Molecular dynamics to enhance structure-based virtual screening on cathepsin B
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- Ogrizek, M., Turk, S., Lešnik, S. et al. J Comput Aided Mol Des (2015) 29: 707. doi:10.1007/s10822-015-9847-2
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Molecular dynamics (MD) and molecular docking are commonly used to study molecular interactions in drug discovery. Most docking approaches consider proteins as rigid, which can decrease the accuracy of predicted docked poses. Therefore MD simulations can be used prior to docking to add flexibility to proteins. We evaluated the contribution of using MD together with docking in a docking study on human cathepsin B, a well-studied protein involved in numerous pathological processes. Using CHARMM biomolecular simulation program and AutoDock Vina molecular docking program, we found, that short MD simulations significantly improved molecular docking. Our results, expressed with the area under the receiver operating characteristic curves, show an increase in discriminatory power i.e. the ability to discriminate active from inactive compounds of molecular docking, when docking is performed to selected snapshots from MD simulations.
KeywordsCathepsin B Molecular dynamics Molecular docking Protein flexibility
Most of the drug design efforts are nowadays assisted by computational methods in the form of virtual screening. With 3D structures of targets often easily available the most popular method remains molecular docking . Normally computer aided drug design software offers different settings and users can use different protocols, all of which influence the performance of docking . Despite the obvious pitfalls of molecular docking, proper validation still often remains neglected and a lot of authors test the docking programs only by re-docking co-crystallized ligands measuring RMSD between docked and co-crystallized conformations . To facilitate validation of molecular docking methods and protocols, DUD and DUD-E benchmarking sets were created [11, 12], and are used to test the performance of virtual screening methods by providing the sets of negative controls. Another important weakness of most molecular docking methods is that they consider the target protein as rigid which is especially problematic if the target is subject to conformational changes or loop movements upon ligand binding. To circumvent this problem, molecular dynamics (MD) can be used to simulate target dynamics, and then docking can be performed to individual snapshots obtained from MD. Performance of the docking is then evaluated on each snapshot .
Databases of compounds
Nitroxoline derivatives with known cathepsin B activity were reported previously by our group [6, 7] and were classified as actives (31 compounds; Table S1, Supporting Information) or inactives (26 compounds; Table S2, Supporting Information) according to their biological activity on cathepsin B. Additionally, 1900 decoys were generated using the DUD-E web service  based on our active nitroxoline derivatives. We used a script utilizing OpenBabel toolkit version 2.3.2 [15, 16] on each compound to assign hydrogens appropriate for pH 7.4 and to generate 3D structures and minimize them in 1000 steps utilizing the MMFF94 force field. Compound properties such as molecular weight, number of H-bond acceptors, number of H-bond donors, number of atoms, and logP were calculated using RDKit nodes for KNIME .
We used CHARMM 3.6 biomolecular simulation program  to perform molecular dynamics simulation of the closed form of cathepsin B bound to the ligand nitroxoline (PDB ID 3AI8) . Hydrogen atoms were added using HBUILD routine in CHARMM. Steepest descent and adopted basis Newton-Raphson energy minimizations were performed to remove atomic clashes and to optimize the atomic coordinates of the protein–ligand complex. The nitroxoline was held fixed, and the protein was allowed to move freely during the minimization process. We embedded the protein in a cube of water, with TIP3P  water molecules, previously minimized using the SD algorithm and adopted Newton-Rhapson algorithm (ABNR). KCl was added at a concentration of 0.35 M to neutralize the system. Trajectories were generated at 310.15 K (37 °C) and covered 4 ns of constant pressure and temperature molecular dynamics employing periodic boundary conditions. No constrains were used during the simulation to allow cathepsin B and the nitroxoline ligand to position themselves freely according to physical forces between them. From the resulting simulation we quenched 40 snapshots, one for every 100 ps of the simulation . Initial atomic positions of the nitroxoline molecule were obtained with MOLDEN . Force field parameters for nitroxoline were estimated using CgenFF/ParamChem tool version 0.9.6 and the force field version 2b7 [22, 23, 24]. Parameters for the nitroxoline molecular force field were refined using the GAUSSIAN package version 09. For visualization of the target and ligands we used VMD .
For all molecular docking experiments AutoDock Vina version 1.1.2. was used . Active site of cathepsin B from the crystal structure 3AI8 was defined as a box of size 15 × 15 × 15 Å with nitroxoline in its center. All 40 snapshots of catB obtained from molecular dynamics were then superimposed on the original cathepsin B crystal structure and consequently we were able to use the same active site defining box for all the docking experiments. All snapshot PDB structures of catB were converted to PDBQT format using OpenBabel version 2.3.2. Finally, all 1957 compounds (actives, inactives and decoys) were docked in 41 structures of catB using the standard parameters of AutoDock Vina.
Molecular dynamics was carried out on columns and rows of computers (CROW) cluster at the National Institute of Chemistry, Ljubljana, Slovenia consisting of computers with Intel Core i7 3.40 GHz processors, having 8 GB of RAM, running Gentoo Linux . Molecular docking was performed on a HP workstation with two quad core Intel Xeon 2.2 GHz processors, 8 GB of RAM, 320 GB and 1 TB hard drives, and a Nvidia Quadro FX 4800 graphic card, running a 64-bit version of Arch Linux. All calculations were performed on CPU cores.
Results and discussion
We assessed the ability of molecular dynamics to enhance the discriminatory power of structure-based virtual screening protocol, using the crystal structure of cathepsin B (catB) with the co-crystallized nitroxoline (PDB code 3AI8) as a starting point. Molecular dynamics simulation of this crystal structure was performed using CHARMM  as described in the Methods section and resulted in 40 snapshots spaced 100 ps apart.
Average properties of compound sets
All molecular docking experiments were done using the same protocol so not to introduce any bias. All MD snapshots of catB were aligned to the catB crystal structure (PDB code 3AI8). The active site was in all cases defined as a box of size 15 × 15 × 15 Å with crystallized nitroxoline in its center. Finally, all 1957 compounds (actives, inactives and decoys) were docked to the catB crystal structure and to all 40 snapshots obtained from MD.
The AUC and standard deviation (SD) values obtained from docking to the crystal structure (CS) and first ten snapshots. Best AUC values are in italics
In this study, we set out to evaluate the influence of protein flexibility on molecular docking. We simulated a virtual screening scenario using known active and inactive compounds in addition to generated decoys, and found that with correct choice of MD snapshots, we significantly improve docking performance. With short molecular dynamics simulations we were able to obtain a target conformation where an active versus inactive discrimination was significantly better than random choosing. Running short molecular dynamics simulations on a protein target prior to docking with subsequent statistical evaluation of different conformations at different molecular dynamics running times is a useful strategy in improving the performance of virtual screenings and can be a generally applicable approach in early drug discovery processes.