PELE general parameters

These are parameters that affect the general behaviour of PELE.

List of PELE general parameters:

List of examples:

Warning

Note that these parameters will not affect fragPELE.

steps

  • Description: The number of PELE steps to perform in each PELE iteration. Thus, this is the exact number of steps that each PELE explorer will execute and, as a result, all trajectories will have the same length.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter is set according to the Platform package that is chosen since it has a strong connection with the type of simulation that is pursued. However, if this parameter is set, it will prevail over the default settings of any package.

Note

Usually, the right number of PELE steps also depends on the number of Adaptive iterations that are set.

See also

iterations, minimum_steps, packages, PELE basis, Example 1, Example 2

minimum_steps

  • Description: When this parameter is set to True, each PELE explorer will continue running PELE steps until all of them have reached the requested number of steps. As a result, those explorers that are faster might end up performing more steps than the minimum threshold.

  • Type: Boolean

  • Default: False

Note

This strategy allows for more efficient exploration, since the cores that already reached the required number of steps do not wait idly but continue the computation until all explorers have finished. However, it will produce an unbalanced sampling since some explorers might produce larger trajectories due to the fact that we force a minimum of steps to be executed but not a maximum.

See also

steps, PELE basis, Example 2

equilibration

  • Description: When set to true, it will equilibrate the system and generate multiple starting poses. This strategy reduces the bias towards the initial ligand position since the production run starts from the different poses that are obtained during the equilibration.

  • Type: Boolean

  • Default: False

Note

Do not confuse equilibration with pre-equilibration. The former entails running several equilibration steps to produce different initial structures. The latter only checks the amount of contacts between the ligand and the protein to correctly set the right clustering conditions for Adaptive.

See also

equilibration_steps, cluster_conditions, Example 1

equilibration_steps

  • Description: The number of PELE steps to perform during the equilibration stage.

  • Type: Integer

  • Default: 2

Note

This parameter will only be effective if equilibration is activated.

See also

equilibration, Example 1

temperature

  • Description: The temperature in Kelvin to be used in the Metropolis criterion of PELE.

  • Type: Float

  • Default: 1500

See also

PELE basis, Example 3

anm_freq

  • Description: The frequency for the ANM algorithm of PELE. For example, a frequency of 1 means that it will run at every PELE step, and a frequency of 2 means running every 2 steps. Thus, increasing the frequency of the ANM algorithm will reduce the protein perturbation but the simulation will run faster.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter is set according to the Platform package that is chosen since it has a strong connection with the type of simulation that is pursued. However, if this parameter is set, it will prevail over the default settings of any package.

Note

Setting a frequency of 0 completely disables the ANM algorithm.

See also

PELE basis, Example 3

sidechain_freq

  • Description: The frequency for the side chain prediction algorithm of PELE. For example, a frequency of 1 means that it will run at every PELE step, and a frequency of 2 means running every 2 steps. Thus, increasing the frequency of the side chain prediction algorithm will reduce the side chain relaxation but the simulation will run faster.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter is set according to the Platform package that is chosen since it has a strong connection with the type of simulation that is pursued. However, if this parameter is set, it will prevail over the default settings of any package.

Note

Setting a frequency of 0 completely disables the side chain prediction algorithm.

See also

PELE basis, Example 3

min_freq

  • Description: The frequency for the minimization algorithm of PELE. For example, a frequency of 1 means that it will run at every PELE step, and a frequency of 2 means running every 2 steps. Thus, increasing the frequency of the minimization algorithm will reduce the acceptance ratio of the Metropolis criterion but the simulation will run faster.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter is set according to the Platform package that is chosen since it has a strong connection with the type of simulation that is pursued. However, if this parameter is set, it will prevail over the default settings of any package.

Note

Setting a frequency of 0 completely disables the minimization algorithm.

See also

PELE basis, Example 3

water_freq

  • Description: The frequency for the aquaPELE algorithm of PELE. For example, a frequency of 1 means that it will run at every PELE step, and a frequency of 2 means running every 2 steps. Thus, increasing the frequency of aquaPELE algorithm will reduce water sampling but the simulation will run faster.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter is set according to the Platform package that is chosen since it has a strong connection with the type of simulation that is pursued. However, if this parameter is set, it will prevail over the default settings of any package.

Note

Note that aquaPELE is enabled only when we set some water molecules to be perturbed. Refer to water parameters in order to get further information about how to set up aquaPELE.

Note

Setting a frequency of 0 completely disables the aquaPELE algorithm.

See also

aquaPELE parameters, Example 4

conformation_freq

  • Description: The frequency for the conformation perturbation algorithm of PELE. For example, a frequency of 1 means that it will run at every PELE step, and a frequency of 2 means running every 2 steps. Thus, increasing the frequency of conformation perturbation will promote the sampling of the different ligand conformations but the acceptance ratio of PELE steps might significantly decrease.

  • Type: Integer

  • Default: 4

Note

Note that conformation perturbation is enabled only when we set the ligand_conformations parameter. Refer to ligand parameters in order to get further information about how to set it up.

Note

Setting a frequency of 0 completely disables the conformation perturbation algorithm.

See also

ligand_conformations, Example 5

forcefield

  • Description: The force field to use during the PELE simulation. There are several options available:

    • OPLS2005

    • openff-2.0.0

    • openff-1.3.0

    • openff-1.2.1

    • openff-1.2.0

    • openff-1.1.1

    • openff-1.1.0

    • openff-1.0.1

    • openff-1.0.0

  • Type: String

  • Default: OPLS2005

Warning

Selecting any OpenFF force field requires the use of peleffy to parametrize non standard residues. Currently, peleffy is not the default parametrization tool. To know how to enable it, check parametrization options.

Note

Using any OpenFF force field implies modeling protein residues with OPLS2005 and non standard residues with OpenFF.

See also

use_peleffy, Example 6

solvent

  • Description: The implicit solvent to use during the PELE simulation. There are 2 options available:

    • VDGBNP

    • OBC

  • Type: String

  • Default: VDGBNP when using OPLS2005 forcefield, OBC when using any OpenFF force field

Warning

Note that the only implicit solvent compatible with OpenFF is OBC.

See also

forcefield, Example 7

Example 1

In this example we set an induced fit docking simulation with 30 computation cores. We then replace the default number of PELE steps of the induced fit docking package. Instead of 12 steps we ask for 6. This will result in an even faster simulation (twice as fast) at the expense of reducing the exploration.

We are also enabling the equilibration. Thus, prior to the production run we will run a few steps to obtain different starting positions of our ligand. The number of PELE steps that will be devoted to the equilibration is set to 5, replacing the default value of 2 equilibration steps.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# PELE parameters
steps: 6
equilibration: True
equilibration_steps: 5

Example 2

In this example we set an induced fit docking simulation with 30 computation cores. We then replace the default number of PELE steps of the induced fit docking package. Instead of 12 steps we ask for 5. Moreover, we activate the minimum_steps mode which will transform the number of steps into a minimum threshold. Thus, we are forcing all explorers to perform a minimum of 5 steps but we will not block them once they finish the 5th step. Instead, they will be able to continue executing more steps until all independent explorers achieve the minimum threshold of 5. This strategy allows those explorers that run faster to generate more steps, thereby increasing the overall performance of PELE.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# PELE parameters
steps: 5
minimum_steps: True

Example 3

In this example we set an induced fit docking simulation with 30 computation cores. We then replace the default frequencies of the internal PELE algorithms. Specifically, we are completely disabling the ANM algorithm, we are ensuring that the side chain prediction runs at every PELE step and we are minimizing the system every 2 steps. Finally, we are also changing the default temperature of the Metropolis criterion, instead of 1500, we set 2000, so the acceptance probability increases.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# PELE parameters
anm_freq: 0
sidechain_freq: 1
min_freq: 2
temperature: 2000

Example 4

In this example we set an induced fit docking simulation with 30 computation cores. We also activate aquaPELE by adding 2 new water molecules with n_waters parameter. Finally, we set the frequency at which aquaPELE runs with water_freq option. Thus, it runs every 2 PELE steps.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# aquaPELE parameters
n_waters: 2

# PELE parameters
water_freq: 2

Example 5

In this example we set an induced fit docking simulation with 30 computation cores. We also provide a path that contains different conformations of our ligand with the ligand_conformations parameter. This option will activate the Conformation perturbation algorithm that adds an extra perturbation step to visit all supplied ligand conformations during the PELE simulation. However, to diminish the effects of the Conformation perturbation algorithm, we reduce its frequency from a default of 4 to 6. This strategy modification will help to prevent the Metropolis acceptance ratio from dropping too much.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# Ligand parameters
ligand_conformations: "LIG_conformations/"

# PELE parameters
conformation_freq: 6

Example 6

In this example we set an induced fit docking simulation with 30 computation cores. We also select the latest OpenFF force field, which works with the OBC solvent model. In order to use it, we need to activate peleffy. Check parametrization section to get further details.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# Parametrization parameters
use_peleffy: True

# PELE parameters
forcefield: "openff-2.0.0"

Example 7

In this example we set an induced fit docking simulation with 30 computation cores. We also change the implicit solvent model. The default solvent when using OPLS2005 is VDGBNP. However, we can also use OBC if we select it with the solvent parameter.

# Required parameters
system: 'system.pdb'
chain: 'L'
resname: 'LIG'

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# PELE parameters
solvent: "OBC"