Protein–Protein Interactions as Drug Discovery Targets

Abstract

Protein–protein interactions (PPIs) recently have been recognized as a major class of drug targets. Many of the successful “classical” biotechnology protein drugs are agonists or antagonists of PPIs and there are established pathways for their development. However, small molecular weight compounds as antagonists of PPIs still pose a major problem to discovery due to the inherent physicochemical properties of their targets. Recently, several small molecular weight antagonists of PPIs advanced into clinical trials and thus comprise a proof‐of‐concept to this young area in medicinal chemistry. This chapter summarizes the area of small molecular weight antagonists of PPIs with a focus on recent success stories. After a short introduction into the structural biology of PPIs, recognized important PPI targets, discovery pathways of their inhibitors are discussed. The PPI p53‐Mdm2 with potential applications in cancer is used as a case study to demonstrate to diversity of approaches leading to many different lead structures. In contrast, the discovery of IL‐2/IL‐2 rec. antagonists is discussed to highlight adaptive high affinity binding to hotspot regions.

iBRET Screen of the ABCD1 Peroxisomal Network and Mutation-Induced Network Perturbations

“…Automated detection of protein−protein interactions in living cells sets the basis for future demands such as mapping of dynamic networks 76 or tissue-specific networks. 77 When considering protein−protein interactions as potential pharmaceutical targets, 78,79 application of iBRET in compound library screens may constitute a significant added value to detect molecules that stabilize or disrupt protein complexes. 80 Applicability of iBRET in protein−protein interaction assays was further demonstrated in a whole-organelle peroxisomal screen.…”

Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

“…The optimised protocol for these so-called hydrocarbon-stapled peptides uses α-methyl-, α-alkenylglycines in a distance of i, i + 3/i + 4 for one helix turn or i, i + 7 for two helix turns, respectively, followed by Ru-catalysed cross-linking [7]. By this robust and reliable approach, a library of stapled peptides was generated influencing diverse α-helical dominated protein-protein interactions (PPI) spanning pathways involved in cancer, infectious diseases, metabolic diseases and neurological disorders [8], which had been considered undruggable for a long time due to their large contact area and shallow surface [9]. Since then, many other reactions have been investigated for macrocyclisation with the objective of peptide stapling [10] including lactam- [11,12], disulfide- [13], thioether- [14][15][16][17][18][19][20], triazole- [21,22], oxime- [23] and hydrazone formation [24] as well as multicomponent reactions such as the Ugi-or Petasis reaction [25][26][27][28][29][30][31][32].…”

Training host-pathogen protein–protein interaction predictors

“…4,1,2,5 Therefore, to understand functional mechanisms of proteins, it is very important to study their protein-protein interactions (PPIs). 6 A special type of PPIs is host-pathogen interactions (HPIs) that involve interactions between proteins from a pathogen (virus or bacteria) and its host. 7 According to the World Health Organization, each year more than 17 million people are killed by infectious diseases.…”

“…8,9 To fight these infectious diseases, it is important to identify HPIs as it is a key step in drug design and biological discovery of disease mechanisms. 6 Conventional wet lab techniques are expensive and time-consuming, making it almost impossible to assess all possible combinations of protein interactions between a pathogen and its host. 10,11 Therefore, computational approaches are used to predict HPIs.…”