Induced-Fit Posing (Confined) [IFP]

Category Paths

Follow one of these paths in the Orion user interface, to find the floe.

  • Product-based/OEDocking

  • Product-based/Molecular Dynamics/GROMACS

  • Product-based/Molecular Dynamics/OpenMM

  • Role-based/Computational Chemist

  • Role-based/Medicinal Chemist

  • Solution-based/Hit to Lead

  • Task-based/Ligand Posing & Analysis


  • Purpose:

    • This Floe performs Induced-Fit Posing (IFP). Given a reference receptor and a ligand to dock, it carries out the following three steps:

      1. OEDocking and initial processing

      1. OMEGA conformer ensemble generation of the ligand to be posed

      2. Pruning of sidechains in the receptor binding site

      3. OEDocking followed by initial scoring to select the top 12 pose/receptor conformations

      1. MD for induced-fit between the receptor and the posed ligand

      2. Final scoring of the induced-fit pose/receptor conformations

      We refer to the induced-fit process as confined due to its limited scope. The output datasets can be used as input datasets for other MD Floes for further sampling, etc.

  • Input Requirements:

    • The reference receptor needs to have a unique OEReceptor and needs to be prepared to MD standards: protein chains must be capped, all atoms in protein residues (including hydrogens) must be present, and missing protein loops resolved or capped. Typically, SPRUCE is used for protein preparation.

  • Limitations:

    • The input reference protein dataset must contain a bound ligand (An apo state of the reference is not supported in this Floe). The bound ligand of the reference protein will not appear in the final results but will be used to guide the initial docking of the compound provided in the docking ligand input dataset.

    • The Floe takes a single reference receptor and a single docking ligand at a time.

    • Limitations of the current implementation: (1) the induced-fit procedure may not sufficiently reorganize protein secondary structure, tertiary structure, or loop regions where backbone rearrangement is necessary; (2) reference proteins with small outer contours relative to the docking ligand may suffer during docking. One can manually extend the outer contours of the receptor using the MakeReceptor Application before running the IFP Floe.

  • Expertise Level:

    • Intermediate/Advanced

  • Compute Resource:

    • Depends on the number of pose/receptor conformations advanced to the MD-aided induced-fit, and the length of MD simulations.

  • Keywords:

    • IFP, Induced-Fit Posing, Induced Fit Posing, IFD, Induced-Fit Docking, Induced Fit Docking, MD, Molecular Dynamics

Promoted Parameters

Title in user interface (promoted name)

OEDocking settings

Posit Method (posit_method): Posit method to use

  • Type: string

  • Default: Hybrid

  • Choices: [‘FRED’, ‘Hybrid’, ‘ShapeFit’, ‘All’]

Number of poses (num_poses): Number of poses to generate per ligand

  • Type: integer

  • Default: 50

Additional POSIT Switch (additional_posit_switch): If True, it will run an additional POSIT with a different docking method

  • Required

  • Type: boolean

  • Default: True

  • Choices: [True, False]

Additional OEDocking settings

Posit Method (posit_method2): Posit method to use

  • Type: string

  • Default: FRED

  • Choices: [‘FRED’, ‘Hybrid’, ‘ShapeFit’, ‘All’]

Number of poses (num_poses2): Number of poses to generate per ligand

  • Type: integer

  • Default: 50

Optional Inputs

Target Receptor Input Dataset for retrospective study (ref_in): Optional target receptor with the known bound ligand for comparison to IFP outputs. For later visual inspection, it is recommended to align the reference receptor and the target receptor during receptor preparation.

  • Type: data_source

Optional Pruned Reference Receptor Input Dataset (mut_receptor_in): Optional pruned reference receptor input dataset

  • Type: data_source

MD Setup Parameters

Ligand Force Field (ligand_ff): Force field to be applied to the ligand. The ‘OpenFF_2.0.0’, ‘OpenFF_1.3.1’, and ‘Custom’ force fields may be augmented with bespoke force field parameters by turning on ‘Use Bespoke Parameters When Available’ and providing SMIRNOFF format parameters on the input record.

  • Required

  • Type: string

  • Default: OpenFF_2.0.0

  • Choices: [‘Gaff_1.81’, ‘Gaff_2.11’, ‘OpenFF_1.1.1’, ‘OpenFF_1.2.1’, ‘OpenFF_1.3.1’, ‘OpenFF_2.0.0’, ‘Smirnoff99Frosst’, ‘Custom’]

Custom Ligand Force Field File (custom_offxml_file_in): One or more SMIRNOFF XML files defining the force field to be applied to the ligand. This input is required when ‘Ligand Force Field’ is set to ‘Custom’.

  • Type: file_in

Protein Force Field (protein_ff): Force field to be applied to the protein.

  • Required

  • Type: string

  • Default: Amber14SB

  • Choices: [‘Amber14SB’, ‘Amber99SB’, ‘Amber99SBildn’, ‘AmberFB15’]

MD Engine (md_engine): Select the available MD engine

  • Type: string

  • Default: OpenMM

  • Choices: [‘OpenMM’, ‘Gromacs’]

Hydrogen Mass Repartitioning (HMR): Give hydrogens more mass to speed up the MD

  • Type: boolean

  • Default: True

  • Choices: [True, False]

NPT Production Runtime (prod_ns): NPT simulation production time in nanoseconds

  • Type: decimal

  • Default: 2

Trajectory Interval (prod_trajectory_interval): Trajectory saving interval in nanoseconds

  • Type: decimal

  • Default: 0.004

CPU GPU Spot Policy Selection

CPUs (cpu_count_md): The number of CPUs to run this cube with

  • Type: integer

  • Default: 16

GPUs (gpu_count_md): The number of GPUs to run this cube with

  • Type: integer

  • Default: 1

Spot policy (spot_policy_md): Control cube placement on spot market instances

  • Type: string

  • Default: Allowed

  • Choices: [‘Allowed’, ‘Preferred’, ‘NotPreferred’, ‘Prohibited’, ‘Required’]