Systematic Targeting of Protein-Protein Interactions

Abstract

Over the past decade, protein-protein interactions (PPIs) have gone from being neglected as 'undruggable' to being considered attractive targets for the development of therapeutics. Recent advances in computational analysis, fragment-based screening, and molecular design have revealed promising strategies to address the basic molecular recognition challenge: how to target large protein surfaces with specificity. Several systematic and complementary workflows have been developed to yield successful inhibitors of PPIs. Here we review the major contemporary approaches utilized for the discovery of inhibitors and focus on a structure-based workflow, from the selection of a biological target to design.

Keywords: fragment-based design; inhibitors; protein-domain mimics; protein–protein interactions; rational design; screening.

Figure 1

Approaches to inhibitor design can be categorized into phenotypic screening, target-based screening and structure-based design. Left: Phenotypic Screening. A compound library is screened in a model system (i.e. cells, mice, flies) and analyzed for a specific phenotype. Center: Target-Based Screening. A library is screened against a particular protein target of interest in cell free or cell culture assays. Right: Structure-Based Design. A protein of interest is computationally assessed to design a modulator. Binding and biophysical assays are then performed on designed modulators to determine the best compound.

Figure 2

Modulators for PPIs may function using orthosteric and allosteric mechanisms to lead to PPI inhibition or stabilization.

Figure 3

Computational analysis of PPIs. Starting from the native structure, alanine scanning mutagenesis (left) can be performed on the “ligand” to quantify how much each contact residue contributes to the overall binding of the complex. The example shows a phenylalanine residue mutated to alanine to analyze the contribution of Phe to binding. While, fragment-centric topographical mapping of the “receptor’s” surface (right) reveals underutilized contact surface area. The native residue may not be optimal and a nonnatural amino acid may provide added contacts. Surface mapping allows judicious exploration of nonnatural residues.

Figure 4

Structure-guided design of PPI inhibitors. Hot spot contact residues (hot pink spheres) are identified through experimental or computational alanine scanning, mapped on the ligand protein (yellow ribbon), and analyzed to understand how they interact with the receptor protein surface (grey surface). Protein domain mimetics (PDMs) with high hot spot density (blue ribbon) can be chosen as starting points for inhibitor design. Nonnative residues may be utilized to optimize binding interactions. In a fragment-based design (FBD) approach, the consecutive hot spots on the high-density domain may be utilized as initial fragments, which can be optimized via fragment linking or growing.

Figure 5

Fragment-based design. Top: Fragment growing. A single fragment is progressively grown to optimize contacts with the target protein. Bottom: Fragment linking. Multiple fragments that bind in nearby sites are individually optimized and subsequently linked together.

Figure 6

Surface-exposed protein secondary structures often mediate binding of one protein with another (Top). Mimics of these folded domains can lead to potent PPI inhibitors. Several methods for mimicking protein motifs (Bottom). For example, mimicking an α-helix can be achieved by using side chain crosslinks, nonnatural oligomers that adopt helical conformations, hydrogen bond surrogates (HBS) and topographical/surface mimics that reproduce the side chain disposition. β-strands may be mimicked by nonnatural backbones, and turn inducers or macrocycles that hold peptide stands in β-sheet conformations. Emerging methods allow tertiary (3°) and quaternary (4°) structures to be stabilized by crosslinks or nonnatural residues. Key: Green denotes backbone analogs or nonnatural residues (i.e. β-amino acid residues). Blue and purple indicate constraints or cross-linkings. Orange indicates side chain residues placed to reproduce the orientation of the side chains coming from the canonical secondary structures. Protein (PDB: 3QKR), Helix Foldamer (PDB: 3S1K), Tertiary Foldamer (PDB: 2QMT).