High Throughput Molecular Dynamics and Analysis

Overview

question Questions
  • How are protein-ligand systems parameterized for molecular dynamics simulation?

  • What kind of analysis can be carried out on molecular trajectories?

  • How can high-throughput MD be used to study multiple ligands?

objectives Objectives
  • Learn about force-fields and MD parameterization

  • Learn how to conduct MD simulation and analysis for a protein-ligand system

  • Understand how different molecular interactions contribute to the binding affinity of various ligands for the Hsp90 protein.

requirements Requirements

time Time estimation: 3 hours

level Level: Advanced level level level

Supporting Materials

last_modification Last modification: Jul 17, 2020

Introduction

This tutorial provides an introduction to using high-throughput molecular dynamics to study protein-ligand interaction, as applied to the N-terminal domain of Hsp90 (heat shock protein 90).

Agenda

In this tutorial, we will cover:

  1. Background
    1. What is high-throughput molecular dynamics?
    2. Why is Hsp90 interesting to study?
    3. Get data
  2. Simulation
    1. Topology generation
    2. Combine topology and GRO files
    3. Create the simulation box
    4. Solvation
    5. Energy minimization
    6. Equilibration
    7. Production simulation
  3. Analysis
    1. Create PDB file needed by most analysis tools
    2. Convert trajectory to DCD format
    3. RMSD analysis - protein
    4. RMSD analysis - ligand
    5. RMSF analysis
    6. PCA analysis
    7. Hydrogen bond analysis
  4. Optional: Automating high throughput calculations

Background

What is high-throughput molecular dynamics?

Molecular dynamics (MD) is a method to simulate molecular motion by iterative application of Newton’s laws of motion. It is often applied to large biomolecules such as proteins or nucleic acids. A common application is to assess the interaction between these macromolecules and a number of small molecules (e.g. potential drug candidates). This tutorial provides a guide to setting up and running a high-throughput workflow for screening multiple small molecules, using the open-source GROMACS tools provided through the Galaxy platform. Following simulation, the trajectory data is analyzed using a range of tools to investigate structural properties and correlations over time.

Why is Hsp90 interesting to study?

The 90 kDa heat shock protein (Hsp90) is a chaperone protein responsible for catalyzing the conversion of a wide variety of proteins to a functional form; examples of the Hsp90 clientele, which totals several hundred proteins, include nuclear steroid hormone receptors and protein kinases (Pearl and Prodromou 2006). The mechanism by which Hsp90 acts varies between clients, as does the client binding site; the process is dependent on post-translational modifications of Hsp90 and the identity of co-chaperones which bind and regulate the conformational cycle (Schopf et al. 2017).

Due to its vital biochemical role as a chaperone protein involved in facilitating the folding of many client proteins, Hsp90 is an attractive pharmaceutical target. In particular, as protein folding is a potential bottleneck to cellular reproduction and growth, blocking Hsp90 function using inhibitors which bind tightly to the ATP binding site of the NTD could assist in treating cancer; for example, the antibiotic geldanamycin and its analogs are under investigation as possible anti-tumor agents (Stebbins et al. 1997, Hermane et al. 2019).

In the structure which will be examined during this tutorial, the ligand of concern is a resorcinol, a common class of compounds with affinity for the Hsp90 N-terminal domain. It is registered in the PubChem database under the compound ID 135508238 (PubChem). As can be seen by viewing the PDB structure, the resorcinol part of the structure is embedded in the binding site, bound by a hydrogen bond to residue aspartate-93. The ligand structure also contains a triazole and a fluorophenyl ring, which lie nearer to the surface of the protein.

Hsp90 structure, with a ligand bound
Figure 1: Structure of Hsp90, with a ligand bound. Click to view in NGL. (Rose et al. 2018)

Get data

As a first step, we create a new Galaxy history and then we download a crystal structure for the Hsp90 protein from the Protein Data Bank (PDB). The structure is provided under accession code 6HHR (Schuetz et al. 2018) and shows Hsp90 in complex with a ligand belonging to the resorcinol class.

hands_on Hands-on: Data upload

  1. Create a new history for this tutorial
  2. Search Galaxy for the ‘Get PDB’ tool. Request the accession code 6hhr.
  3. Rename the dataset to ‘Hsp90 structure’
  4. Check that the datatype is correct (PDB file).

    tip Tip: Changing the datatype

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, click on the galaxy-chart-select-data Datatypes tab on the top
    • Select datatypes
    • Click the Change datatype button

Simulation

Topology generation

Now we have downloaded a PDB structure of the protein we wish to study, we will start preparing it for MD simulation; this process may also be referred to as parameterization or topology generation.

GROMACS distinguishes between constant and dynamic attributes of the atoms in the system. The constant attributes (e.g. atom charges, bonds connecting atoms) are listed in the topology (TOP file), while dynamic attributes (attributes that can change during a simulation, e.g. atom position, velocities and forces) are stored in structure (PDB or GRO) and trajectory (XTC and TRR) files.

The PDB file we start from only explicitly states atom element (i.e.carbon, oxygen, and so on) and 3D Cartesian coordinates of each atom; additionally, it will usually not include hydrogen atoms. Therefore, before beginning simulation, we need to calculate the rest of the information contained within the topology file. Parameterization needs to be done separately for the ligand and protein. Therefore, the first step is to separate the PDB file into two sets of coordinates - one for the ligand and one for the protein.

Extract protein and ligand coordinates

Parameterization needs to be done separately for the ligand and protein. Therefore, the first step is to separate the PDB file into two sets of coordinates - one for the ligand and one for the protein. Here, we can make use of the simple text manipulation tools integrated into Galaxy.

question Question

  1. Why do protein and ligand need to be parameterized separately?

solution Solution

  1. Protein and small molecules are constructed differently. A protein is made up of 20 different building blocks (amino acids) - therefore, to construct a protein topology, amino acid topologies simply need to be combined appropriately. By contrast, the structure of small molecules is far more flexible and needs to be calculated for each different structure.

hands_on Hands-on: Separate protein and ligand coordinates

  1. Search in textfiles tool with the following parameters:
    • “Select lines from”: ‘Hsp90 structure’
    • “that”: Don't Match
    • “Regular Expression: HETATM
  2. Rename output to ‘Protein (PDB)’
  3. Search in textfiles tool with the following parameters:
    • “Select lines from”: ‘Hsp90 structure’
    • “that”: Match
    • “Regular Expression: AG5E
  4. Rename output to ‘Ligand (PDB)’

Here, we simply filter the original PDB twice: once for lines which do not match HETATM, which returns a PDB file containing only protein, not ligand and solvent; and once for lines which match the ligand’s identity code AG5E, which returns a PDB file containing only the ligand.

Set up protein topology

Firstly, we need to calculate the topology for the protein file. We will use the GROMACS initial setup tool tool.

hands_on Hands-on: Generate protein topology

  1. GROMACS initial setup tool with the following parameters:
    • “PDB input file”: ‘Protein (PDB)’ file
    • “Force field”: AMBER99SB
    • “Water model”: TIP3P
    • “Generate detailed log”: Yes

    comment Comment

    A force field is essentially a function to calculate the potential energy of a system, based on various empirical parameters (for the atoms, bonds, charges, dihedral angles and so on). There are a number of families of forcefields; some of the most commonly used include CHARMM, AMBER, GROMOS and OpenFF (for a recent, accessible overview see (Lemkul 2020).

    A wide range of models exist for modeling water. Here we are using the common TIP3P model, which is an example of a ‘three-site model’ - so-called because the molecule is modeled using three points, corresponding to the three atoms of water. (Four- and five-site models include additional ‘dummy atoms’ representing the negative charges of the lone pairs of the oxygen atom).

The tool produces four outputs: a GRO file (containing the coordinates of the protein), a TOP file (containing other information, including on charges, masses, bonds and angles), an ITP file (which will be used to restrain the protein position in the equilibration step later on), and a log for the tool.

Please note all GROMACS tools output a log. Generally, you only need to look at this when a job fails. These provide useful information for debugging if we encounter any problems.

Generate a topology for the ligand

To generate a topology for the ligand, we will use the acpype tool tool (da Silva and Vranken 2012). This provides a convenient interface to the AmberTools suite and allows us to easily create the ligand topology in the format required by GROMACS.

Inspecting the contents of the Ligand (PDB) file shows that it contains no hydrogen atoms. These need to be added before the topology can be calculated. The Compound conversion tool (which is based on OpenBabel) can be used to achieve this.

hands_on Hands-on: Generate ligand topology

  1. Compound conversion tool with the following parameters:
    • “Molecular input file”: ‘Ligand (PDB)’
    • “Output format”: Protein Data Bank format (pdb)
    • “Add hydrogens appropriate for pH”: 7.0
  2. Rename the output file to Hydrated ligand (PDB).
  3. Generate MD topologies for small molecules tool with the following parameters:
    • “Input file”: ‘Hydrated ligand (PDB)’
    • “Charge of the molecule”: 0
    • “Multiplicity”: 1
    • “Force field to use for parameterization”: gaff
    • “Save GRO file?”: Yes

Here, we use GAFF (general AMBER force field), which is a generalized AMBER force field which can be applied to almost any small organic molecule.

We select charge and multiplicity as appropriate. The ligand studied here is neutral, so the charge is 0. The multiplicity is 1, which will be the case for every simple organic molecule we encounter; only if we deal with more exotic species such as metal complexes or carbenes will we need to consider higher values.

Having generated topologies, we now need to combine them, define the box which contains the system, add solvent and ions, and perform an energy minimization step.

Combine topology and GRO files

While we have separate topology and structure files for both protein and ligand, we need to combine them into a single set of files to continue with the simulation setup. A dedicated Galaxy tool is provided for this, using the Python library ParmEd (Swails et al. 2016).

hands_on Hands-on: Combine GRO and topology files

  1. Merge GROMACS topologies tool with the following parameters:
    • param-file “Protein topology (TOP) file”: TOP file created by the GROMACS initial setup tool
    • param-file “Ligand topology (TOP or ITP file)”: Topology created by the acpype tool
    • param-file “Protein structure (GRO) file”: GRO file created by the GROMACS initial setup tool
    • param-file “Ligand structure (GRO) file”: Structure file (GRO format) created by the acpype tool

Create the simulation box

The next step, once combined coordinate (GRO) and topology (TOP) files have been created, is to create a simulation box in which the system is situated.

hands_on Hands-on: Create simulation box

  1. GROMACS structure configuration tool with the following parameters:
    • param-file “Input structure”: System GRO file (Input dataset)
    • “Configure box?”: Yes
      • “Box dimensions in nanometers”: 1.0
      • “Box type”: Triclinic
    • “Generate detailed log”: Yes

    comment Comment

    This tool returns a new GRO structure file, containing the same coordinates as before, but defining a simulation box such that every atom is a minimum of 1 nm from the box boundary. A distance of at least 1 nm is recommended to avoid interactions between the protein and its mirror image. On the other hand, increasing the box size too far will increase the simulation time, due to the greater number of solvent molecules which need to be treated. A variety of box shapes are available to choose: we select triclinic, as it provides the most efficient packing in space and thus fewer computational resources need to be devoted to simulation of solvent.

Solvation

The next step is solvation of the newly created simulation box - as we are simulating under biological conditions, we use water as the solvent. Note that the system is charged (depending on the pH) - the solvation tool also adds sodium or chloride ions (replacing existing water molecules) as required to neutralize this.

hands_on Hands-on: Solvation

  1. GROMACS solvation and adding ions tool with the following parameters:
    • param-file “GRO structure file”: output (output of GROMACS structure configuration tool)
    • param-file “System topology”: output
    • “Generate detailed log”: Yes

Energy minimization

After the solvation step, parameterization of the system is complete and preparatory simulations can be performed. The first of theses is energy minimization, which can be carried out using the GROMACS energy minimization tool tool.

question Question

  1. What is the purpose of energy minimization?

solution Solution

  1. Running an energy minimization (EM) algorithm relaxes the structure by removing any steric clashes or unusual geometry which would artificially raise the energy of the system.

hands_on Hands-on: Energy minimization

  1. GROMACS energy minimization tool with the following parameters:
    • param-file “GRO structure file.”: GRO output of GROMACS solvation and adding ions tool
    • param-file “Topology (TOP) file.”: TOP output of GROMACS solvation and adding ions tool
    • “Parameter input”: Use default (partially customisable) setting
      • “Number of steps for the MD simulation”: 50000
      • “EM tolerance”: 1000.0
    • “Generate detailed log”: Yes
    • Rename output to Minimized GRO file

The EM tolerance here refers to the maximum force which will be allowed in a minimized system. The simulation will be terminated when the maximum force is less than this value, or when 50000 steps have elapsed.

As an aside, we can use the Extract energy components tool to plot the convergence of the potential energy during the minimization.

hands_on Hands-on: Checking EM convergence

  1. Extract energy components with GROMACS tool with the following parameters:
    • param-file “EDR file.”: EDR output of GROMACS energy minimization tool
    • “Terms to calculate”: Potential
    • “Output format”: Galaxy tabular
  2. On the output tabular file, click on the ‘Visualize this data’ icon. This provides a range of visualization options. Select ‘Line chart (jqPlot)’.
  3. In the visualization window which appears, click on Select data. Enter the following parameters:
    • “Provide a label”: Energy potential
    • “Values for x-axis”: Column: 1
    • “Values for y-axis”: Column: 2

The resulting plot should resemble the figure below. The system first drops rapidly in energy, before slowly converging on the minimized state.

Energy potential during the EM simulation
Figure 2: Energy potential during the EM simulation. Click to view as a Galaxy visualization

Equilibration

We now carry out equilibration in two stages: NVT and NPT. This is discussed at greater length in the basic GROMACS tutorial. Equilibration requires restraining the protein structure - we use the ITP file produced by the initial setup tool for this.

Simulation under the NVT ensemble allows the solvent to be brought to the desired temperature, while simulation under the NPT ensemble bring the solvent to the correct pressure.

comment More detail about equilibration

At this point equilibration of the solvent around the solute (i.e. the protein) is necessary. This is performed in two stages: equilibration under an NVT (or isothermal-isochoric) ensemble, followed by an NPT (or isothermal-isobaric) ensemble. Use of the NVT ensemble entails maintaining constant number of particles, volume and temperature, while the NPT ensemble maintains constant number of particles, pressure and temperature.

For equilibration, the protein must be held in place while the solvent is allowed to move freely around it. This is achieved using the position restraint file (ITP) we created in system setup. When we specify this restraint, protein movement is not forbidden, but is energetically penalized.

hands_on Hands-on: NVT equilibration

  1. GROMACS simulation tool with the following parameters:
    • param-file “GRO structure file”: Minimized GRO file (from energy minimization step)
    • param-file “Topology (TOP) file”: TOP file produced by solvation step.
    • In “Inputs”:
      • param-file “Position restraint (ITP) file”: ITP file produced by initial setup step.
    • In “Outputs”:
      • “Trajectory output”: Return .xtc file (reduced precision)
      • “Structure output”: Return .gro file
      • “Produce a checkpoint (CPT) file”: Produce CPT output
      • “Produce an energy (EDR) file”: Produce EDR output
    • In “Settings”:
      • “Parameter input”: Use default (partially customisable) setting
        • “Bond constraints (constraints)”: All bonds (all-bonds).
        • “Temperature /K”: 300
        • “Step length in ps”: 0.001
        • “Number of steps that elapse between saving data points (velocities, forces, energies)”: 1000
        • “Number of steps for the simulation”: 50000
    • “Generate detailed log”: Yes

Once the NVT equilibration is complete, it is worth using the Extract energy components tool again to check whether the system temperature has converged on 300 K. This can be done as described for energy minimization, this time specifying Temperature under Terms to calculate rather than Potential. The plot should show the temperature reaching 300 K and remaining there, albeit with some fluctuation.

Having stabilized the temperature of the system with NVT equilibration, we also need to stabilize the pressure of the system. We therefore equilibrate again using the NPT (constant number of particles, pressure, temperature) ensemble.

Note that we can continue where the last simulation left off (with new parameters) by using the checkpoint (CPT) file saved at the end of the NVT simulation.

hands_on Hands-on: NPT equilibration

  1. GROMACS simulation tool with the following parameters:
    • param-file “GRO structure file”: GRO output of GROMACS simulation tool (NVT equilibration)
    • param-file “Topology (TOP) file”: TOP file produced by solvation step.
    • In “Inputs”:
      • param-file “Checkpoint (CPT) file”: Output of GROMACS simulation tool (NVT equilibration))
      • param-file “Position restraint (ITP) file”: ITP file produced by initial setup step.
    • In “Outputs”:
      • “Trajectory output”: Return .xtc file (reduced precision)
      • “Structure output”: Return .gro file
      • “Produce a checkpoint (CPT) file”: Produce CPT output
      • “Produce an energy (EDR) file”: Produce EDR output
    • In “Settings”:
      • “Ensemble”: Isothermal-isobaric ensemble (NPT)
      • “Parameter input”: Use default (partially customisable) setting
        • “Bond constraints (constraints)”: All bonds (all-bonds).
        • “Temperature /K”: 300
        • “Step length in ps”: 0.001
        • “Number of steps that elapse between saving data points (velocities, forces, energies)”: 1000
        • “Number of steps for the simulation”: 50000
    • “Generate detailed log”: Yes

After the NPT equilibration is complete, Extract energy components tool can be used again to view the pressure of the system. This is done as described for energy minimization, specifying Pressure under Terms to calculate. The plot should show convergence on 1 bar and remain there, although some fluctuation is expected.

Production simulation

We can now remove the restraints and continue with the production simulation. The simulation will run for 1 million steps, with a step size of 1 fs, so will have a total length of 1 ns. This is rather short compared to the state-of-the-art, but sufficient for the purposes of a tutorial. For longer-scale simulations, the tool can be used multiple times (with the checkpoint file) to continue the existing simulation.

hands_on Hands-on: Task description

  1. GROMACS simulation tool with the following parameters:
    • param-file “GRO structure file”: Output of GROMACS simulation tool (NPT equilibration)
    • param-file “Topology (TOP) file”: Output of the solvation step
    • In “Inputs”:
      • param-file “Checkpoint (CPT) file”: Output of GROMACS simulation tool (NPT simulation))
    • In “Outputs”:
      • “Trajectory output”: Return .xtc file (reduced precision)
      • “Structure output”: Return .gro file
      • “Produce a checkpoint (CPT) file”: Produce CPT output
    • In “Settings”:
      • “Ensemble”: Isothermal-isobaric ensemble (NPT)
      • “Parameter input”: Use default (partially customisable) setting
        • “Temperature /K”: 300
        • “Step length in ps”: 0.001
        • “Number of steps that elapse between saving data points (velocities, forces, energies)”: 1000
        • “Number of steps for the simulation”: 1000000
    • “Generate detailed log”: Yes

Analysis

After the completion of the simulation, the following questions arise: 1) is the simulation converged enough, and 2) what interesting molecular properties are observed. To answer these questions, an analysis of the GROMACS simulation outputs (structure and trajectory file) will be carried out using Galaxy tools developed for computational chemistry (Senapathi et al. 2019) based on popular analysis software, such as MDAnalysis (Michaud‐Agrawal et al. 2011), MDTraj (McGibbon et al. 2015), and Bio3D (Skjærven et al. 2014). These tools output both tabular files as well as a variety of attractive plots.

Create PDB file needed by most analysis tools

Before beginning a detailed analysis, the structure and trajectory files generated previously need to be converted into different formats. First, convert the structural coordinates of the system in GRO format into PDB format. This PDB file will be used by most analysis tools as a starting structure. Next, convert the trajectory from XTC to DCD format, as a number of tools (particularly those based on Bio3D) only accept trajectories in DCD format.

hands_on Hands-on: Convert coordinate format

  1. GROMACS structure configuration tool with the following parameters:
    • “Output format”: PDB file
    • “Configure box?”: No

    comment Comment

    This tool can also be used to carry out initial setup (as discussed in the simulation methods section) for GROMACS simulations and convert from PDB to GRO format.

Convert trajectory to DCD format

Convert from XTC to DCD format. A number of the analysis tools being used have been built to analyse trajectories in CHARMM’s DCD format.

hands_on Hands-on: Convert trajectory format

  1. MDTraj file converter tool with the following parameters:
    • “Output format”: DCD file

    comment Comment

    This tool can also be used to interconvert between several trajectory formats.

RMSD analysis - protein

The Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) are calculated to check the stability and conformation of the protein and ligand through the course of the simulation. RMSD is a standard measure of structural distance between coordinate sets that measures the average distance between a group of atoms. The RMSD of the Cα atoms of the protein backbone is calculated here and is a measure of how much the protein conformation has changed between different time points in the trajectory.

hands_on Hands-on: RMSD Analysis

  1. RMSD Analysis tool with the following parameters:
    • “Select domains”: C-alpha

    comment Comment

    Note that for more complex systems, you may need to consider a more focused selection. For example, if you have a ligand that is a protein consider modifying this selection.

RMSD timeseries Hsp90
Figure 3: RMSD timeseries for the Hsp90 Cα atoms
RMSD histogram Hsp90
Figure 4: RMSD histogram for the Hsp90 Cα atoms

The RMSD time series for the protein shows a thermally stable and equilibrated structure that plateaus at 1.0Å with an average RMSD between 0.8Å and 1.0Å. There are no large conformational changes during the simulation. The RMSD histogram confirms this. Note these graphs are automatically created by Galaxy as part of the tool’s outputs.

RMSD analysis - ligand

Calculating the RMSD of the ligand is necessary to check if it is stable in the active site and to identify possible binding modes. If the ligand is not stable, there will be large fluctuations in the RMSD.

For the RMSD analysis of the ligand, the Select domains parameter of the tool can for convenience be set to Ligand; however, this automatic selection sometimes fails. The other alternative, which we apply here, is to specify the Residue ID in the textbox provided. In this example the ligand’s Residue ID is G5E. The output is the requested RMSD data as a time series, the RMSD plotted as a time series and as a histogram.

hands_on Hands-on: RMSD analysis

  1. RMSD Analysis tool with the following parameters:
    • param-file “DCD trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB input”: output (output of GROMACS structure configuration tool)
    • “Select domains”: Residue ID
      • “Residue ID”: G5E

In our case the ligand is stable with a single binding mode. The RMSD fluctuates around 0.3Å, with a slight fluctuation near the end of the simulation. This is more clearly seen in the histogram. The conformation seen during simulation is very similar to that in the crystal structure and the ligand is stable in the active site.

RMSD timeseries Hsp90 ligand
Figure 5: RMSD timeseries for the Hsp90 Residue ID G5E (ligand)
RMSD histogram Hsp90 ligand
Figure 6: RMSD histogram for the Hsp90 Residue ID G5E (ligand)

RMSF analysis

The Root Mean Square Fluctuation (RMSF) is valuable to consider, as it represents the deviation at a reference position over time. The fluctuation in space of particular amino acids in the protein are considered. The Cα of the protein, designated by C-alpha, is a good selection to understand the change in protein structure. Depending on the system these fluctuations can be correlated to experimental techniques including Nuclear Magnetic Resonance (NMR) and M"{o}ssbauer spectroscopy (Berjanskii and Wishart 2006, Kuzmanic and Zagrovic 2010). The output from the tools is the requested RMSF data and the RMSF plotted as a time series.

hands_on Hands-on: RMSF analysis

  1. RMSF Analysis tool with the following parameters:
    • param-file “DCD trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB input”: output (output of GROMACS structure configuration tool)
    • “Select domains”: C-alpha
RMSF Hsp90
Figure 7: RMSF(Å) vs the residue position. Large fluctuations occur at various positions, which correspond to flexible loop regions on the surface of the protein.

When considering the RMSF, fluctuations greater than 1.0Å are of interest; for example see the fluctuations near residue positions 50, 110 and 160. Inspecting the structure with molecular visualization software such as VMD, these can be seen to correspond to flexible loop regions on the protein surface. In addition, very large fluctuations are seen for the C-terminus; this is common and no investigation is needed.

Note that the first few residues of this protein are missing in the PDB, and therefore residue position 0 in the RMSF corresponds to position 17 in the Hsp90 FASTA primary sequence. This is a fairly common problem that can occur with molecular modeling of proteins, where there may be missing residues at the beginning or within the sequence.

PCA analysis

Principal component analysis (PCA) converts a set of correlated observations (movement of selected atoms in protein) to a set of principal components (PCs) which are linearly independent (or uncorrelated). Here several related tools are used. The PCA tool calculates the PCA in order to determine the relationship between statistically meaningful conformations (major global motions) sampled during the trajectory. The Cα carbons of the protein backbone are again a good selection for this purpose. Outputs include the PCA raw data and figures of the relevant principal components (PCs) as well as an eigenvalue rank plot which is used to visualize the proportion of variance due to each principal component (remembering that the PCs are ranked eigenvectors based on the variance). Having discovered the principal components usually these are visualized. The PCA visualization tool will create trajectories of specific principal components which can be viewed in a molecular viewer such as VMD (Humphrey et al. 1996) or NGL viewer (Rose et al. 2018). We also consider the PCA cosine content which when close to 1 indicates that the simulation is not converged and a longer simulation is needed. For values below 0.7, no statement can be made about convergence or lack thereof.

hands_on Hands-on: PCA

  1. PCA tool with the following parameters:
    • param-file “DCD trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB input”: output (output of GROMACS structure configuration tool)
    • “Select domains”: C-alpha
PCA Hsp90
Figure 8: PCA results which include graphs of PC2 vs PC1, PC2 vs PC3, PC3 vs PC1 colored from blue to red in order of time, and an eigenvalue rank plot. In the eigenvalue plot the cumulative variance is labeled for each data point.

The first three principal components are responsible for 32.8% of the total variance, as seen in the eigenvalue rank plot. The first principal component (PC1) accounts for 15.4% of the variance (see PC1 vs PC2 and eigenvalue rank plots). Visualization of PC1 using VMD shows a rocking motion and wagging of the C-terminus.

hands_on Hands-on: PCA cosine content calculation

  1. Cosine Content tool with the following parameters:
    • param-file “DCD/XTC trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB/GRO input”: output (output of GROMACS structure configuration tool)

The PCA cosine content of the dominant motion related to PC1 is 0.93, indicating that the simulation is not fully converged. This is expected due to the short simulation length. For production level simulations, it is the norm to extend simulations to hundreds of nanoseconds in length, if not microseconds. As this tutorial is designed to be carried out on public webservers, we limit simulations to 1 ns, as we cannot provide a large amount of computational resources for training purposes.

hands_on Hands-on: PCA visualisation

  1. PCA visualization tool with the following parameters:
    • param-file “DCD trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB input”: output (output of GROMACS structure configuration tool)
    • “Select domains”: C-alpha
PC1 Hsp90 gif
Figure 9: PC1 motion for Hsp90

Hydrogen bond analysis

Hydrogen bonding interactions contribute to binding and are worth investigating, in particular persistent hydrogen bonds. All possible hydrogen bonding interactions between the two selected regions, here the protein and the ligand, are investigated over time using the VMD hydrogen bond analysis tool included in Galaxy. Hydrogen bonds are identified and in the output the total number of hydrogen bonds and occupancy over time is returned.

hands_on Hands-on: Hydrogen bond analysis

  1. Hydrogen Bond Analysis using VMD tool with the following parameters:
    • param-file “DCD/XTC trajectory input”: output (output of MDTraj file converter tool)
    • param-file “PDB/GRO input”: output (output of GROMACS structure configuration tool)
    • “Selection 1”: protein
    • “Selection 2”: resname G5E

The active site of this protein is quite hydrophobic, yet multiple hydrogen bonds were identified. The hydrogen bond between aspartate-93 and the ligand (as identified in the crystal structure) was found to be persistent, meeting the hydrogen bond criteria for 89.22% of the simulation. A hydrogen bond between the ligand and the carbonyl group of glycine-97 was found to have a 15.27% occupancy. Hydrogen bonding interactions with threonine-184, asparagine-51 and lysine-58 were also observed but these are not persistent and only present for a minority of the simulation. These values can be accessed from the ‘Percentage occupancy of the H-bond’ output of the hydrogen bond analysis tool.

Optional: Automating high throughput calculations

Up until this step, Galaxy tools have been applied sequentially to datasets. This is useful to gain an understanding of the steps involved, but becomes tedious if the workflow needs to be run on multiple protein-ligand systems. Fortunately, Galaxy allows entire workflows to be executed with a single mouse-click, enabling straightforward high-throughput analyses.

We will demonstrate the high-throughput capabilities of Galaxy by running the workflow detailed so far on a further three ligands.

hands_on Hands-on: High-throughput MD

  1. Create a new history for running the high-throughput workflow and name it Hsp90 HTMD simulation
  2. Upload the SD-file containing the new ligand structures from Zenodo and rename it Ligands (SDF)
  3. Import the simulation workflow from the European (Galaxy | Europe | Accessible History | Protein-ligand HTMD simulation) or the South African Galaxy server (Galaxy | South Africa | Accessible History | Protein-ligand HTMD analysis).
  4. Run the imported workflow with the following parameters:
    • “Send results to a new history”: Yes
    • “History name results to a new history”: Hsp90 HTMD analysis
    • “GRO input”: ‘Collection of GRO files produced by simulation workflow
    • “XTC input”: ‘Collection of XTC files produced by simulation workflow

This process runs the entire simulation and analysis procedure described so far on the new set of ligands. It uses Galaxy’s collection feature to organize the data; each item in the history is a collection (essentially a directory containing multiple individual datasets) containing one file corresponding to each of the input ligands.

Note that the SD-file needs to contain ligands with the correct 3D coordinates for MD simulation. The easiest way to obtain these is using a molecular docking tool such as Autodock Vina (Trott and Olson 2009) or rDock (Ruiz-Carmona et al. 2014); tutorials and workflows are available for both of these from the Galaxy Training Network. As an example, the history in which the SD-file used in the HTMD workflow is generated (using AutoDock Vina) is provided (Galaxy | Europe | Accessible History | Protein-ligand docking (6hhr)).

Apart from manual setups or collections, there are several other alternatives which are helpful in scaling up workflows. Galaxy supports and provides training material for converting histories to workflows, using multiple histories, and the Galaxy Application Programming Interface (API). For beginners and users who prefer a visual interface, automation can be done using multiple histories and collections with the standard Galaxy user interface.

If you are able to write small scripts, you can automate everything you have learned here with the Galaxy API. This allows you to interact with the server to automate repetitive tasks and create more complex workflows (which may have repetition or branching). The simplest way to access the API is through the Python library BioBlend (Sloggett et al. 2013). An example Python script, which uses BioBlend to run the GROMACS simulation workflow for each of a list of ligands, is given in the hands-on box below.

hands_on Hands-on: Bioblend script

from bioblend import galaxy

# Server and account details
API_KEY = 'YOUR USEGALAXY.EU API KEY'
gi = galaxy.GalaxyInstance(key=API_KEY,
   url='https://usegalaxy.eu/')

# ID for GROMACS workflow
workflow_id = 'adc6d049e9283789'

# Dataset IDs for ligands to dock
ligands = {
# ligand_name: dataset ID,
'lig1': '11ac94870d0bb33a79c5fa18b0fd3b4c',
# ...
}

# Loop over ligands, invoking workflow
for name, _id in ligands.items():
   inv = gi.workflows.invoke_workflow(
       workflow_id,
       inputs={
           '1': {'src': 'hda', 'id': _id}
       },
       history_name=f'HTMD run on {name}'
   )

Conclusion

This tutorial provides a guide on how to study protein-ligand interaction using molecular dynamics in Galaxy. Performing such analyses in Galaxy makes it straightforward to set up, schedule and run workflows, removing much of the difficulty from MD simulation. Thus, the technical barrier to performing high-throughput studies is greatly reduced. Results are structured in the form of Galaxy histories or collections, and include ready-plotted diagrams, which ensure data can be easily understood and reproduced if necessary. Apart from streamlining the process for existing MD users, this tutorial should also prove useful as a pedagogical guide for educating students or newcomers to the field.

After completing the tutorial, the user will be familiar at a basic level with a range of MD analysis techniques, and understand the steps required for a typical MD simulation. Thus, they will be equipped to apply these tools to their own problems.

keypoints Key points

  • Simulating protein-ligand systems is more complex than simply simulating protein-only systems.

  • There are a range of Galaxy tools for MD simulation (using GROMACS) and analysis.

  • Galaxy makes assembling and scaling up workflows for high-throughput MD straightforward for users.

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

References

  1. Galaxy | Europe | Accessible History | Protein-ligand docking (6hhr). https://cheminformatics.usegalaxy.eu/u/sbray/h/protein-ligand-docking-6hhr
  2. Galaxy | Europe | Accessible History | Protein-ligand HTMD simulation. https://cheminformatics.usegalaxy.eu/u/sbray/w/protein-ligand-htmd-sim
  3. Galaxy | South Africa | Accessible History | Protein-ligand HTMD analysis. https://galaxy-compchem.ilifu.ac.za/u/sbray/w/protein-ligand-htmd-sim
  4. PubChem 3-(2,4-Dihydroxyphenyl)-4-(2-fluorophenyl)-1H-1,2,4-triazole-5-thione. Library Catalog: pubchem.ncbi.nlm.nih.gov. https://pubchem.ncbi.nlm.nih.gov/compound/135508238
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  7. Pearl, L. H., and C. Prodromou, 2006 Structure and Mechanism of the Hsp90 Molecular Chaperone Machinery. Annual Review of Biochemistry 75: 271–294. 10.1146/annurev.biochem.75.103004.142738
  8. Berjanskii, M., and D. S. Wishart, 2006 NMR: prediction of protein flexibility. Nature Protocols 1: 683–688. Number: 2 Publisher: Nature Publishing Group. 10.1038/nprot.2006.108
  9. Trott, O., and A. J. Olson, 2009 AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry NA–NA. 10.1002/jcc.21334
  10. Kuzmanic, A., and B. Zagrovic, 2010 Determination of Ensemble-Average Pairwise Root Mean-Square Deviation from Experimental B-Factors. Biophysical Journal 98: 861–871. 10.1016/j.bpj.2009.11.011
  11. Michaud‐Agrawal, N., E. J. Denning, T. B. Woolf, and O. Beckstein, 2011 MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. Journal of Computational Chemistry 32: 2319–2327. 10.1002/jcc.21787 https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.21787
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  14. Ruiz-Carmona, S., D. Alvarez-Garcia, N. Foloppe, A. B. Garmendia-Doval, S. Juhos et al., 2014 rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Computational Biology 10: e1003571. 10.1371/journal.pcbi.1003571
  15. Skjærven, L., X.-Q. Yao, G. Scarabelli, and B. J. Grant, 2014 Integrating protein structural dynamics and evolutionary analysis with Bio3D. BMC Bioinformatics 15: 399. 10.1186/s12859-014-0399-6
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  18. Schopf, F. H., M. M. Biebl, and J. Buchner, 2017 The HSP90 chaperone machinery. Nature Reviews Molecular Cell Biology 18: 345–360. 10.1038/nrm.2017.20
  19. Rose, A. S., A. R. Bradley, Y. Valasatava, J. M. Duarte, A. Prlić et al., 2018 NGL viewer: web-based molecular graphics for large complexes. Bioinformatics 34: 3755–3758. 10.1093/bioinformatics/bty419
  20. Rose, A. S., A. R. Bradley, Y. Valasatava, J. M. Duarte, A. Prlić et al., 2018 NGL viewer: web-based molecular graphics for large complexes. Bioinformatics 34: 3755–3758. 10.1093/bioinformatics/bty419
  21. Schuetz, D. A., M. Bernetti, M. Bertazzo, D. Musil, H.-M. Eggenweiler et al., 2018 Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics. Journal of Chemical Information and Modeling 59: 535–549. 10.1021/acs.jcim.8b00614
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Citing this Tutorial

  1. Simon Bray, Tharindu Senapathi, Christopher Barnett, Björn Grüning, 2020 High Throughput Molecular Dynamics and Analysis (Galaxy Training Materials). /training-material/topics/computational-chemistry/tutorials/htmd-analysis/tutorial.html Online; accessed TODAY
  2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

details BibTeX

@misc{computational-chemistry-htmd-analysis,
    author = "Simon Bray and Tharindu Senapathi and Christopher Barnett and Björn Grüning",
    title = "High Throughput Molecular Dynamics and Analysis (Galaxy Training Materials)",
    year = "2020",
    month = "07",
    day = "17"
    url = "\url{/training-material/topics/computational-chemistry/tutorials/htmd-analysis/tutorial.html}",
    note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
        doi = {10.1016/j.cels.2018.05.012},
        url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
        year = 2018,
        month = {jun},
        publisher = {Elsevier {BV}},
        volume = {6},
        number = {6},
        pages = {752--758.e1},
        author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
        title = {Community-Driven Data Analysis Training for Biology},
        journal = {Cell Systems}
}
                    

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