Efficient and Accurate Hydration Site Profiling for Enclosed Binding

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Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites Matthew R Masters, Amr H. Mahmoud, Ying Yang, and Markus A Lill J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00544 • Publication Date (Web): 05 Oct 2018 Downloaded from http://pubs.acs.org on October 23, 2018

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Journal of Chemical Information and Modeling

Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites Matthew R. Masters, Amr H. Mahmoud, Ying Yang, and Markus A. Lill∗ Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47906, United States E-mail: [email protected] Phone: (765) 496-9375. Fax: 765 494-1414

Abstract Molecular dynamics (MD) simulations allow for accurate prediction of the thermodynamic profile of binding-site water molecules critical for protein-ligand association. Whereas this hydration-site profiling converges rapidly for solvent-exposed sites independent of the initial water placement, an accurate and reliable placement is required for water molecules in occluded binding sites. Here, we present an accurate and efficient hydration-site prediction method for occluded binding sites combining water placement based on 3D-RISM and MD simulations using WATsite.

Introduction Water molecules play a crucial role in protein-ligand association by mediating interactions and by contributing significantly to the binding affinity of ligands due to their desolvation free energy. 1,2 A water molecule’s free energy contribution can be estimated using moleculardynamics (MD) simulation-based methods such as WATsite 3,4 or WaterMap. 5,6 Whereas 1

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the initial water placement for solvent-exposed binding sites is rather uncritical as frequent positional exchange of water molecules between binding site and bulk solvent guarantees rapid convergence of hydration site profiling on the nanosecond time scale, the same is not true for occluded binding sites where water can often be buried beneath the surface of a protein after folding. Similar problems occur for water molecules that are trapped by a ligand occupying the binding site. 7 In both scenarios, the placement of water molecules into the binding site cavity is essential for determining the accurate thermodynamic properties of hydration sites. Currently, there are several methods for water placement in preparation for molecular dynamics simulations. One popular technique is to use a pre-equilibrated box of water molecules. After placing the protein inside the water box, solvent molecules that sterically overlap with protein residues are removed. This can result in vacuum bubbles around and within the protein that must be resolved through equilibration of the system before a production run. Equilibration may smooth the density of water surrounding the protein, but does not fill the vacuum created inside occluded areas. 8 Another method for placing water molecules is through grand-canonical Monte Carlo (GCMC) simulations. 9 GCMC can predict the locations of water molecules based on iteratively inserting and deleting these molecules in a specific region. By performing both operations, GCMC can accurately sample water molecules both around the protein and within occluded cavities, but is also rather computationally expensive as a large number of iterations is required for correct placement. For more rapid water placement, statistical-mechanics methods have been developed that place explicit water molecules based on a solvent density distribution generated by algorithms such as 3D-RISM. 10,11 3D-RISM negates the need for longer simulation-based methods and produces reliable solvent distributions, including ions, within minutes. It is able to do this through it’s integral-based approach which can analyze a system very quickly. One of these statistical-mechanical methods, Placevent, uses a population function where water molecules are placed one at a time into the location with the highest probability of occupation until

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an adequate density is reached. 11,12 More recently, GAsol, a genetic algorithm approach for placing water molecules into density distributions showed improved accuracy compared to Placevent. 13 Here we present a novel method combining initial water placement of GAsol with molecular dynamics and hydration site analysis to accurately predict the location and thermodynamic properties of water molecules within enclosed binding sites.

Method Six protein systems with occluded binding pockets containing crystallographic water molecules were chosen. Three of the structures are ligand-free apo states of a protein while the other three are complexed with a ligand. The crystal water molecules within the binding site of the protein were identified as important areas of hydration and were used to test the accuracy of the new computational approach. The crystal structures for HIV-1 protease, BRD4 bromodomain 1, major urinary protein, haloalkane dehalogenase, cytochrome c peroxidase, and beta-lactamase were taken from PDB IDs 2ZYE, 4O7A, 2OZQ, 1CQW, 4NVA, and 3S1Y respectively. The binding site of the three apo protein systems were defined using aligned protein structures of the same system complexed with ligands (PDB IDs 1ZND, 3FBW, and 4NVE). Each protein system was prepared using Schrodinger’s Protein Preparation Wizard 14 with the default protocol, and protonation states of histidine, glutamic and aspartic acid residues were predicted using Propka 15 at a pH of 7.0. 3D-RISM was used for each protein system to generate the solvent distribution. 10 The 3D-RISM prediction used a solution of SPC water, Na+, and Cl- ions with concentrations of 55.5M, 0.005M, and 0.005M respectively. Using the solvent distribution generated from 3DRISM, GAsol was used to identify the water network that optimally fits this distribution. 13 A spatial filter was applied to GAsol so only water molecules within a 10 ˚ A radius of the binding site center were calculated.

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Each protein including its water network predicted by GAsol was then further solvated in a pre-equilibrated water box using tleap, extending a minimum of 10 ˚ A from the protein surface. 16 Chloride and sodium ions were added to neutralize the system. For comparison, another set of simulations was prepared using tleap without the water network produced by GAsol. MD simulations were performed using the modified GPU-accelerated OpenMM-WATsite package 17 with the AMBER14SB force field 18 and SPC/E water model. 19,20 The SHAKE algorithm 21 was applied to constrain bonds including hydrogen atoms to their equilibrium lengths and to maintain rigid water geometries. Long-range electrostatic interactions were treated with the Particle Mesh Ewald method 22 with a cutoff of 10 ˚ A for the direct interactions. The Lennard-Jones interactions were truncated at a distance of 10 ˚ A, and a long-range isotropic correction was applied to the pressure representing Lennard-Jones interactions beyond the cutoff. A Langevin integrator with a time step of 2 fs was used with a stochastic thermostat collision frequency of 1 ps−1 . Pressure control was implemented via isotropic volume changes of the simulation box attempted by Monte Carlo moves every 25 time steps. During the entire simulation process, all protein and ligand heavy atoms were harmonically ˚−2 . restrained with a spring constant of 4.8 kcal mol−1 A Each system was first energy minimized and then heated to 298 K over 50 ps of MD simulations, followed by 1 ns of equilibration MD simulations at a temperature of 298 K and a pressure of 1 bar with periodic boundary conditions in all three dimensions. Production run was performed for 20 ns, which allowed enough time for the hydration site prediction to converge. Each MD trajectory was analyzed using WATsite3.0 17 to calculate hydration site residues positions and their thermodynamic properties, i.e. enthalpy, entropy and free energy of desolvation. For the major urinary protein system, a collection of 12 co-crystallized ligands was used to examine the influence of displaced hydration sites on binding affinities. 23 Binding affinities were calculated by summing the free energies of the hydration sites displaced by each ligand.

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A hydration site is considered displaced if an atom in the ligand is within 1.4 ˚ A of the hydration site. The calculated binding free energy was then compared to the experimental binding affinity for each ligand.

Implementation The combination of WATsite3.0 with GAsol has been implemented as a command-line tool or as part of a PyMOL plugin. WATsite3.0 itself utilizes the OpenMM 24 toolkit for GPUaccelerated molecular dynamics simulations. OpenMM was modified on the Python, C++ and CUDA layer to calculate water interaction energy on-the-fly throughout the MD simulation.

Installation For efficient installation a Docker image was generated including all necessary software, such as Miniconda, OpenMM-WATsite, WATsite3.0, PyMOL-1.7 together with the userfriendly WATsite PyMOL-plugin. This Docker image is distributed via Docker Hub (https: //hub.docker.com) under lilllab/watsite3. Detailed step-by-step instructions for installation of the Docker image are provided with the User Guide provided in the Supporting Information. The User Guide is also available at http://people.pharmacy.purdue.edu/~mlill/software/watsite/. Currently, only Linux OS (Redhat / Ubuntu) is supported.

Application Example Along with the software, the Docker image also includes a tutorial for running the software on the example of HIV-1 Protease. All input files are located in the Docker image in a directory that can be accessed in a terminal by the command: cd / programs /WATsite/ example /2 zye 5



The first step is to perform 3D-RISM and GAsol for initial water placement, and use tleap to generate the amber topology and coordinate files with the following commands: pymol −c / programs /WATsite/ dat / remove wat . pml sed −i ‘ /OXT/a TER’ p r o t d r y . pdb / programs / miniconda / bi n / t l e a p −s −f t l e a p r i s m 3 d . i n bash / programs /WATsite/ b i n / r u n w a t e r p l a c e m e n t . sh

/ programs / miniconda / bi n / t l e a p −s −f tleap MD . i n / programs / miniconda / bi n /ambpdb −p prot amber . prmtop \ −c prot amber . i n p c r d > prot amber . pdb Next, MD simulation with OpenMM-WATsite needs be performed using the following command: nohup . / run omm watsite . sh & Finally, hydration site prediction can be performed with the following commands: pymol −c / programs /WATsite/ dat / s u p e r . pml / programs / miniconda / bi n / c p p t r a j < / programs /WATsite/ dat / c p p t r a j . i n / programs /WATsite/ bi n / w a t s i t e −c i n p u t . b c f −o WATsite OUT To analyze and visualize the results, it is recommended to use the WATsite PyMOL plugin (PyMOL version < 2.0). WATsite.py is already installed inside the Docker container but can also be downloaded separately at http://people.pharmacy.purdue.edu/~mlill/ software/watsite/. User can install the plugin outside the Docker container in case of issues with the graphics engine. As shown in Figure 1, the location and density of predicted hydration sites will be displayed in the PyMOL viewer. Hydration sites will also be based on their thermodynamic properties, which is also tabulated in a separate window.

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Figure 1: Example of hydration site results visualized with WATsite PyMOL plugin.

Results and Discussion Reproduction of X-ray water positions We analyzed hydration site prediction using WATsite simulations for each protein with and without the initial placement of water molecules using GAsol. The locations of hydration sites were compared to the locations of the crystal water molecules. In total, 46 water molecules were identified in the crystal structures of the six protein systems. Our new approach combining GAsol with WATsite successfully identified 36 and 45 out of 46 crystal water molecules within 1 ˚ A and 2 ˚ A, respectively (Fig. 2). This represent a significant improvement over using GAsol alone, which was only able to reproduce the correct water position within 1 ˚ A and 2 ˚ A in 16 and 32 cases, respectively. WATsite alone identified only 16 and 26 crystal water molecules within 1 ˚ A and 2 ˚ A, respectively. The relatively poor performance of using WATsite alone agrees with the hypothesis that the timescale of the underlying MD simulations is too short to allow access of water molecules to enclosed binding sites. This fact is further highlighted in Fig. 3 for the enclosed cavities of major urinary protein (MUP) (first row) and BRD4 bromodomain 1 (second row) where the initial water placement using tleap with subsequent MD simulations identifies no or only a single water in the binding site for the two systems, respectively. Whereas GAsol is able to place mutliple

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water molecules into the enclosed binding sites, its placement algorithm is not accurate enough to identify crystal water positions precisely within 1 ˚ A or 2 ˚ A (Fig. 2 and 3).

Figure 2: Number of crystal water locations predicted within cutoff distances of 1 ˚ A, 2 ˚ A, and 4 ˚ A. Data collected across six protein systems with a total of 46 crystal water locations. Fig. 3 and 4 also demonstrates that the improvement in hydration site prediction from GAsol to GAsol+WATsite is consistent over all six tested systems. Haloalkane dehydrogenase remains the only system where the combined GAsol+WATsite approach misses one crystal water molecule (within 2 ˚ A radius). The reason for this lack of one water molecule is a subpocket connected to the binding site by a small hydrophobic tunnel which is difficult for water to pass through. GAsol only placed one water molecule near the two crystal water molecules in this pocket. An additional water was placed near the hydrophobic tunnel but did not enter the subpocket during the MD simulation. Fig. 4 also reveals that the crystal water locations in polar binding sites (BRD4 bromod8


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Figure 3: Position of hydration sites using GAsol placement alone (red) and WATsite with (blue) and without (yellow) initial GAsol placement compared to crystal water locations (green) in the occluded binding site of each protein system.

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Figure 4: Distance between predicted and crystal water locations for each crystal water in all six protein systems using WATsite, GAsol and WATsite+GAsol.

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omain 1 and HIV-1 protease) are well reproduced with distances smaller than 1 ˚ A whereas larger deviations are observed for a few water molecules in hydrophobic binding sites (MUP and haloalkane dehalogenase). These results are not surprising as water molecules in hydrophobic environments are often more dynamic compared to polar binding sites where the water molecules are anchored by hydrogen bonds to protein residues.

Thermodynamic characterization of hydration sites In addition to the prediction of hydration site position, we used WATsite to compute the free energy of desolvation of each water molecule. Testing the performance of thermodynamic profiling in the context of the combined GAsol+WATsite approach, 12 co-crystallized ligands to MUP were overlayed with the predicted hydration sites and the free energy of desolvation was calculated based on the displaced water molecules (Table ??). Assuming that the protein’s desolvation free energy is a driving force for ligand binding, these energies computed for all ligands in the dataset were correlated against the experimental values, obtaining an R2 value of 0.63 (Fig. ??). This significant correlation is superior to MM-GB/SA results, we obtained for the same system, where almost no correlation was found (R2 < 0.01).

Convergence of desolvation free energy calculations Finally, we studied the convergence of enthalpy, entropy and free energy of desolvation prediction for the MUP and BRD4 bromodomain systems (Fig. ?? and ??). After 1 ns, the predicted free energy of the hydration sites converged to R2 values of 0.90 and 0.92 for the two systems, respectively, compared to the full 20 ns simulation. After 5 ns, the free energy predictions converged to R2 values of 0.96 and 0.99, respectively. Therefore, 5 ns MD simulations seem to be sufficient to refine the GAsol water network predictions and accurately profile the thermodynamic data of the hydration sites. A 5 ns MD simulation with GPU-acceleration will take approximately 1 hour to complete.

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Conclusion In summary, we have developed a novel protocol combining GAsol prediction with WATsite refinement and thermodynamic profiling to accurately reproduce crystallographic water positions and their free energy of desolvation in enclosed binding sites. Our results demonstrate that almost all occluded crystal water molecules can be reproduced with high positional precision (< 1 ˚ A deviation). The method is efficient and allows full profiling in about one hour per protein system.

Acknowledgement We acknowledge the Purdue Research Foundation for partially supporting this research.

Supporting Information Available Binding free energies from hydration site displacement of 12 MUP ligands; Correlation between WATsite desolvation free energies and experimental binding affinities for 12 MUP ligands; Convergence of hydration site thermodynamic profiles for MUP water molecules at 1, 5, and 10 ns time points; Convergence of hydration site thermodynamic profiles for BRD4 bromodomain 1 water molecules at 1, 5, and 10 ns time points (PDF); WATsite3.0 User Manual (PDF).

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Efficient and Accurate Hydration Site Profiling for Enclosed Binding

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