1,2

  • 1†School of Medicine and Medical Science, ‡Complex and Adaptive Systems Laboratory, ¶Conway Institute of Biomolecular and Biomedical Research, and §School of Biomolecular and Biomedical Science, University College Dublin, Dublin 4, Ireland, and.

Journal of Chemical Information and Modeling
|February 11, 2015
PubMed
Summary

Developing novel cyclic peptide drugs to target protein interactions is challenging. This study presents a computational method to identify promising protein interfaces for drug discovery, successfully finding a thrombin inhibitor.

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Identifying Protein-protein Interaction Sites Using Peptide Arrays
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Area of Science:

  • Biochemistry
  • Medicinal Chemistry
  • Computational Biology

Background:

  • Protein-protein and protein-peptide interactions are crucial for biological functions but difficult to target with small molecules.
  • Efficient computational and experimental screening methods are needed to identify viable therapeutic targets.
  • Macrocyclic compounds, particularly cyclic peptides, offer potential for modulating these interactions.

Purpose of the Study:

  • To develop and validate a computational method for identifying protein interfaces amenable to targeting with cyclic peptides.
  • To generate and screen virtual libraries of cyclic peptides against pharmacophore models of protein interaction sites.
  • To accelerate the discovery of lead macrocyclic compounds for therapeutic intervention.

Main Methods:

  • Generated combinatorial 3D virtual libraries of 108,659 disulfide-bonded cyclic peptides.
  • Created 372 reference pharmacophore models from Protein Data Bank structures of protein-protein and protein-peptide interactions.
  • Matched cyclic peptides against pharmacophores, focusing on turn structures and protein-binding motifs, and validated hits against thrombin.
  • Normalized results to exclude nonspecific cyclic peptides and identified top hits covering critical interaction 'hot spots'.

Main Results:

  • The screening identified cyclic peptides mimicking turn structures and protein-binding peptides.
  • Top hits effectively covered critical 'hot spot' interaction sites on target proteins.
  • Validated method by discovering a cyclic peptide inhibitor with lead-like activity against human alpha thrombin.
  • The computational approach successfully prioritized targetable protein interfaces.

Conclusions:

  • Computational screening of virtual cyclic peptide libraries is effective for identifying potential drug targets.
  • This method accelerates the selection of protein interfaces for lead compound screening.
  • It facilitates the development of novel macrocyclic drugs targeting protein-protein and protein-peptide interactions.