Non-Equilibrium Switching [MDRun] [FECalc]

Category Paths

  • Product-based/Molecular Dynamics/GROMACS

  • Role-based/Computational Chemist

  • Role-based/Medicinal Chemist

  • Task-based/Molecular Dynamics

  • Task-based/Affinity Prediction

  • Solution-based/Hit to Lead/Affinity Prediction/Free-Energy Calculations

  • Solution-based/Small Molecule Lead-opt/Affinity

Description

  • Purpose:

    • This Floe performs relative binding free energy (RBFE) calculations using nonequilibrium switching (NES) method refined by the de Groot lab (Gapsys et al., Chem. Sci., 2020, 11, 1140-1152).

  • Method Recommendations/Requirements:

    • Four inputs are required:

      • An Orion dataset containing an equilibrium run for each bound ligand participating in the NES run.

      • An Orion dataset containing an equilibrium run for each unbound ligand participating in the NES run.

      • A Orion Dataset containing the ligand edges to run the RBFE calculation generated by using the Mapper Floe

      • [Optional] a text file containing experimental binding free energies for at least one ligand, one experimental datapoint per line, of form “ligA_name {deltaG(exptl)} {error_deltaG(exptl)} {units}” for example, “gn1c -8.56 0.17 kcal/mol”.

  • Limitations

    • If no experimental binding free energies (the fourth input above) are given, the estimation of ligand binding free energies has no reference value so the relative values will be centered around the mean.

    • Currently there is no mitigation for the effects of changes in buried waters, protein sidechain flips, or large protein movements between ligA and ligB.

  • Expertise Level:

    • Regular

  • Compute Resource:

    • High

  • Keywords:

    • MD, FECalc

  • Related Floes:

    • Ligand Bound and Unbound Equilibration for NES [MDPrep] [MD]

    • Equilibration and Non-Equilibrium Switching [MDPrep] [MD] [FECalc]

    • Compare Experimental Affinity with NES Results [Utility] [FECalc]

    • Non-Equilibrium Switching Recovery [Utility] [FECalc]

The Floe will draw a number of starting snapshots from the bound and unbound trajectories of the ligands. Then for each edge in the edge file, it will generate an RBFE alchemical transformation from ligA into ligB, and carry out the NES fast transformation of ligA into ligB, and vice versa, for each of the snapshots. The resulting relative free energy change, or DeltaDeltaG, for each edge is the primary output of this method. A maximum likelihood estimator is then used to derive a predicted binding affinity (free energy, or DeltaG) for each ligand. The mean value of the input experimental binding free energies is used as the reference value for the computed ones.

The speed of the NES transformation and the number of snapshots transformed can be adjusted from default values by the user at runtime. The Floe outputs two Floe report/dataset pairs, one for the calculated RBFE edges (DeltaDeltaGs), and one for the derived affinity predictions (DeltaGs) of ligand.

Promoted Parameters

Title in user interface (promoted name)

CPU GPU Spot Policy Selection

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

  • Type: integer

  • Default: 16

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

  • Type: integer

  • Default: 1

AWS Spot Instances For MD Cubes (spot_policy_md): Control cube placement on spot market instances

  • Type: string

  • Default: Required

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