Article pubs.acs.org/jcim
A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein−Protein Interaction Interfaces David Xu,‡,§ Yubing Si,‡ and Samy O. Meroueh*,†,‡ †
Department of Biochemistry and Molecular Biology, ‡Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States § Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, Indiana 46202, United States S Supporting Information *
ABSTRACT: The binding affinity of a protein−protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein−protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein−protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein−protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein−protein interactions do not optimally mimic protein−ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.
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INTRODUCTION Protein−protein interactions control nearly every aspect of normal cellular function. These interactions contribute to activating or suppressing signaling networks involved in pathological processes such as cancer.1,2 In cells, it is estimated that signaling pathways occur in a network of more than 200,000 protein−protein interactions.3−5 Protein−protein interactions range from transient to tight.6−8 They have been classified as primary, secondary, or tertiary depending on the architecture at the interface of the complex.9 Primary interfaces are generally simple, involving a short linear peptide bound to the surface of another protein. Secondary interactions consist of an α-helix or β-turn that is often ensconced into a well-defined cavity of the receptor. Tertiary interactions are more complex, sometimes involving multiple secondary structures such as αhelices and β-strands. The size of the contact surface increases from primary to tertiary, reaching more than 1500 Å2 in some cases for tertiary interactions.10 The amino acid composition of protein−protein interfaces favors certain amino acids, such as hydrophobic aromatic residues like tyrosine or tryptophan.10,11 © 2017 American Chemical Society
Charged residues such as arginine, lysine, and glutamic acid are also frequently found at interfaces and often engage residues through salt-bridge and π-cation interactions.12,13 There is intense interest in the development of small organic molecules to disrupt protein−protein interactions.14 Small molecules provide useful tools to dissect individual interactions of the cellular protein−protein interaction network. In addition, small molecules that disrupt protein−protein interactions associated with a disease can be further developed into therapeutic agents. Early strategies for developing protein− protein interaction inhibitors consisted of designing compounds with substituents that mimicked side chains of the protein ligand.15 This has worked particularly well for the development of peptidomimetic inhibitors of protein−protein interactions16 such as the MDM2•p53 interaction.17 Another approach consists of searching for fragment-like compounds that bind to cavities at the protein−protein interface.18 This Received: March 27, 2017 Published: August 2, 2017 2250
DOI: 10.1021/acs.jcim.7b00181 J. Chem. Inf. Model. 2017, 57, 2250−2272
Journal of Chemical Information and Modeling
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method has led to nanomolar and submicromolar inhibitors of Bcl-xL•Bak, 1 9 IL-2•IL-2Rα, 2 0 and more recently KEAP1•NRF2.21 Structure-based computational screening of commercially available chemical libraries have also been applied toward the discovery of small-molecule protein−protein interaction inhibitors. Virtual screening led to the discovery of fragment-like compounds that disrupted the interaction between IFN-α and its binding partner IFNAR.22 Another strategy combining docking and pharmacophore definitions led to inhibitors of the LEDGF•p75 interaction.23 In an innovative approach that used protein structures collected by explicitsolvent molecular dynamics simulations, we discovered a smallmolecule inhibitor of the tight uPAR•uPA interaction.24 A binding mode for the compound was predicted25 and independently confirmed.26 It has been suggested that small molecules disrupt tight protein−protein interactions by engaging or mimicking hot spots located at the protein−protein interface.10,27−30 Hot spots are amino acids that contribute substantially to the protein−protein interaction. They can be located either on the protein ligand or on the receptor. Hot spots are generally identified by alanine scanning studies, where individual amino acids are mutated to alanine, and the resulting impact on the binding affinity is measured using biochemical or biophysical methods. 31 Computational methods such as molecular dynamics simulations have also been successfully used.32,33 Despite the widely accepted view that disruption of hot-spot interactions is critical for the successful inhibition of protein− protein interactions, there is no systematic approach to take advantage of hot spots for the rational design of small-molecule antagonists. Strategies that consist of the design of compounds compounds to mimic hot spots on the protein ligand generally ignore hot spots located on the protein receptor. Similarly, compounds that are designed using fragment-based methods are conceived to bind to pockets on the receptor protein without regard to hot spots located on the protein ligand. Understanding how compounds engage and mimic hot spots could help guide the design of chemical libraries and to guide structure-based computational screening of these chemical libraries for the discovery of small-molecule protein−protein interaction inhibitors. Here, we subject protein−compound and protein−protein structures to explicit-solvent molecular dynamics simulations and free energy calculations. We select five protein−protein interactions that have been successfully inhibited with small molecules and for which there exists quality binding affinity data and co-crystal structures: Bcl-xL•Bak, MDM2•p53, XIAP•Smac, IL-2•IL-2Rα, and BRD4•H4. For each protein−protein and protein−compound complex, Molecular Mechanics, Generalized Born and Solvent-Accessible Surface Area (MM-GBSA) free energy calculations were carried out to determine the binding free energy for comparison to experimental binding affinities and IC50s. In addition, for each protein−protein complex, we determine the free energy change due to mutation of interface residues to alanine (computational alanine scan). We explore the interaction of each compound and protein ligand to the predicted hot spots on the receptor using per-residue decomposition energy calculations. Furthermore, we use pharmacophore modeling to investigate how effectively compounds mimic hot spots located on the protein ligand. Finally, molecular dynamics simulations are analyzed to compare the effect of compounds on the dynamics of the receptor to those of the protein ligand.
Article
MATERIAL AND METHODS
Structure Preparation. A set of protein structures corresponding to both protein−protein and protein−compound complexes were identified from 2P2I34 and UniProt35 (Table 1). In total, 36 protein−compound complexes for inhibitors of five protein−protein interactions that possess binding affinity data were collected. Existing experimental measure of binding affinity (Kd), inhibition constants (Ki), and concentration at which 50% inhibition is observed (IC50) for each complex were identified from the literature, whenever available. Protein−compound affinities were confirmed against data in the PDBbind,36 BindingMOAD,37 and BindingDB38 databases, whenever possible. The structure of each complex was retrieved and prepared using Protein Preparation Wizard in the Schrödinger software package (Schrödinger LLC, New York, NY, 2015). Bond orders were assigned, hydrogen atoms were added, and disulfide bonds were created. Water residues were retained, while additional ions and heteroatom groups aside from the inhibitor were discarded. Missing side chains and loops were introduced using the Prime module.39 The resulting protein and compound structures were protonated at pH 7.0 using PROPKA40 and Epik41 in Schrödinger, respectively. Schrödinger concurrently samples side-chain orientations and protonation states to optimize hydrogen bonding, charge interactions, and orientations of hydroxyl, thiol, and terminal amide groups of Asn, Gln, and His residues. Protein−protein complexes were separated into monomeric chains, and protein−inhibitor complexes were separated into protein and compound structures for molecular dynamics simulations. Molecular Dynamics Simulations. Prepared structures were used to run molecular dynamics simulations using the AMBER14 software package.80 Each compound was assigned AM1-BCC81 charges and gaff82 atom types using antechamber.83 Crystal water molecules were retained. Complexes were immersed in a box of TIP3P84 water molecules. No atom on the complex was within 14 Å from any side of the box. The solvated box was further neutralized with Na + or Cl − counterions using the tleap program. Simulations were carried out using the GPU accelerated version of the pmemd program with ff12SB85 and gaff82 force fields in periodic boundary conditions. All bonds involving hydrogen atoms were constrained by using the SHAKE algorithm,86 and a 2-femtosecond (fs) time step was used in the simulation. The particle mesh Ewald87 (PME) method was used to treat long-range electrostatics. Simulations were run at 298 K under 1 atm in NPT ensemble employing Langevin thermostat and Berendsen barostat. Water molecules were first energy-minimized and equilibrated by running a short simulation with the complex fixed using Cartesian restraints. This was followed by a series of energy minimizations in which the Cartesian restraints were gradually relaxed from 500 kcal·Å−2 to 0 kcal·Å−2, and the system was subsequently gradually heated to 298 K with a 48 ps molecular dynamics run. For each complex, we generated 10 independent simulations (replicates) that are each 10 nanoseconds (ns) in length. The initial velocities for each trajectory were randomly assigned using the built-in random number generator in Amber. This ensures that the trajectories follow different paths in phase space, resulting in better sampling of the molecular dynamics of the protein−protein or protein− ligand complexes. In total, 100 ns of simulation were carried out for each protein−protein and protein−compound complex. 2251
DOI: 10.1021/acs.jcim.7b00181 J. Chem. Inf. Model. 2017, 57, 2250−2272
Compound number
1 2 3 4 5 6
7 8 9 10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27
28 29
PDB
Bcl-xL•Bak 1BXL 1YSI 2YXJ 3QKD 3SP7 3SPF 4QVX
MDM2•p53 1YCR 1RV1 1T4E 3JZK 3LBK 3TU1 3W69 4DIJ 4ERE 4ERF 4HG7
XIAP•Smac 1G73 2JK7 2OPY 3CLX 3CM2 3EYL 3HL5 5C83
2252
IL-2•IL-2Rα 1Z92 1M48 1PW6 1PY2 1QVN
BRD4•H4 3UVW 2YEL 3MXF
− WSH JQ1
− FRG FRB FRH FRI
− BI6 CO9 X22 X23 SMK 9JZ 4YN
− IMZ DIZ YIN K23 07G LTZ BLF 0R2 0R3 NUT
− N3B N3C HI0 03B B50 3CQ
Ligand
423.5 457.0
447.5 535.5 662.6 685.6
491.6 439.5 493.6 493.6 507.7 507.6 423.6
686.4 580.2 536.2 461.3 476.9 698.7 574.5 519.4 477.4 581.5
551.6 815.4 839.5 908.5 525.1 669.8
Molecular weight (Da)
32 31
33 36 45 47
36 32 36 36 37 37 31
41 32 31 32 33 47 40 35 32 40
38 56 58 63 37 47
Heavy atom count
5.0 4.8
1.6 1.9 1.3 3.8
2.3 −1.6 0.7 −0.6 −0.1 1.1 1.3
6.8 4.2 5.4 5.7 3.5 4.3 5.8 5.2 4.7 6.5
5.8 5.1 5.3 6.9 3.3 4.8
aLogP
0.31 0.32
0.23 0.20 0.22 0.19
0.27 0.19 0.25 0.23 0.25 0.16 0.30
0.23 0.28 0.26 0.25 0.27 0.21 0.26 0.33 0.38 0.24
0.27 0.23 0.20 0.19 0.14 0.32
Ligand efficiencya
2.3 2.5
3.9 3.4 5.9 2.8
4.9 6.1 5.9 6.6 6.8 3.4 5.5
0.0 2.5 0.5 0.1 3.1 3.0 1.7 3.2 4.3 0.7
1.6 4.4 3.1 2.1 0.6 6.2
Lipophilic efficiency
Table 1. physico-chemical and Experimental Characteristics of Protein−Protein Interaction Complexes
4800 ± 400 52.5 49
13 8200
420 ± 20 67 ± 18 30000 ± 12000 250 870 220 34000
0.4
916 250
80
295
340 ± 30 36 ± 1.6 0.37 4.2
