In silico structure-based approaches to discover protein-protein interaction-targeting drugs - PubMed
- ️Sun Jan 01 2017
Review
In silico structure-based approaches to discover protein-protein interaction-targeting drugs
Woong-Hee Shin et al. Methods. 2017.
Abstract
A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.
Keywords: Fragment-based drug discovery; Hotspot; PPI drug; Protein-ligand docking; Protein-protein interaction; Virtual screening.
Copyright © 2017 Elsevier Inc. All rights reserved.
Figures

A. Illustration of PPI. Blue and green circles represent proteins. Red and white colored regions are core and surrounding rim regions of the PPI, respectively. B. SMPPII binds to PPI to inhibit binding. C. Illustration of a traditional binding pocket bound with a ligand. D. Example of PPI, a complex of IL-2 (blue) and IL-2α (green) (PDB ID: 1Z92). E. SP4206 (magenta), an inhibitor of IL-2 and IL-2α, binds with IL-2 (PDB ID: 1PY2). F. AC1L9LOG (magenta), an inhibitor of lymphocyte-specific kinase (gold), binds to its target protein (PDB ID: 1QPE).

Crystal structures of typical PPI drugs binding to their targets. A. Benzodiazepinedione binds to HDM2 (PDB ID: 1T4E). Benzodiazepinedione has three benzene-like rings out of four rings, 19 multiple bonds, 18 aromatic carbons. Its molecular weight, polar surface area, and logP are 581.2 Da, 87 Å2, and 6.1, respectively. B. ABT-737 binds to Bcl-xL (PDB ID:.2YZJ) The drug has five benzene-like rings out of six rings, 34 multiple bonds, 30 aromatic carbons. Its molecular weight and polar surface area, and logP are 813.4 Da, 164 Å2, and 9.1, respectively. The ligands are colored in cyan and the protein surfaces are colored in gold.

Overview of the EleKit algorithm. A receptor protein (RP) is shown in gray, the ligand protein (LP) is in green, and the ligand small molecule (LSM) is shown at the bottom. (1) a mask of RP (gray) is created; (2) a near-but-not-inside mask of LP (red) is created; (3) a near-but-not-inside mask of LSM (blue) is created, (4) the logical conjunction of the three masks is used to select points to correlate from the electrostatic potentials of LP and LSM. The figure is Figure 2 of the original paper of the Elekit method (Electrostatic similarities between protein and small molecule ligands facilitate the design of protein-protein interaction inhibitors, Voet A, Berenger F, Zhang KYJ, PLOS One, 8: e75762,
https://doi.org/10.1371/journal.pone.0075762.g002). The caption is modified from the original.

DARC and DARC 2.0 ray casting. Left, In DARC, ray casting is performed from an origin behind the pocket. Right, In DARC 2.0, ray casting is performed from four additional origins. (The figure is modified from Figure 2 of the original paper of DARC 2.0: DARC 2.0: Improved docking and virtual screening at protein interaction sites. Gowthaman R, Lyskov S, Karanicolas J, PLOS One, 10: e0131612,
https://doi.org/10.1371/journal.pone.0131612.g002). The caption is modified from the original.

Building “exemplars” from surface pockets. A, Bcl-xL (grey surface) is shown in complex with an inhibitor (spheres). B, The protein surface features a large pocket (small spheres) that is complementary in shape to the inhibitor. C, From this surface pocket, an exemplar is built: The exemplar is comprised of hydrogen bond donors (yellow) and acceptors (magenta) that complement surface polar groups on the protein, and hydrophobic atoms that fill the remainder of the surface pocket (cyan). (The figure is Figure 1 in the original paper of this method: Selectivity by small-molecule inhibitors of protein interactions can be driven by protein surface fluctuations. Johnson Dk, Karanicolas J, PLoS Comp. Biol., 11: e1004081,
https://doi.org/10.1371/journal.pcbi.1004081.g001). The caption is modified from the original.
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