FEP for Molecular Design

Free Energy Perturbation with Desmond for Relative Binding

Energy Prediction: 2,4-bis-anilinopyrimidine inhibitors of EphB4

Authors

Odin Kvam1,§, Derek Ogg1, Martin J. Packer1,

Daniel Robinson2

odin.kvam@astrazeneca.com 1: AstraZeneca Plc.

2: Schrodinger Inc.

§: Corresponding author

At the foundation of structure-based design

lies the direct relation between observed

structural motifs and binding affinity,

allowing rational hypothesis generation and

testing. Using free energy perturbation

theory, ligands can be mutated by alteration

of functional groups while bound to the

target protein, allowing relative binding free

energies and conformational ensembles for

protein-ligand complexes to be predicted

with unprecedented accuracy. Direct affinity

prediction enables a more rational approach

to iterative design, reducing the number of

inactive compounds made and providing a

means for rapid hypothesis testing.

Computational Setup

The EphB4 tyrosine kinase receptor is a well

characterized oncology target with a chemically

rich set of published AstraZeneca ligands3,4,

exploring solvent channel optimization across a

potency range of pIC50 4.4 to 7.4. PDB

structure 2VWZ was chosen as a consensus

structure, with missing residues inserted by

homology modeling. Ligands were aligned to

the 2VWZ binding pose and relaxed to

generate initial low-energy conformers.

A mutation graph connecting each ligand

to near neighbors within the set was generated

based on similarity in heavy atom structure,

displayed in Figure 1. Each FEP mutation was

simulated over 5 ns using the OPLS 2.1 force

field and REST enhanced sampling1,

parallelized across four GK110 GPUs.

Resulting relative energies were used to

calculate consensus ΔΔG values based on a

maximum likelihood approach1.

Predictive Power

The accuracy of FEP for relative binding

energy prediction was assessed for the full set

of ligands by comparison with experimental

IC50 data, assuming [LA] ≈ [LB] and Km >> [L].

For a mutation from ligand A to ligand B,

relative free energy can be estimated as

A sample conformational ensemble for

3’-sulfonamide is displayed in Figure 2, while

predicted versus experimental binding energies

are presented in Figure 3. Predicted relative

free energies strongly correlate with

experimental values, showing a MUE (mean

unsigned error) of 0.5 kcal/mol. This level of

accuracy is unprecedented for a test set this

large and chemically diverse, and compares

very favorably with reported ligand solvation

free energies predicted using the OPLS 2.0

force field (MUE 0.7 kcal/mol2) as well as

typical experimental uncertainties, estimated as

MUE 0.5-1.0 kcal/mol5.

With comparable predictive power to

typical experimental biochemical assays, FEP

is ripe for application in ligand design at lead

generation and optimization stages. Typical

observed simulation times for FEP mutations

are 12-24 hours/ligand.

⇌

Figure 2 Binding conformations for a 3’-sulfonamide in EphB4 generated as part of the FEP simulation with

REST, overlaid with experimental Fo – Fc electron density for the ligand contoured at 1σ confidence. The dual

occupancy binding mode of this ligand is captured, with the aniline ring rotating between initial binding pose

(right) and alternative binding pose (left).

y = 0.9688x + 0.4316 R² = 0.7025

-5.

-3.

-1.

1.

3.

5.

-5. -4. -3. -2. -1. 0. 1. 2. 3.

ΔΔ

GF

EP

[k

ca

l/m

ol]

Δ RT ln( IC50 ) [kcal/mol]

Figure 3 Predicted relative free energy (ΔΔG) by FEP

plotted against experimental free energy based on

IC50 measurements. MUE for the anilinopyrimidine

ligand set is 0.5 kcal/mol, RMSE is 1.1 kcal/mol.

Sulfonamide and morpholine compounds were

identified as frequent outliers.

References

1. Wang, L., Deng, Y., Knight, J. L. Wu, Y., Kim, B., Sherman, W., Shelley, J. C., Lin, T., Abel, R. (2013) Modeling local structural rearrangements using FEP/REST: Application to relative binding affinity predictions of CDK2

inhibitors. J. Chem. Theory Comput., 9, 1282-1293.

2. Shivakumar, D.; Harder, E.; Damm, W.; Friesner, R. A.; Sherman, W. (2012) Improving the prediction of absolute solvation free energies using the next generation OPLS force field. J. Chem. Theory Comput., 8, 2553-2558.

3. Bardelle, C.; Cross, D.; Davenport, S.; Kettle, J. G.; Ko, E. J.; Leach, A. G.; Mortlock, A.; Read, J.; Roberts, N. J.; Robins, P.; Williams, E. J. (2008) Inhibitors of the tyrosine kinase EphB4. Part 1: Structure-based design and

optimization of a series of 2,4-bis-anilinopyrimidines. Bioorg. Med. Chem. Lett., 18, 2776-2780.

4. Bardelle, C.; Coleman, T.; Cross, D.; Davenport, S.; Kettle, J. G.; Ko, E. J.; Leach, A. G.; Mortlock, A.; Read, J.; Roberts, N. J.; Robins, P.; Williams, E. J. (2008) Inhibitors of the tyrosine kinase EphB4. Part 2: Structure-based

discovery and optimisation of 3,5-bis substituted anilinopyrimidines. Bioorg. Med. Chem. Lett., 18, 5717-5721

5. Wang, L.; Berne, B. J.; Friesner, R. A. (2012) On achieving high accuracy and reliability in the calculation of relative protein-ligand binding affinities. PNAS, 109(6) 1937-1942.

Figure 1 Relation map for free energy calculations

within the anilinopyrimidine ligand set. Nodes

represent ligands and directed edges individual FEP

simulations. Each ligand is connected by two or

more edges, with average connectivity 3.5 and a

total of 67 edges. The central ligand (yellow) was

chosen as reference.