© 2016 Cloud Pharmaceuticals, Inc.
WHITE PAPER
AccurateInSilicoPredictionofLigandBindingPotencyinTherapeuticTargetsusingQuantumMolecularDesign
Cloud Pharmaceuticals, Inc. 6 Davis Dr.
Research Triangle Park, NC +1 919.424.6894
http://www.cloudpharmaceuticals.com Email: [email protected]
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Speeding up the development of novel drug candidates Traditionally, drug discovery and development are very time- and resource-consuming processes. Designing new drugs that bind to a specified protein target requires finding the best molecule in a vast chemical space. With an estimated 1065 stable molecules with a molecular weight below 850 available for exploration; examining this space by direct enumeration and evaluation is prohibitively costly. Research Triangle Park-based Cloud Pharmaceuticals’ Quantum Molecular Design (QMD) process enables a more efficient and accurate search of this chemical space. QMD uses a combination of reverse engineering and artificial intelligence methods to map from a set of desired properties to realistic chemical structures. QMD discovers novel drug candidates by using an enhanced version of the Inverse Design algorithm [1-5]. This proprietary technology combines an artificial intelligence (AI) and cloud computing to search virtual chemical space and accurately calculate binding affinities between biological targets and molecular drug candidates and then filters for drug-like properties. This approach enables the rapid and accurate discovery, design, and development of new therapeutics to improve human health and wellbeing and represents a paradigm shift in computational drug design, with broad applications in drug discovery. While Cloud Pharmaceutical’s primary proprietary advantage is its AI search method, this paper reports on a useful method of binding affinity calculation that is much more computationally efficient than competing methods, yet it achieves similar levels of accuracy. Quantum Molecular Design - QMD QMD accurately predicts the binding strength of ligands to potential protein targets. The very low accuracy of industry-standard docking tools used for calculating binding strength leads to results that are not predictive of experimental data. In contrast, Cloud uses QMD, which incorporates best-of-breed binding affinity algorithms. This paper focuses on the use of quantum mechanics/molecular mechanics (QM/MM) [6, 7] in conjunction with the Linear Interaction Energy (LIE) method [8-10] for ligand binding calculations in solvent and in protein environments. QM/MM is a multi-scale/multi-resolutions computational approach that describes the ligand using quantum mechanics, while the protein and water environment are described using force field methods. QM/MM calculations start from the three-dimensional coordinates of the targeted protein and ligands, allow flexibility of the whole system, explicit atomic description of all components (including water) and longer simulation times for biological systems. Moreover, due to the use of atomistic water in the protein, QM/MM calculations accurately represents the role of water interactions within the protein active site; therefore, it can incorporate water-bridging effects. We chose this method because it achieves similar binding affinity accuracy as other algorithms (such
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as Free Energy Perturbation, FEP), but require dramatically less computational cost and allow much more diverse ligand’s chemical space. Cloud combines this with the Inverse Design algorithm [1-5] to search large virtual chemical space to design novel ligands, which bind strongly to a specific therapeutic target. Numerous studies have shown QM/MM calculations to be highly accurate in predicting binding energies in medicinal chemistry research [11, 12]. QM/MM is also very effective in elucidating structural details of complex biological systems, such as G-protein coupled receptors (GPCRs) for which X-ray structures are unavailable [13, 14]. This results in correlation between calculated and measured ligand binding values of 0.6 to 0.8, much higher than the industry standard docking tools. Description of the protein ligand interactions using quantum mechanics provides an extra accuracy in comparison with methods that describe ligand by molecular mechanics only. Most molecular mechanics force field methods use fixed-point charges located on atom centers to describe electrostatic interaction. It means that the effect of polarization is not included in calculations and such models are unable to accurately reproduce electrostatic potential around polar molecules. To overcome these limitations, polarizable force field models are currently being developed [15, 16] but their applications to describe interaction in macromolecules have been very limited and have a complicated parameterization procedure. Polarizable force fields also may have problems with transferability: parameters that are developed for one class of ligands may not work for new class of ligands. [17] Our approach to describe ligands at a quantum level includes polarization effect explicitly and transferable for any class of protein-solvent ligand system. Specifics of the algorithm To calculate the binding energy between a ligand and a protein, fast molecular dynamics (MD) simulations are used to sample geometries of either the ligand-water or the ligand-protein-water (solvated protein) interactions, followed by calculation of the QM/MM energies along the MD trajectory. This methodology allows for sampling of both ligand conformations in water and in protein. Sampling the ligand-protein interactions in QMD has advantages over other computational methods (such as docking) because QMD allows ligand and protein to move, which in turn leads to accurate prediction of the ligand binding energy. For each protein target, the X-ray structure is downloaded from the protein data bank (PDB) and the system is prepared by adding missing backbone atoms, disulfide bridges, heavy atoms and assigning protonation states. The resulting structure is placed in a water box. The ligand conformers are added to the protein structure, and water and ions are equilibrated. The ligand solvation energy is calculated by Boltzmann averaging over all solvation energies of all snapshots from all conformers. This process is then mirrored within the protein’s binding pocket where the ligand conformer is strategically placed inside the pre-equilibrated solvated protein, and MD simulations are performed followed by several QM/MM calculations. Following the LIE protocol, binding energy is calculated from the difference between QM/MM energies and solvent accessible surface terms within the protein and the solvent. The binding energy equation [8-10] is given here:
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∆!! = ! !!"! ! − !!"! ! + ! !!"#! ! − !!"#! ! + ! !! − !!
Where α = 0.3, β = 0.6 and γ = 0.001 are the LIE coefficients similar to those reported in Su et
al. [8] ∆!! = ! !!"! ! − !!"! ! + ! !!"#! ! − !!"#! ! + ! !! − !! and ∆!! = ! !!"! ! − !!"! ! + ! !!"#! ! − !!"#! ! + ! !! − !! are the average QM/MM electrostatic energy in the ligand-protein complex and average QM/MM electrostatic energy of the free ligand in water, respectively.
∆!! = ! !!"! ! − !!"! ! + ! !!"#! ! − !!"#! ! + ! !! − !! and ∆!! = ! !!"! ! − !!"! ! + ! !!"#! ! − !!"#! ! + ! !! − !! are the average QM/MM van-der-Waals energy in the ligand-protein complex and average QM/MM van-der-Waals energy of the free ligand in water, respectively. Lastly, Ac and Aw are the solvent accessible surface area of the ligand in the complex and in water, respectively. Results The results below show the results for two known ligand sets in four protein targets: Beta-secretase 1 (BACE1, red squares), heat shock protein 90 (HSP90, blue diamonds), Eukaryotic translation initiation factor 2-alpha kinase 3 (PERK, brown triangles) and Non-receptor tyrosine-protein kinase 2 (TYK2, green triangles). The ligand binding sets were chosen based on criteria of similarity to the available X-ray ligands, diversity between data sets, the scale of the measured binding affinities, and the quality of experimental data. The conformers for each ligand in the data sets were generated using the BALLOON module. In order to calculate the binding interaction energy, each ligand conformer is placed in a pre-equilibrated water box or solvated protein system and undergoes a 100ps molecular dynamics simulation. After equilibration, 40 snapshots at 2ps time intervals were taken along the MD trajectory and QM/MM energies were collected and averaged. Then for each conformer of the ligand data set, the LIE energy is calculated from the collected solvent accessibility, and QM/MM energy terms using the equation above. The QM/MM calculations for both the solvent and protein environment took about 20 hours on a single CPU machine. The specifics of the protein and ligand sets used in these calculations can be found at the end of this document. A summary of the results for all four proteins can be found in the following table. The accuracy of prediction is evident by the high correlation between measured binding affinity and calculated LIE QM/MM binding energy and low root mean square error (RMSE) and mean average error (MAE). The plots of calculated binding energy vs. measured ligand binding activity for the 22 ligands of BACE1 and 32 ligands of PERK are also shown here, including the core ligand structures.
BACE1 TYK2 HSP90 PERK Number of compounds 22 16 70 32 Binding affinity range log(IC50 [nM]) 4.19 3.15 3.78 4.66
Crystal structure 4DJY 3LXN 2VCI 4G31 Correlation 0.73 0.71 0.60 0.86 Slope 1.10 0.60 0.85 1.58 Intercept -13.27 -12.73 -13.81 -13.86 RMSE 0.95 0.55 1.09 1.45 MAE 0.86 0.42 0.86 1.11
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Another benefit of using LIE QM/MM is the ability to calculate binding affinities of very diverse ligand populations, which may not be possible in other high accuracy computational chemistry methods such as FEP. Here are the ligands sets that were used:
H3C
X X
X
N NH2
N
O
R1
R1
O
N
NH
R2
R3
H3C
N
ONH
NH2+
R1 R2
OH
OH
N
NH
NX
R1
R2
O
OH
OH
R3
O
N
HN
R2
R1
HSP90
Cl
O
NH
N
NH
O
Cl
R1tyk2
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In the following figure we plot the LIE QM/MM calculated binding energy vs. measured log (IC50 [nM]) for 3 proteins: Beta-secretase 1 (BACE1, red squares), heat shock protein 90 (HSP90, blue diamonds), and Eukaryotic translation initiation factor 2-alpha kinase 3 (PERK, brown triangles). Also plotted on the same graph is the QM/MM calculated binding energy vs. measured log (Ki) for Non-receptor tyrosine-protein kinase 2 (TYK2, green triangles). The best-fitted line (with slope = 1.13; and intercept = -13.73) is also plotted, with correlation between measured and calculated values at 0.69. Also note that the LIE QM/MM predicted binding free energies for most of the ligands are within one log(IC50) unit of their experimental values, and only ~20 of 168 studied ligands differ from their experimental values by more than 2 log(IC50) units (dotted lines).
BACE1 TYK2 HSP90 PERK All Number of compounds 22 16 70 32 168 Correlation 0.73 0.71 0.60 0.86 0.69 RMSE 1.08 0.74 1.26 1.82 1.34 MAE 0.90 0.60 1.04 1.46 1.07
The statistics for each protein are shown in the table. The table enumerates the correlation, RMSE and MAE between the LIE QM/MM calculated and measured binding affinities for the ligands of a specific protein. Correlation between the LIE QM/MM calculated binding energies [in kcal/mol] and measured binding activities IC50 values [in nM] for all 168
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ligands over four proteins is 0.7 with RMSE of 1.34 kcal mol. These results show robustness, applicability and have sufficient accuracy to guide small molecule, computational drug design in a drug discovery program. Two other benefits of using LIE QM/MM calculations are the acceptable length of the calculations, as well as the ability to calculate binding affinities of very diverse ligand populations, which may not be possible in other high-accuracy computational chemistry methods such as FEP. Cloud Pharmaceuticals Quantum Molecular Design offering Cloud Pharmaceuticals offers its QMD process as a service via Microsoft Azure, Amazon EC2, Google Cloud Platform and private cloud implementations. In a typical engagement, the partner provides a target and its X-ray structure (in the absence of the X-ray structure, Cloud Pharmaceuticals will build the homology structural models), if not readily available from the Protein Database, and analyzes the target using QMD, along with a number of “bioinformatics filters” to eliminate toxic leads and/or leads with poor manufacturing properties. An analysis begins with the design of a scaffold for a small molecule or a peptide, based on client choice. The model is calibrated against published data or assays conducted by our experimental partners. The depth of the search of molecular space is determined by the partner. Upon completion of a partner engagement, typically a three-month effort for a single target, Cloud Pharmaceuticals provides a small, highly focused library of novel lead compounds or peptide drug candidates for each target.
Cloud Pharmaceuticals partnering Cloud Pharmaceuticals works with partners at all stages of drug development including early stage, preclinical, IND, and the clinic. We are currently engaged in drug discovery and development programs spanning a wide range of therapeutic targets including but not limited to cancer, inflammation, autoimmune, central nervous system diseases. We partner extensively with biotechnology firms, medical research institutions, pharmaceutical companies, governments, and academic organizations to jointly design new drugs. We then in-license the best drug candidates and further their development. For further information contact our business development team or write to [email protected].
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Proteins used in this white paper: BACE1: Beta-secretase 1, also known as beta-site amyloid precursor protein cleaving enzyme 1, is an aspartic-acid protease important in the formation of myelin sheaths in peripheral nerve cells. Drugs to block this enzyme (BACE1 inhibitors) may prevent the build up of beta-amyloid and may help slow or stop Alzheimer’s disease. The ligand set used here consisted of selection of 22 similar imino-hydratoin type compounds from the pdb.org database. The PDBs used were 3IN3, 3IN4, 3INH, 3INF, 3LHG, 3OOZ, 4B00, 4FRK, 3S7M, 4ACU, 4ACX, 4B1C, 3S7L, 4DJY, 4DJX, 4AZY, 4FRJ, 4DJW, 2VA6, 4FRI, 4DJV and 3IGB. The 4DJY.pdb x-ray structure was prepared as a protein model for both sets. TYK2: Non-receptor tyrosine-protein kinase 2, is part of the Janus kinase family. TYK2 phosphorylates cytokine receptor subunits in order to promote cytokine signaling. Cytokines regulate immune cell functions; therefore, TYK2 targets inflammation and immunity. Protein structure was taken from 3LXN.pdb, 2 ligand sets were used: Liang et al. (Euro. J. Med. Chem. 2013, 67, 175-187) and Liang et al. (J. Med. Chem. 2013, 56, 4521−4536). The total set contained 16 ligands with Ki values ranging from 2.5nM to 2000nM. For these experiments, since ligand concentration is much smaller than Km, Ki ≈ IC50. HSP90: Heat Shock Protein 90 alpha, HSP90 is a chaperone protein that assists other proteins to fold properly, stabilizes proteins against heat stress, and aids in protein degradation. It also stabilizes a number of proteins required for tumor growth, which is why HSP90 inhibitors are investigated as anti-cancer drugs. The HSP90 protein model was generated from the 2VCI.pdb and we used 2 ligand sets. The 1st set contained 18 pyrazole-type ligand from Barril et al (Bioorg. Med. Chem. Lett. 2006,16 2543–2548) and the 2nd set contained 36 isoxazole-type ligands from Baruchello et al. (J. Med. Chem. 2011, 54, 8592−8604). PERK: Eukaryotic translation initiation factor 2-alpha kinase 3, is a human protein that phosphorylates the alpha subunit of eukaryotic translation-initiation factor 2 (EIF2), leading to its inactivation, and thus to a rapid reduction of translational initiation and repression of global protein synthesis. Protein structure was taken from 4G31.pdb, and we show here 1 ligand sets from Axten et al. (J. Med. Chem. 2012, 55, 7193−7207) containing 15 ligands with IC50 values for PERK ranging from 0.2 nM to 17.4nM. References: 1. Wang, M., et al., Designing Molecules by Optimizing Potentials. J. Am. Chem. Soc., 2006. 128: p. 3228-3232. 2. Keinan, S., et al., Computational Design, Synthesis and Biological Evaluation of para-Quinone-Based
Inhibitors for Redox Regulation of the Dual-Specificity Phosphatase Cdc25B. Org. Biomole. Chem., 2008. 6: p. 3256-3263.
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