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Sep 2019

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BRIDGE: An Open Platform for Reproducible Protein-Ligand Simulations and Free Energy of Binding Calculations
BRIDGE:一个可重复的蛋白质配体模拟和结合自由能计算的开放平台   

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Abstract

Protein-ligand binding prediction is central to the drug-discovery process. This often follows an analysis of genomics data for protein targets and then protein structure discovery. However, the complexity of performing reproducible protein conformational analysis and ligand binding calculations, using vetted methods and protocols can be a challenge. Here we show how Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE), an open-source web-based compute and analytics platform for computational chemistry developed based on the Galaxy bioinformatics platform, makes protocol sharing seamless following genomics and proteomics. BRIDGE makes available tools and workflows to carry out protein molecular dynamics simulations and accurate free energy computations of protein-ligand binding. We illustrate the dynamics and simulation protocols for predicting protein-ligand binding affinities in silico on the T4 lysozyme system. This protocol is suitable for both novice and experienced practitioners. We show that with BRIDGE, protocols can be shared with collaborators or made publicly available, thus making simulation results and computations independently verifiable and reproducible.

Keywords: Protein Dynamics (蛋白质动力学), Binding (结合), Free energy (自由能), Drug Discovery (药物开发), Simulations (模拟), BRIDGE (BRIDGE)

Background

The Googleplex of protein ligand binding possibilities is best reduced to a focussed set of drug hits, that can be interrogated in vitro and in vivo to produce a candidate drug for clinical studies, using computational chemistry methods. As a consequence, correct use of accurate compute methods that provide estimates of the energy of binding between a potential drug molecule, and a protein is central to the drug discovery process (Cournia et al., 2017). The idea is that the ligand should preferentially bind to the protein and inhibit its function. In this protocol, you will learn how to calculate relative protein-ligand binding free energies using open-source free energy tools and the Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE) platform (Senapathi et al., 2019) that is built on the Galaxy platform (Afgan et al., 2018). The free energy differences between two ligands are calculated through an in silico perturbation from one ligand to another as they are i) bound to a protein (e.g., T4 lysozyme) and ii) solvated in water. Setting up free energy molecular simulations to run on High-Performance Computing (HPC) hardware resources, often done using scripts and the Linux command line, is challenging and a barrier to obtaining reliable results. In addition to the computational challenges, not all steps are repeatable as when run using varied software and hardware resources available through different laboratories, universities, public facilities, and cloud platforms. However, the objective of BRIDGE development is to provide a web-based platform that includes reliable methods for simulation, analysis, and analytics. A similar approach to BRIDGE is employed by the BioExcel Building Blocks software (Andrio et al., 2019). The BRIDGE-Galaxy platform makes the transition from genetics to proteomics to chemistry a seamless one and enables repeatable computer simulations and analysis using curated workflows.

We study T4 lysozyme as an illustration of a protocol to compute the relative binding of ligands, and we use benzene vs. p-xylene bound to T4 lysozyme as an example. The following tools are used: ProtoCaller for setup (Suruzhon et al., 2020), GROMACS for molecular dynamics (MD) simulations (Abraham et al., 2015), and alchemical analysis for simulation analysis (Klimovich et al., 2015). At least two simulations are required to compute the relative free energy of binding. First, the transformation between ligands in water and then the transformation between ligands bound to the solvated protein. Ligand 2 can be transformed into Ligand 1 or vice versa; the choice of direction is important to understand the meaning of the free energy calculated. Calculating in both directions is not required but can be used as an additional metric to confirm the computational convergence of the free energy result.

The identification of a protein target may be made through genomics experiments and bioinformatics analysis of gene expressions or arrived at through a literature search that may point to one or many protein targets. The protein structures are sourced from the RCSB Protein Data Bank. The identification of ligand hits is often guided by molecules that share a similarity to the known substrate or inhibitors found in the PDB, or derived from existing pre-screened molecule libraries, using similarity measure and docking calculations. Public molecular databases are good resources for finding possible ligands, for example, ChEMBL (Gaulton et al., 2012), ZINC (Irwin and Shoichet, 2005) and PubChem (Kim et al., 2016).

Preparing the protein from a PDB file into a state ready for simulation, posing the ligand in the binding site, and selecting appropriate parameters for accurate simulation are some of the immediate barriers to an MD simulation, even more so for free energy simulation. There are numerous alternative methods to prepare the protein, e.g., PDBFixer (Eastman et al., 2013). However, the preparation often requires specialist training to treat challenges such as missing loops from PDBs properly, repairing or installing mutated residues, disulfide bonds creation and assigning experimentally consistent protonation states. In the case of ligands, accurate charges require parameterization and assignment. In the protocol presented here, the automated setup of protein and ligand is done using the Alchemical Setup tool that links several specialized tools to perform protein setup and parameterization.

The BRIDGE interface (Figure 1) is straightforward and follows the Galaxy design. Tools are in the left panel, and a history of progress is in the right panel and information about the current tool or dataset selected is in the central panel. Data can be uploaded using the green upload icon at the top of the Tools menu (left panel), while the top menu bar includes your user account and other information. To search for tools, type a keyword into the search bar and tools with matching words will be displayed. Changes to the tools are versioned and the available versions can be selected via the central panel interface. Example histories of this process that include the outputs can be found by browsing to shared data and clicking on published histories (https://galaxy-compchem.ilifu.ac.za/histories/list_published). Choose the example history and click on the ‘+’ icon to add to your histories. Browse the example outputs using the eye icon. The free energy workflow illustrated in Figure 2 is also available (Reference 15).


Figure 1. An overview of the Galaxy interface. Tools are in the left panel. Tool information or data are shown in the central panel and a history is displayed in the right panel.


Figure 2. An overview of the Relative Binding Free Energy Workflow. The receptor in PDB format and ligand in SMILES are inputs to the Alchemical Setup. The multiple simulations are run using the Alchemical Run and the Alchemical Analysis tool is used to check convergence.

As part of the procedures below, a brief analysis is provided that is used to confirm that the free energy simulations are converged. An analysis of the molecular trajectories of the lead inhibitor can be conducted to identify the molecular interactions responsible for ligand binding and consider the conformation of ligand and protein. The aim of this further analysis would be to understand the rationale for binding. Further analysis might, for example, include hydrogen bond analysis and Ramachandran analysis, Root Mean Square Deviation (RMSD) and Principal Component Analysis (PCA). An example workflow is provided (Reference 14). PCA will reveal the flexibility of the catalytic domain and principal protein motions affecting inhibitor binding, while hydrogen bonding analysis would indicate key hydrogen bonding interactions between the ligand and the protein binding site.

Software

  1. Local computer
    To run this on your local compute resources, obtain the BRIDGE Docker (https://github.com/scientificomputing/bridge-docker) and install the Docker image on your local machine. All libraries, compilers and links are packaged within the Docker image, so installation is seamless. A quad-core processor with 8GB RAM and 80GB hard drive space is sufficient for testing with Docker on any operating system.
  2. Public servers
    Access is obtained through a web browser and software installation is not required. The BRIDGE platform can be accessed at https://galaxy-compchem.ilifu.ac.za/. The tools are available on Galaxy Europe (http://cheminformatics.usegalaxy.eu).

Procedure

  1. System Preparation with Alchemical Setup
    1. Login to the BRIDGE server.
    2. To start preparing the simulation of lysozyme (protein, or receptor) and the benzene ligand, click on the Alchemical Setup tool to set up the simulation (Figure 3). Nine outputs will be generated–one report, four structures and four topologies. A structure and topology are created for the Ligand 1 to alchemically transform to Ligand 2 and vice versa in water and the protein.


      Figure 3. The Alchemical Setup tool. A protein and two ligands are specified and GROMACS compatible outputs will be generated.

    3. Type in the PDB ID for T4 lysozyme: “181L” (https://www.rcsb.org/structure/181L).
    4. Choose “Upload files” and change to “No”.
    5. Choose the “Ligand Reference” as “400”. This is a benzene molecule in the active site (residue ID is 400). We are going to map the two ligands (benzene and p-xylene) onto this benzene. Therefore, Ligand Reference should be given as “400”.
    6. ProtoCaller can build ligands using the SMILES notation of a molecule. Insert a SMILES string for Ligand 1–use benzene “C1=CC=CC=C1”. This string can be generated using molecular software or an online database.
    7. Insert a SMILES string for Ligand 2 -use p-xylene "CC1=CC=C(C=C1)C.
    8. Choose the “PDB chain ID” as chain “A”.
    9. Choose the “Protein force field” as “ff14SB”. This is the AMBER Force Field that has been developed for proteins, and alternative force fields can be selected.
    10. Change any mutated selenomethionines back to methionine Set this option to “Yes".
    11. Set “delete any atoms with alternate locations (altLoc B)” to “Yes”. Only the primary coordinates will be used.
    12. Select “gaff2”, the General Amber Force Field 2, for “Ligand force field”.
    13. The protein-ligand complex will need to be solvated in a box of water molecules. Choose the box dimension for the protein-ligand system to be “7” nm. The longest side of lysozyme is approximately 5 nm and we use nonbonded cutoffs of 1.2 nm. We need to leave a buffer region for the protein to move freely in the water box and ensure there is sufficient space so that it does not interact with its periodic image. Adjust this value depending on the system under consideration.
    14. The ligand will also be solvated in a water box. Choose the size of the simulation box. “4” nm will be sufficient for most ligands, but this can be increased if the ligand is longer than 2.5 nm. Simulations of larger water boxes will take more time to complete.
    15. The last option is to define the concentration of ions to place inside the water box. By default, “0.154” M of sodium (Na+) and chloride (Cl-) ions are included, which is the physiological concentration.
    16. Click the “Execute” button to run this tool.
    17. The simulation will have started and will appear in the history panel. The files created are the GROMACS topology (.top) and structure (.gro) files required for the Alchemical Run tool. This will generate files to perturb Ligand 2 into Ligand 1 inside the protein and in a water box in both forward and backward directions.
    18. Check the progress of the simulations by clicking the eye icon in the report. This will take some time. The time taken will depend on available compute resources and may also be due to tool dependency checking and installation. This tool is parameterizing the ligand force field, which should take less than 20 minutes. If the history items remain grey for more than 1 day, contact a Galaxy Admin. e.g., via Gitter (https://gitter.im/galaxycomputationalchemistry/Lobby).
    19. The simulation is finished when the history items turn green.

  2. Relative free energy simulations in protein (Ligand 2 to Ligand 1) with Alchemical Run
    Once complete with setup, the alchemical free energy simulations for each ligand are started and these are needed to calculate the ligand perturbation in water and the active site. The alchemical simulation is carried out in four steps for each free energy window, energy minimization, MD simulations in the Canonical ensemble (NVT) ensemble followed by Isothermal-Isobaric ensemble (NpT) ensemble. This pre-equilibration is vital to remove any Hamiltonian lags. Finally, the production simulation is carried out in an NpT ensemble. These steps are all carried out by the Alchemical Run tool in Galaxy (see Figure 4). It is from these simulations that the relative binding free energies can be calculated. Here, a simple perturbation of p-xylene (Ligand 2) to benzene (Ligand 1) will be carried out, and we choose a short simulation length for the purposes of illustrating the protocol. In reality, lengthier simulations are required. Often production simulations are at least 1 ns for free energy runs. For our converged lysozyme simulations we used the following parameters:
    Minimization steps:10000, NVT steps: 500000 (500 ps), NpT steps: 500000 (50 ps), MD (production) steps: 1000000 (1000 ps), time step: 0.001 ps. Whichever parameters are chosen the analysis tools must be used to check convergence.
    1. Select the Alchemical Run tool. This tool requires the topology and structure outputs from the alchemical setup tool.
    2. Select the “Structure file” to be “Ligand 2 to Ligand 1 in Protein Structure”.
    3. Select the “Topology file” to be “Ligand 2 to Ligand 1 in Protein Topology”.
    4. Set the number of “Minimization steps” to “10000”.
    5. Set the number of “NVT equilibration steps” to “500” (1 ps).
    6. Set the number of “NpT equilibration steps” to “500” (1 ps).
    7. Set the number of “MD (production) steps” to “1000” (2 ps).
    8. The “seed” is set to 19880924 by default and it must be changed. This is the seed to start the random number generator for molecular dynamics, change this to the current date or any random number, for example, “20200130”.
    9. Set the “time step” to “0.002” ps.
    10. Create hydrogen bond constraints. Choose “Yes” to apply constraints to the ligands and choose “H-bond constraints”. Choose “LINCS order” “4”, “LINCS iterations” “1”, “LINCS maximum angle” “30”.
    11. Select the free energy perturbation (FEP) path and choose to go from a larger ligand (p-xylene) to a smaller ligand (benzene).
    12. Type in “39” for the “number of free energy windows”. The more windows, the more likely the simulations will overlap and converge. The number of windows is zero-indexed, typing in 39 means there are 40 windows. First the charges are perturbed, then the Van der Waals and Bonding terms are perturbed. Here we are alchemically perturbing from Ligand 2 (0.00) to Ligand 1 (1.00) on a scale from 0 to 1. Any number between 0 and 1 represents how the charges, Van der Waals, Bonding interactions are scaled to form a hybrid system.
    13. Scale the charges of molecules in the simulations for the first 10 windows from 0.00 to 1.00 in increments of 0.11. The rest of the free energy windows are not perturbed. The exact lambda values must be described, by typing in the following 40 numbers: “0.00 0.11 0.22 0.33 0.44 0.56 0.67 0.78 0.89 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00”.
    14. Scale the Van der Waals interaction in the simulations for the final 29 windows after the charge has been perturbed. These are scaled from 0.00 to 1.00 in increments of 0.04 and then the change tapers to 0.02 and 0.01 at the end point to prevent end point catastrophes. The exact lambda values must be described, type in the following 40 numbers: “0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.08 0.11 0.15 0.19 0.23 0.27 0.30 0.34 0.38 0.42 0.46 0.49 0.53 0.57 0.61 0.65 0.68 0.72 0.76 0.80 0.84 0.87 0.91 0.95 0.97 0.98 0.99 0.99 1.00”.
    15. Scale the Bonding terms in the simulations in tandem with the Van der Waals interaction scaling. The exact lambda values must be described, type in the following 40 numbers: “0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.08 0.11 0.15 0.19 0.23 0.27 0.30 0.34 0.38 0.42 0.46 0.49 0.53 0.57 0.61 0.65 0.68 0.72 0.76 0.80 0.84 0.87 0.91 0.95 0.97 0.98 0.99 0.99 1.00”.
    16. Use the default free energy options.
    17. Set the temperature to “300” K and the pressure to “1” bar.
    18. Choose “Perform the simulation”. The input files can be generated and simulated outside of Galaxy if required.
    19. Click “Execute”.
    20. Check the report in your history for the simulation progress. Output will also include ‘.tar’ files (archives) of the FEP/TI (Thermodynamic Integration) data and trajectories. View these trajectories to ensure no unphysical events have occurred during the FEP calculations.
    21. Optional: Repeat this for Ligand 1 to Ligand 2 in protein.


      Figure 4. The Alchemical Run tool. This tool is used to run the minimization, equilibration and production simulations in GROMACS for the alchemical transformation between the ligands in a particular environment.

  3. Relative free energy simulations in water (Ligand 2 to Ligand 1) with Alchemical Run 
    1. As for B, but choose the “Structure file” as “Ligand 2 to Ligand 1 in Water Structure” and the “Topology File” as “Ligand 2 to Ligand 1 in Water Topology”.
    2. Optional: Repeat this for Ligand 1 to Ligand 2 in water.

  4. Analysis and convergence testing of Ligand 2 to Ligand 1 in the protein with Alchemical Analysis
    The Alchemical Analysis tool (Figure 5) provides a comprehensive analysis (although the GROMACS inbuilt bar module can be used). The data.tar file from the Alchemical Run tool is a required input. The expected tool outputs are as follows:
    • Report and free energy outputs.
    • Overlap matrix of free energy windows (as PNG).
    • Convergence plot (as PNG).
    • Curve Fitting Method (CFM) based consistency inspector (as PNG).
    • Free energy change breakdown (as PNG).
    • Thermodynamic Integration plot (as PNG).
    For more details about Alchemical Analysis, read the article (Klimovich et al., 2015) and the code repository (https://github.com/MobleyLab/alchemical-analysis).
    1. Open the Alchemical Analysis tool.
    2. Select the Protein data output; this will be in the “tar” format.
    3. Set the prefix to “md”, the alchemical run uses the prefix md to save the simulation data by default.
    4. Set the temperature to “300” K.
    5. The “Equilibration time” is set as “0” ps. This can be changed to discard some simulation data. It would be useful in a scenario where the simulations are found not to have converged and the first few ps need to be removed.
    6. Report energies in “kcal”.
    7. If required, include lambda states to skip. By default, none are skipped.
    8. Click “Execute”.
    9. Optional: Repeat for Ligand 1 to Ligand 2 in protein.


    Figure 5. The Alchemical Analysis tool. This tool is used to analyze free energy simulations. The user provided simulation data from GROMACS in tar format along with further simulations details such as the temperature. The outputs provided assist the user in evaluating if the calculations have converged.

  5. Analysis and convergence testing of Ligand 2 to Ligand 1 in water with Alchemical Analysis
    1. As for D but choose the Ligand 2 to Ligand 1 in Water data output.
    2. Optional: Repeat for Ligand 1 to Ligand 2 in Water.

  6. Interpret the analysis for Ligand 2 to Ligand 1 in the protein
    1. Check the results of the calculated free energy values with different estimators and methods (Thermodynamic integration [TI], Deletion exponential averaging [DEXP], Insertion exponential averaging [IEXP], Bennett acceptance ratio [BAR], Multistate Bennett acceptance ratio [MBAR]); see Figures S1 and S2.
    2. Check the free energy change dF(t) output which is a text file containing the calculated free energies as a function of time in both forward and backward directions. The backward direction simulation was not run but this tool can extract that data from the forward direction simulation.
    3. Check the Convergence Plot dG(t) to see if the simulation has converged. The forward and reverse ΔG should be the same within tolerance, preferably within the purple shaded band (Figure 6).


      Figure 6. Free Energy Convergence plot. This plot is used to confirm the forward and reverse ΔG values fall within tolerance.

    4. Check the Overlap Matrix which shows the overlapping of each free energy window in the simulation. There should be a good overlap between neighbouring free energy windows. If so, this indicates the stratification (number of free energy windows) is sufficient for the simulation.
    5. Consider the Free energy change breakdown. This gives the visualization of the calculated free energy for each window using each free energy estimator used. This plot shows that the IEXP exhibits a huge deviation from the other methods.
    6. Consider the Thermodynamic Integration Plot. This visualization of the data is useful for calculating free energy using TI.
    7. Consider the Curve Fitting Method based consistency inspector. This will indicate if all simulations have converged. If not, the simulations can be rerun using a longer simulation time (repeat protocol B with a longer simulation time) or the unconverged fraction of data of the simulation can be removed using alchemical analysis (rerun protocol D and set the equilibration time to be an appropriate number of ps.
    8. We have completed the analysis of the perturbation from Ligand 2 to Ligand 1 in the protein.
    9. Optional: Repeat for Ligand 1 to Ligand 2 in protein.

  7. Interpret the analysis for Ligand 2 to Ligand 1 in water
    1. As for F but choose the Ligand 2 to Ligand 1 in Water analysis results.
    2. Optional: Repeat for Ligand 1 to Ligand 2 in water.

  8. Calculate the relative free energy of Ligand 2 to Ligand 1
    1. Find the free energy estimated with MBAR for Ligand 2 to Ligand 1 in protein (10.212 ± 0.278 kcal/mol).
    2. Find the free energy estimated with MBAR for Ligand 2 to Ligand 1 in water (10.600 ± 0.258 kcal/mol).
    3. Calculate the relative free energy of binding (the difference), -0.388 ± 0.379 kcal/mol. This value is negative, which means that Ligand 1 (benzene) binds more favorably than Ligand (2) p-xylene. This compares well to the experimental difference, which is -0.52 ± 0.17 kcal/mol (Morton et al., 1995).
    4. Optional: Carry out a trajectory analysis of the first and last free energy windows.

Notes

  1. Cautionary points on free energy calculations should be noted (Reference 13).
    1. “These rules are not the end-all set and you should be familiar with why each one is suggested before just accepting them”.
    2. “More states are better than fewer. Variance shrinks rapidly with the number of states. You want the difference between intermediaries to be between 2-3 kBT”.
  2. These simulations are repeatable using workflows and histories (Reference 15).
  3. There will be some variability in the molecular ensembles if a new random seed is chosen, and it is recommended to choose a new random seed for each simulation.

Acknowledgments

We would like to acknowledge the Galaxy community, the Galaxy EU team and the Galaxy computational chemistry team on GitHub for tool and code review.
  An original paper and the workshop from which this protocol has been derived is unpublished at the time of this writing. However, this may be found when searching for i) “BRIDGE an open platform for reproducible high throughput free energy” Tharindu Senapathi, Miroslav Suruzhon, Christopher B. Barnett, Jonathan Essex and Kevin J. Naidoo.
  This work is based on research supported by the South African Research Chairs Initiative (SARChI) of the Department of Science and Technology (DST) and National Research Foundation (NRF) grant 449130 and supported by the South African Medical Research Council under a Self-Initiated Research Grant and the Medical Research Council grant (KJN). Even though the work is supported by the MRC, the views and opinions expressed are not those of the MRC but of the authors of the material produced or publicized. TS thanks SARChI for a doctoral award. We thank the University of Cape Town, the National Research Foundation of South Africa (NRF) for support in hosting BRIDGE on the ilifu data centre and the Centre for High Performance Computing for the use of their clusters when developing BRIDGE.

Competing interests

There are no competing interests.

References

  1. Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B. and Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1: 19-25. 
  2. Afgan, E., Baker, D., Batut, B., Van Den Beek, M., Bouvier, D., Čech, M., Chilton, J., Clements, D., Coraor, N. and Grüning, B. A. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46(W1): W537-W544. 
  3. Andrio, P., Hospital, A., Conejero, J., Jordá, L., Del Pino, M., Codo, L., Soiland-Reyes, S., Goble, C., Lezzi, D., Badia, R. M., Orozco, M., Gelpi, J. L. (2019). BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows. Scientific Data 6(1): 169.
  4. Cournia, Z., Allen, B. and Sherman, W. (2017). Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf and Model 57(12): 2911-2937. 
  5. Eastman, P., Friedrichs, M. S., Chodera, J. D., Radmer, R. J., Bruns, C. M., Ku, J. P., Beauchamp, K. A., Lane, T. J., Wang, L.-P., Shukla, D., Tye, T., Houston, M., Stich, T., Klein, C., Shirts, M. R. and Pande, V. S. (2013). OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation. J Chem Theory Comput 9(1): 461-469. 
  6. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D. and Al-Lazikani, B. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1): D1100-D1107. 
  7. Irwin, J. J. and Shoichet, B. K. (2005). ZINC− a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1): 177-182. 
  8. Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S. and Shoemaker, B. A. (2016). PubChem substance and compound databases. Nucleic Acids Res 44(D1): D1202-D1213. 
  9. Klimovich, P. V., Shirts, M. R. and Mobley, D. L. (2015). Guidelines for the analysis of free energy calculations. J Comput Aided Mol Des 29(5): 397-411. 
  10. Morton, A., Baase, W. A., & Matthews, B. W. (1995). Energetic origins of specificity of ligand binding in an interior nonpolar cavity of T4 lysozyme. Biochemistry 34(27): 8564-8575.
  11. Senapathi, T., Bray, S., Barnett, C. B., Grüning, B. and Naidoo, K. J. (2019). Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE). Bioinformatics 35(18): 3508-3509. 
  12. Suruzhon, M., Senapathi, T., Bodnarchuk, M. S., Viner, R., Wall, I. D., Barnett, C. B., Naidoo, K. J. and Essex, J. W. (2020). ProtoCaller: Robust Automation of Binding Free Energy Calculations. J Chem Inf Model 60(4): 1917-1921.
  13. Constructing a Pathway of Intermediate States - AlchemistryWiki. (2016, August 12). Retrieved May 19, 2020, from http://www.alchemistry.org/wiki/Constructing_a_Pathway_of_Intermediate_States.
  14. Galaxy Training: Analysis of molecular dynamics simulations. (2020, January 13). Retrieved May 19, 2020, from https://galaxyproject.github.io/training-material/topics/computational-chemistry/tutorials/analysis-md-simulations/tutorial.html.
  15. Scientificomputing/bioprotocol-paper-2020-sm: Data and workflows - Bioprotocol. (2020, May 19). Retrieved May 19, 2020, from https://zenodo.org/badge/latestdoi/264202784.

简介

[摘要 ] 蛋白质-配体结合预测是药物发现过程的核心。这通常如下进行蛋白质目标,然后一个基因组数据的分析protei N结构的发现。然而,使用审查的方法和协议进行可再现的蛋白质构象分析和配体结合计算的复杂性可能是一个挑战。在这里,我们展示了生物分子反应和相互作用动力学全球环境(BRIDGE), 基于银河生物信息学平台开发的用于计算化学的基于Web的开放源代码计算和分析平台,使遵循基因组学和蛋白质组学的协议共享变得无缝。BRIDGE提供了可用的工具和工作流程来进行蛋白质分子动力学模拟和蛋白质-配体结合的精确自由能计算。我们举例说明了预测T4溶菌酶系统上计算机中蛋白质-配体结合亲和力的动力学和模拟方案。该协议适用于新手和有经验的从业人员。我们证明,使用BRIDGE,协议可以与合作者共享或公开使用,从而使仿真结果和计算能够独立地进行验证和再现。

关键字:蛋白质动力学, 结合, 自由能, 药物开发, 模拟, BRIDGE



[背景 ] 蛋白的Googleplex的配体结合的可能性,最好减少到聚焦组药物的命中,也可以询问在体外和体内以产生候选药物用于临床研究,使用计算化学方法。因此,正确使用准确的计算方法来估计潜在药物分子与蛋白质之间的结合能对于药物发现过程至关重要(Cournia 等人,2017)。想法是配体应优先结合蛋白质并抑制其功能。在这个协议中,您将学习浩W¯¯计算相对蛋白配体结合使用开源的自由能的工具和生物分子反应或相互作用动力学全球环境(BRIDGE)平台自由能(Senapathi 等,2019),这是建立在银河平台(Afgan et al。,2018)。两个配位体之间的自由能差异通过计算在硅片扰动从ë配体到另一个,因为它们是我)结合到蛋白质(例如,T4溶菌酶)和ii)的水溶剂化。设置自由能量分子模拟以在高性能计算(HPC)硬件资源上运行(通常使用脚本和Linux命令行来完成)是一项挑战,并且是获得可靠结果的障碍。除了计算上的挑战外,并非所有步骤都是可重复的,就像使用不同实验室,大学,公共设施和云平台上可用的各种软件和硬件资源运行时一样。但是,BRIDGE开发的目的是提供一个基于Web的平台,其中包括用于仿真,分析和分析的可靠方法。BioExcel Building Blocks软件采用了类似于BRIDGE的方法(Andrio 等,2019)。BRIDGE-Galaxy平台使从遗传学到蛋白质组学到化学的无缝过渡成为可能,并且可以使用精心设计的工作流程进行可重复的计算机模拟和分析。


  我们研究T4溶菌酶作为计算配体相对结合的方案的例证,并以苯与对二甲苯结合到T4溶菌酶为例。以下工具被使用:ProtoCaller 进行设置(Suruzhon 。等人,2020) ,用于分子动力学(MD)模拟GROMACS (亚伯拉罕。等人,2015) ,以及用于模拟分析炼金分析(克里莫维奇等人,2015) 。至少需要两次模拟才能计算结合的相对自由能。首先是水中配体之间的转化,然后是与溶剂化蛋白质结合的配体之间的转化。可以将配体2转换为配体1,反之亦然;方向的选择对于理解计算出的自由能的含义很重要。不需要在两个方向上进行计算,但可以用作确认自由能结果的计算收敛性的附加度量。


  可以通过基因表达的基因组实验和生物信息学分析来鉴定蛋白质靶标,或者可以通过文献检索找到蛋白质靶标,该文献可以指向一个或多个蛋白质靶标。蛋白质结构来自RCSB蛋白质数据库。配体命中的鉴定通常是由与PDB中发现的已知底物或抑制剂具有相似性或使用相似性度量和对接计算衍生自现有预筛选分子库的分子所指导。公共分子数据库是寻找可能的配体的良好资源,例如ChEMBL (Gaulton 等,2012),ZINC (Irwin和Shoichet,2005)和PubChem (Kim 等,2016)。


  将蛋白质从PDB文件准备成可以进行模拟的状态,将配体置于结合位点,以及选择合适的参数进行精确模拟,这是MD模拟的一些直接障碍,对于自由能模拟则更为如此。有许多可供选择的方法制备的蛋白质,例如,PDBFixer (伊士曼等人,2013年)。但是,该准备工作通常需要经过专门培训以应对挑战,例如正确地丢失PDB的环,修复或安装突变的残基,创建二硫键并指定实验上一致的质子化状态。对于配体,精确的电荷需要参数化和分配。在此处介绍的协议中,蛋白质和配体的自动设置是使用Alchemical Setup工具完成的,该工具链接了几个专用工具来执行蛋白质设置和参数设置。


  BRIDGE界面(图1)非常简单,遵循Galaxy设计。工具位于左侧面板中,进度历史记录位于右侧面板中,有关当前工具或所选数据集的信息位于中央面板中。数据可以使用在顶部的绿色载图标上传牛逼ools菜单(左图),而顶部的菜单栏包括用户帐户等信息。要搜索工具,请在搜索栏中输入关键字,然后会显示带有匹配单词的工具。对工具的更改进行了版本控制,可以通过中央面板界面选择可用的版本。通过浏览共享数据并单击已发布的历史记录(https://galaxy-compchem.ilifu.ac.za/histories/list_published),可以找到包含输出的此过程的示例历史记录。选择示例历史记录,然后单击“ +”图标以添加到您的历史记录中。使用眼睛图标浏览示例输出。图2中所示的自由能工作流也是可用的(参考文献15 )。


 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \图1.jpg


图1. Galaxy界面概述。工具在左侧面板中。工具信息息或数据显示在中央面板和历史显示在右侧面板中。


 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \图2.jpg


图2.相对束缚自由能工作流程的概述。PDB格式的受体和SMILES中的配体是“炼金术”设置的输入。使用炼金术运行运行多个模拟,并且使用炼金术分析工具检查收敛性。


 


  作为以下过程的一部分,提供了一个简短的分析,用于确认自由能模拟已收敛。可以对铅抑制剂的分子轨迹进行分析,以确定负责配体结合的分子相互作用,并考虑配体和蛋白质的构象。进一步分析的目的是理解约束的理由。例如,进一步的分析可能包括氢键分析和Ramachandran分析,均方根偏差(RMSD)和主成分分析(PCA)。一个示例工作流是provid Ë d (参考14 )。PC A将揭示催化域的灵活性以及影响抑制剂结合的主要蛋白质运动,而氢键分析将表明配体与蛋白质结合位点之间的关键氢键相互作用。


 


软件


 


本地电脑
要在本地计算资源上运行此代码,请获取BRIDGE Docker(https://github.com/scientificomputing/bridge-docker)并将Dock er映像安装在本地计算机上。所有库,编译器和链接都打包在Docker映像中,因此安装是无缝的。四核处理器,8G 乙RAM和80G 乙硬盘空间足够用于任何操作系统上具有泊坞测试。


公共服务器
可通过Web浏览器获得访问权限,而无需安装软件。可以从https://galaxy-compchem.ilifu.ac.za/访问BRIDGE平台。这些工具可在Galaxy Europe(http://cheminformatics.usegalaxy.eu)上获得。


 


程序


 


使用炼金术设置进行系统准备
登录到BRIDGE服务器。
要开始准备溶菌酶(蛋白质或受体)和苯配体的模拟,请单击Alchemical Setup工具以设置模拟(图3)。将产生九个输出-一个报告,四个结构和四个拓扑。为配体1创建了结构和拓扑,使其在水中和蛋白质中化学转化为配体2,反之亦然。
 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \ 3.jpg


图3.炼金术设置工具。指定了一种蛋白质和两个配体,将生成与GROMACS兼容的输出。


 


输入T4溶菌酶的PDB ID:“ 181L”(https://www.rcsb.org/structure/181L)。
选择“上传文件”,然后更改为“否”。
选择“配体参考”为“ 400”。这是活性位点中的苯分子(残基ID为400)。我们将两个配体(苯和对二甲苯)映射到该苯上。因此,配体参考应为“ 400”。
ProtoCaller 可以使用分子的SMILES符号构建配体。樱雪室温一个SMILES字符串配体1- 使用苯“C1 = CC = CC = C1”。可以使用分子软件或在线数据库生成该字符串。
为配体2用对二甲苯插入SMILES字符串“ CC1 = CC = C(C = C 1)C 。
选择“ PDB链ID”作为链“ A”。
选择“蛋白质力场”作为“ ff14SB ”。这是针对蛋白质开发的AMBER力场,可以选择其他力场。
将所有突变的硒代蛋氨酸更改回蛋氨酸将此选项设置为“是”。
将“删除任何具有备用位置的原子(altLoc B)”设置为“是”。仅使用主要坐标。
在“配体力场”中选择“ gaff2”,即一般琥珀色力场2。
蛋白质-配体复合物需要在一盒水分子中溶解。选择蛋白质-配体系统的盒子尺寸为“ 7” nm。溶菌酶的最长边大约为5 nm,我们使用1.2 nm的无键截止值。我们需要为蛋白质在水箱中自由移动保留一个缓冲区,并确保有足够的空间,使其不与周期图像相互作用。根据所考虑的系统调整此值。
配体还将在水箱中溶剂化。选择模拟框的大小。“ 4” nm对于大多数配体就足够了,但是如果配体长于2.5 nm,则可以增加。大型水箱的仿真将花费更多时间来完成。
最后一个选项是定义放置在水箱内的离子浓度。默认情况下,“0.154”钠M(钠+ )和氯化物(氯- )离子都包括在内,这是生理浓度。
单击“执行”按钮运行此工具。
模拟将开始,并将显示在“历史记录”面板中。创建的文件是GROMACS拓扑(.TOP)和结构(GRO )的炼金运行工具所需的文件。这将生成文件,将蛋白2中的配体2以及水箱中的配体1向前和向后扰动。
单击报告中的眼睛图标,检查仿真的进度。这将需要一些时间。花费的时间将取决于可用的计算资源,也可能是由于工具依赖性检查和安装所致。该工具正在参数化配体力场,该过程将少于20分钟。如果历史记录项目保持灰色超过1天,请联系Galaxy Admin。例如,通过Gitter (https://gitter.im/galaxycomputationalchemistry/Lobby)。
历史记录项目变为绿色时,模拟结束。
 


使用Alchem Run 对蛋白质(配体2至配体1)的相对自由能进行模拟
一旦设置完成,就开始对每个配体的炼金术自由能模拟,这是计算水和活性位点中配体扰动所必需的。炼金仿真在四个步骤对于每个自由能窗口中进行,能量最小化,MD SIMU 在办法第十四正则系综(NVT )合奏接着等温等压系综(NPT )ENS emble。这种预平衡对于消除任何汉密尔顿滞后至关重要。最后,在NpT 集成中进行生产模拟。这些步骤全部由Galaxy中的Alchem Run工具执行(请参见图4)。从这些模拟中,可以计算出相对的结合自由能。在这里,对二甲苯(配体2)对苯(配体1)的简单扰动将被执行,并且为了说明该协议,我们选择了较短的模拟长度。实际上,需要更长的模拟时间。对于自由能运行,生产模拟通常至少为1 ns。对于我们的融合溶菌酶模拟,我们使用以下参数:最小化步骤:10000 ,NVT 步骤:500000(500 ps ),NpT st eps:500000(50 ps ),MD(生产)步骤:1000000(1000 ps ),时间步骤: 0.001 ps。无论选择哪个参数,都必须使用分析工具来检查收敛性。


选择“炼金术运行”工具。该工具需要炼金术设置工具提供的拓扑和结构输出。
选择“结构文件”为“蛋白质结构中的配体2到配体1”。
选择“拓扑文件”作为“蛋白质拓扑中的配体2到配体1”。
将“最小化步骤” 的数量设置为“ 10000”。
将“ NVT 平衡步骤” 的数量设置为“ 500”(1 ps )。
将“ NpT 平衡步骤” 的数量设置为“ 500”(1 ps )。
将“ MD(生产)步数”设置为“ 1000”(2 ps )。
默认情况下,“种子”设置为19880924,必须进行更改。这是启动用于分子动力学的随机数生成器,将其更改为当前日期或任何随机数(例如“ 20200130”)的种子。
将“时间步长”设置为“ 0.002” ps。
创建氢键约束。选择“是”将约束应用于配体,然后选择“ H键约束”。选择“ LINCS顺序”“ 4”,“ LINCS迭代”“ 1”,“ LINCS最大角度”“ 30”。
选择吨他˚F REE Ë NERGY p erturbation(FEP )路径,并选择从较大的配位体(对二甲苯),以去到一个较小的配体(苯)。
输入“ 39”作为“自由能窗口数”。窗口越多,模拟重叠和收敛的可能性就越大。窗口数为零索引,键入39表示有40个窗口。首先,对电荷进行扰动,然后对Van der Waals和Bonding术语进行扰动。在这里,我们从0到1的比例从配体2(0.00)到配体1(1.00)进行化学扰动。0到1之间的任何数字表示电荷,范德华力,键合相互作用如何缩放以形成混合系统。
在模拟的前10个窗口中,将分子的电荷从0.00缩放到1.00,以0.11为增量。其余的自由能窗口不会受到干扰。必须通过输入以下40个数字来描述确切的Lambda值:“ 0.00 0.11 0.22 0.33 0.44 0.56 0.67 0.78 0.89 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00”。
扰动电荷后,在仿真中为最后29个窗口缩放Van der Waals交互作用。这些值从0.00缩放到1.00,以0.04为增量,然后在端点逐渐减小到0.02和0.01,以防止端点灾难。必须描述确切的lambda值,并键入以下40个数字:“ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.08 0.11 0.15 0.19 0.23 0.27 0.30 0.34 0.38 0.42 0.46 0.49 0.53 0.57 0.61 0.65 0.68 0.62 0.76 0.80 0.84 0.87 0.91 0.95 0.97 0.98 0.99 0.99 1.00”。
与Van der Waals交互缩放一并缩放模拟中的键合项。必须描述确切的lambda值,并键入以下40个数字:“ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.08 0.11 0.15 0.19 0.23 0.27 0.30 0.34 0.38 0.42 0.46 0.49 0.53 0.57 0.61 0.65 0.68 0.62 0.76 0.80 0.84 0.87 0.91 0.95 0.97 0.98 0.99 0.99 1.00”。
使用默认的自由能选项。
将温度设置为“ 300” K,压力设置为“ 1” bar。
选择“执行模拟”。如果需要,可以在Galaxy外部生成和模拟输入文件。
点击“执行”。
检查历史记录中的报告以了解模拟进度。输出还将包括FEP / TI(热力学集成)数据和轨迹的“ .tar”文件(存档)。查看这些轨迹,以确保在FEP计算期间未发生非物理事件。
可选:对蛋白质中的配体1至配体2重复此步骤。
 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \图4.jpg


图4.炼金术运行工具。该工具用于在GROMACS中运行最小化,平衡和生产模拟,以实现特定环境中配体之间的炼金转化。


 


使用炼金术运行模拟水(配体2至配体1)中的相对自由能             
对于B,但是将“结构文件”选择为“水结构中的配体2至配体1”,将“拓扑文件”选择为“水结构中的配体2至配体1”。
可选:在水中对配体1至配体2重复此步骤。
 


蛋白质分析中蛋白质中配体2到配体1的分析和收敛性测试
炼金术分析工具(图5)提供了全面的分析(尽管可以使用GROMACS 内置条形模块)。所述data.tar文件FR ö 米炼金运行工具是必需的输入。预期的工具输出如下:


报告和自由能源输出。
自由能窗口的重叠矩阵(如PNG)。
收敛图(如PNG)。
基于曲线拟合方法(CFM)的一致性检查器(如PNG)。
自由能变化分解(如PNG)。
热力学积分图(如PNG)。
有关炼金术分析的更多详细信息,请阅读文章(Klimovich 等,2015)和代码存储库y (https://github.com/MobleyLab/alchemical-analysis)。


打开炼金术分析工具。
选择蛋白质数据输出;这将是“ tar”格式。
将前缀设置为“ md”,炼金术运行默认使用前缀md保存模拟数据。
将温度设置为“ 300” K。
“平衡时间”设置为“ 0” ps。可以更改它以丢弃一些模拟数据。在发现仿真尚未收敛并且需要去除前几个ps 的情况下,这将很有用。
以“ kcal”报告能量。
如果需要,请包括要跳过的lambda状态。默认情况下,不会跳过任何内容。
点击“执行”。
可选:R 重复蛋白中的配体1至配体2。
 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \ Figure 5-word.jpg


图5. 炼金术分析工具。该工具用于分析自由能模拟。用户以tar格式提供了来自GROMACS的仿真数据以及进一步的仿真细节,例如温度。提供的输出帮助用户评估计算是否收敛。


 


水中的配体2到配体1的分析与收敛性分析
至于D,但在水数据输出中选择配体2到配体1。
可选:[R 水EPEAT配体1〜2配体。
 


解释蛋白质中配体2到配体1的分析
用不同的估计器和方法(热力学积分[ TI ] ,缺失指数平均[ DEXP ] ,插入指数平均[ IEXP ] ,贝内特接受比[ BAR ] ,多态贝内特接受比[ MBAR ] )检查计算的自由能值的结果。; 看到图小号S1和S2。
检查自由能变化dF (t)输出,该输出是一个文本文件,其中包含在前后方向上随时间变化的所计算出的自由能。没有运行反向仿真,但是此工具可以从正向仿真中提取该数据。
检查收敛图dG (t),看仿真是否收敛。正向和反向Δ ģ应的容限内的相同,优选在紫色阴影条带(图6)。
 


D:\ Reformatting \ 2020-7-1 \ 1902723--1498 Kevin Naidoo 746178 \ Figs jpg \图6--word.jpg


图6.自由能收敛图。该图用于确认正向和反向ΔG值均在公差范围内。


 


检查重叠矩阵,该矩阵显示模拟中每个自由能窗口的重叠。相邻的自由能窗口之间应有良好的重叠。如果是这样,则表明分层(自由能窗口的数量)足以进行模拟。
考虑自由能变化细分。这样就可以使用所使用的每个自由能估算器直观地计算出每个窗口的自由能。该图表明IEXP与其他方法相比存在很大的偏差。
考虑热力学积分图。数据的可视化对于使用TI计算自由能非常有用。             
考虑基于曲线拟合方法的一致性检查器。这将指示所有模拟是否已收敛。如果不是,则模拟可以是重新运行使用较长的模拟时间(具有更长的模拟时间重复协议B)或不收敛的模拟的数据的部分可以使用炼金分析(重新运行协议d被移除,并设置平衡时间是一个适当的ps数。             
我们已经完成了对蛋白质中从配体2到配体1的扰动的分析。
可选:R 重复蛋白中的配体1至配体2。
 


解释水中配体2到配体1的分析
至于F,但在水分析结果中选择配体2至配体1。
可选:- [R 在水中EPEAT为配体1配体2。
 


计算配体2与配体1的相对自由能
求出用MBAR估计的配体2到配体1中蛋白质的自由能(10.212±0.278 kcal / mol)。
求出用MBAR估算的配体2至配体1在水中的自由能(10.600±0.258 kcal / mol)。
计算结合的相对自由能(差),-0.388±0.379 kcal / mol。该值为负,这意味着配体1(苯)的结合比配体(2)对二甲苯更有利。这与实验差异为-0.52±0.17 kcal / mol很好地相比较(Morton 等,1995)。
可选:Ç ARRY了第一个和最后自由能量窗的轨迹分析。
 


笔记


 


自由能计算的注意事项应注意d(参考文献13 )。
“ 这些规则不是最终的规则,在接受之前,您应该熟悉为什么建议每个规则”。
“更多的州胜于更少。方差随着州的数量而迅速缩小。您希望中介之间的差异在2-3 kBT 之间。
使用工作流和历史记录,这些模拟是可重复的(参考资料15 )。
如果选择新的随机种子,则分子集合体中会存在一定的可变性,建议为每次模拟选择一个新的随机种子。
 


致谢


 


我们要感谢GitHub上的Galaxy社区,Galaxy EU 团队和Galaxy计算化学团队进行工具和代码审查。


  在撰写本文时,尚未发表该协议的原始论文和研讨会。但是,在搜索i )“ BRIDGE一个可重现的高通量自由能的开放平台” 时,可能会找到它们。Tharindu Senapathi ,Miroslav Suruzhon ,Christopher B.Barnett ,Jonathan Essex和Kevin J. Naidoo。


  这项工作基于科学和技术部(DST)的南非研究主席倡议(SARChI )和国家研究基金会(NRF)赠款449130的研究,并由南非医学研究理事会根据自发发起的研究研究补助金和医学研究理事会补助金(KJN)。即使这项工作得到了MRC的支持,但所表达的观点和意见并非MRC的,而是所制作或宣传的材料的作者。TS感谢SARChI 授予的博士学位。我们感谢开普敦大学,南非国家研究基金会(NRF)为将BRIDGE托管在ilifu 数据中心和高性能计算中心在开发BRIDGE时使用其集群所提供的支持。


 


利益争夺


 


没有利益冲突。






参考文献


 


亚伯拉罕(MJ),穆尔托拉(T.),舒尔兹(R.),帕尔(S.),史密斯(JC),黑斯(B.Hss)和B.琳达(E.)GROMACS:通过从笔记本电脑到超级计算机的多级并行性进行的高性能分子模拟。SoftwareX 1:19-25。              
Afgan ,E.,Baker,D.,Batut ,B.,Van Den Beek,M.,Bouvier,D.,Čech ,M.,Chilton,J.,Clements,D.,Coraor ,N. andGrüning ,BA (2018)。用于可访问,可重复和协作式生物医学分析的Galaxy平台:2018年更新。核酸研究46(W1):W537-W544。              
Andrio ,P.,Hospital,A.,Conejero ,J.,Jordá ,L.,Del Pino,M.,Codo ,L.,Soiland -Reyes,S.,Goble,C.,Lezzi ,D。,Badia , RM,Orozco,M.,Gelpi ,JL(2019)。BioExcel Building Blocks,一个可互操作的生物分子模拟工作流程的软件库。科学数据6(1):169。
Cournia ,Z.,Allen,B.和Sherman,W.(2017年)。药物发现中的相对结合自由能计算:最新进展和实际考虑。J化学信息与模型57(12):2911-2937。              
Eastman,P.,Friedrichs,MS,Chodera ,JD,Radmer ,RJ,Bruns,CM,Ku,JP,Beauchamp,KA,Lane,TJ,Wang,L.-P.,Shukla,D.,Tye ,T. ,休斯顿,麻省,斯蒂奇,T。,克莱恩,C。,衬衫,MR和潘德,VS(2013年)。OpenMM 4:用于高性能分子模拟的可重用,可扩展,独立于硬件的库。J化学理论计算9(1):461-469。              
Gaulton ,A.,Bellis,LJ,Bento,AP,Chambers,J.,Davies,M.,Hersey,A.,Light,Y.,McGlinchey,S.,Michalovich ,D.和Al- Lazikani ,B.( 2012)。ChEMBL:用于药物发现的大规模生物活性数据库。核酸研究40(D1):D1100-D1107。              
Irwin,JJ和Shoichet,BK(2005)。ZINC-用于虚拟筛选的市售化合物的免费数据库。J化学信息模型45(1):177-182。              
Kim,S.,Thiessen,PA,Bolton,EE,Chen,J.,Fu,G.,Gindulyte ,A.,Han,L.,He,J.,He,S. and Shoemaker,BA(2016)。PubChem物质和化合物数据库。核酸研究44(D1):D1202-D1213。              
Klimovich ,PV,衬衫,MR和Mobley,DL(2015)。自由能计算分析指南。J 计算辅助分子研究29(5):397-411。              
Morton,A.,Baase ,WA,&Matthews,BW(1995)。T4溶菌酶内部非极性腔中配体结合特异性的能量来源。生物化学34(27):8564-8575。
Senapathi ,T.,Bray,S.,Barnett,CB,Grüning ,B.和Naidoo,KJ(2019)。生物分子反应和相互作用动力学全球环境(BRIDGE)。生物信息学35(18):3508-3509。              
Suruzhon ,M.,Senapathi ,T.,Bodnarchuk,MS,Viner,R.,Wall,ID,Barnett,CB,Naidoo,KJ and Essex,JW(2020)。ProtoCaller:束缚自由能计算的强大自动化。Ĵ化学Inf文件模式升60(4):1917-1921。
构建中间国家的途径-AlchemWiki 。(2016年8月12日)。于2020年5月19日从http://www.alchemistry.org/wiki/Constructing_a_Pathway_of_Intermediate_States检索。
Galaxy培训:分子动力学模拟分析。(2020年1月13日)。于2020年5月19日从https://galaxyproject.github.io/training-material/topics/computational-chemistry/tutorials/analysis-md-simulations/tutorial.html检索。
科学计算/ bioprotocol-paper-2020-sm:数据和工作流程-Bioprotocol 。(2020年,5月19日)。检索自2020年5月19日,从https://zenodo.org/badge/latestdoi/264202784。
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引用:Senapathi, T., Barnett, C. B. and Naidoo, K. J. (2020). BRIDGE: An Open Platform for Reproducible Protein-Ligand Simulations and Free Energy of Binding Calculations. Bio-protocol 10(17): e3731. DOI: 10.21769/BioProtoc.3731.
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如果您对本实验方案有任何疑问/意见, 强烈建议您发布在此处。我们将邀请本文作者以及部分用户回答您的问题/意见。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。

Wayde Veldman
Rhodes University
This link is not working: https://galaxy-compchem.ilifu.ac.za/
2021/6/10 0:00:24 回复
Tharindu Senapathi

Dear Wade, Thank you for informing us. This link is working now.

2021/6/15 5:48:28 回复


Christopher Barnett
University of Cape Town

Hi Wayde
Thanks for your feedback. The servers were down (see https://github.com/galaxycomputationalchemistry/usegalaxy-za/issues/12) and this has now been resolved.
All the best,
Chris

2021/6/15 7:26:01 回复


Tao Sun
To follow up my previous post, this the error message after clicking the eye icon from report. Hope someone could help me to fix this. I just found this is really cool and convenient if we can use such a web server to prepare relative free energy perturbation. Thanks.
2021/2/19 16:58:01 回复
Tharindu Senapathi

Dear Tao,

Thanks for your interst in our tools. There were some issues with the server, now we have fixed them. We also change the tool version (to Galaxy Version 1.2.0.1) and did some testing. Seems like all works now. Please try now and let me know if you have any issues.

Tharindu

2021/2/22 5:47:44 回复


Tao Sun
I have no idea why every time I always get a failed return message, from alchemsetup , with all parameters kept default.
2021/2/19 16:47:12 回复
Kevin Naidoo
University of Cape Town

Dear Tao

Can you let us know if you are able to generate the correct parameters after the bug fix?

All the best

2021/2/22 10:28:28 回复