Adaptive PELE parameters

These are parameters that affect adaptive PELE. Thus, they affect the way how the phase space of the system is explored.

List of adaptive PELE parameters:

List of examples:

Warning

Note that these parameters will not affect fragPELE.

iterations

  • Description: Adaptive iterations to run. When they are set to 1, Adaptive PELE will be disabled and the simulation will run with no exploration bias.

  • 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

Adaptive iterations are also referred to as epochs.

Note

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

See also

steps, packages, Example 1

spawning

  • Description: It defines the method to spawn new structures every time a new Adaptive iteration starts. There are 3 options available:

    • independent: Trajectories are run independently, as in the original PELE. It may be useful to restart simulations.

    • inverselyProportional: Distributes the processors with a weight that is inversely proportional to the cluster population.

    • epsilon: An epsilon fraction of processors are distributed proportionally to the value of a metric, and the rest are inverselyProportional distributed. This fraction must be defined with an additional parameter called epsilon. The metric of interest is defined with a parameter called bias_column. The bias towards the metric of interest depends on the spawning_condition parameter.

  • Type: String

  • Default: inverselyProportional

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.

See also

bias_column, epsilon, spawning_condition, cluster_conditions, packages, Example 3

adaptive_restart

  • Description: When this parameter is set to True, it will try to continue a previous simulation that might have been interrupted. Thus, it will start from the last Adaptive iteration that it finds in the working directory until the requested number of iterations is achieved.

  • Type: Boolean

  • Default: True

Note

This parameter must not be confused with restart. While restart stands for skipping any input file preparation and directly going to the simulation execution, it still can start from the first Adaptive iteration if adaptive_restart is set to False.

See also

restart, Example 4

bias_column

  • Description: Column in PELE report files that contains the metric of interest for Adaptive’s bias. Counter starts from 1.

  • Type: Integer

  • Default: it depends on the package

Note

This parameter will only be effective if spawning is set to epsilon.

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.

See also

spawning, epsilon, spawning_condition, packages, Example 3

epsilon

  • Description: The fraction of the processors that will be assigned according to the selected metric when spawning method is set to epsilon. It is a value between 0 and 1. The larger, the more bias will be applied to the metric of interest.

  • Type: Float

  • Default: it depends on the package

Note

This parameter will only be effective if spawning is set to epsilon.

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.

See also

spawning, bias_column, spawning_condition, packages, Example 3

spawning_condition

  • Description: Defines how the bias towards the metric of interest is applied, i.e. whether it should promote clusters that minimize or maximize the metric of interest. There are 2 options available:

    • max

    • min

  • Type: String

  • Default: it depends on the package

Note

This parameter will only be effective if spawning is set to epsilon.

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.

See also

spawning, bias_column, epsilon, packages, Example 3

cluster_conditions

  • Description: Defines the clustering parameters that Adaptive will employ to dicretize with structural clusters the conformational space of the ligand. The general strategy is to set up larger clusters when the ligand has few contacts with the protein and reduce their size when protein-ligand contacts increase as we want to capture this region with more detail. Thus, it represents an array of contacts from high to low between the ligand and the protein. It is related to cluster_values and the length of the cluster_conditions array must be equal to the length of cluster_values minus one.

    This parameter can be set to auto to automatically select the right clustering conditions. In this case, the Platform runs a preliminary step called pre-equilibration to capture the protein-ligand contacts for each particular case.

  • Type: List[Float] or String

  • 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

Do not confuse equilibration with pre-equilibration. The former consists in 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

cluster_values, packages, packages, Example 1, Example 2

cluster_values

  • Description: Defines the clustering parameters that Adaptive will employ to dicretize with structural clusters the conformational space of the ligand. The general strategy is to set up larger clusters when the ligand has few contacts with the protein and reduce their size when protein-ligand contacts increase as we want to capture this region with more detail. Thus, it represents the size of each cluster, from low to high, that corresponds with the conditions defined in the cluster_conditions parameter. Higher clustering values mean larger structural clusters.

  • Type: List[Float]

  • 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.

See also

cluster_conditions, packages Example 1, Example 2

Example 1

In this example we set an induced fit docking simulation with 30 computation cores. We then replace the default number of Adaptive iterations of the induced fit docking package. Instead of 25 iterations we ask for 10. This will result in an even faster simulation at the expense of reducing the exploration.

On the other hand, we are also specifying custom parameters for Adaptive’s clustering. We slightly reduce the sizes of clusters with the cluster_values parameter (defaults for the induced fit fast package are "[2.0, 5.0, 7.0]"). We also set cluster_conditions to "auto", so the Platform will run a few pre-equilibration steps to determine the best cluster conditions.

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

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# Adaptive parameters
iterations: 10
cluster_values: "[2.0, 4.0, 6.0]"
cluster_conditions: "auto"

Example 2

In this example we set an induced fit docking simulation with 30 computation cores. We specify custom parameters for Adaptive’s clustering. We slightly reduce the sizes of clusters with the cluster_values parameter (defaults for the induced fit package are "[2.0, 5.0, 7.0]"). We also set cluster_conditions to "[1.5, 0.8]", assuming that our ligand is able to perform more contacts than those seen in common scenarios. For example, these conditions might work better in cases where our ligand is highly buried in a protein cavity. Default cluster conditions for the induced fit package are "[1.0, 0.6]".

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

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# Adaptive parameters
cluster_values: "[2.0, 4.0, 6.0]"
cluster_conditions: "[1.5, 0.8]"

Example 3

In this example we set an out –> in simulation with 50 computation cores. When using this package, we also need to set initial and final sites in order to properly define the starting point and the region to explore during the migration of our ligand. Check perturbation site parameters to get further information about these two options.

Then, we replace the default Adaptive spawning method of out –> in package, which is inverselyProportional, to epsilon. Thus, Adaptive will apply a certain bias towards one metric. Specifically, the portion of bias that will be used is 0.20, as defined with the epsilon parameter. Moreover, the metric of interest to track is the one in the 7th column of PELE’s reports files, which, in this case, corresponds to the distance between the center of mass of the ligand and the chosen final site. When setting spawning_condition to min, we ask Adaptive to apply a bit of bias towards those structures that reduce this distance, thereby promoting the entrance of the ligand to the cavity we specified.

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

# General parameters
cpus: 50
seed: 2021

# Package selection
out_in: True

# Region selection
initial_site: "A:352:CD"
final_site: "A:283:ND2"

# Adaptive parameters
bias_column: 7
spawning: "epsilon"
epsilon: 0.20
spawning_condition: "min"

Example 4

In this example we set an induced fit docking simulation with 30 computation cores. We are disabling Adaptive restart, so in case we apply a restart, the simulation will start from scratch, removing any Adaptive iteration that might have been completed in a previous run.

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

# General parameters
cpus: 30
seed: 2021

# Package selection
induced_fit_fast: True

# Adaptive parameters
adaptive_restart: False