What’s in the Lab?Dr Mark Mackey

CSO, Cresset

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Free Energy Perturbation (FEP)

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> Evaluation via Thermodynamic Integration (TI) or Multistate Bennett Acceptance Ratio (MBAR)

Thermodynamic cycle

Cl

N+

O

N

O

N

N+

O

N

O

N

Shirts, M. et al.,J. Chem. Phys. 2008, 129, 124105

λ = 0

λ = 1

∆Gbind, A

∆Gbind, B

∆Gsolvated

∆𝐺𝐺𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏,𝐴𝐴−𝐵𝐵= ∆𝐺𝐺𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 − ∆𝐺𝐺𝑠𝑠𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑏𝑏

∆Gbound

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Example: Two ligands in PTP1B

> Number of intermediate states depends on how difficult is the modification from A to B> Need to ensure that any adjacent pair of systems are similar enough

A 10% C90% B

90% A10% C

90% A10% C BC 90% C

10% B

λ = 0

λ = 1

λ = 0.5

λ = 1

λ = 0

λ = 0.5

A 5% A95% B

90% A10% B

95% A5% B B

λ =0 λ =0.05 λ =0.1 λ =0.95 λ =1

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> Collaboration with Julien Michel at University of Edinburgh

> Building on top of best open-source software> AmberTools> OpenMM> Improved and extended LOMAP> SIRE> BioSimSpace

> Combines them with Cresset expertise in delivering easy-to-use software

FEP at Cresset

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FEP workflow in Flare

Assessment of ∆∆G

Production run (SOMD using OpenMM as the MD engine, 4.0 ns each)

Generation of SOMD input files (BioSimSpace)

Multi-step equilibration (OpenMM, 0.25ns each)

Generation of input files (AmberTools, OFF toolkit)

Generation of perturbation network (LOMAP and/or manually)

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Results of Flare final implementation on standard data sets

DatasetCresset/ UoE Schrödinger

(Wang et al.)AMBER TI

(Song et al.)GROMACS

(Gapsys et al.), GAFFR2 (95%CI) MUE R2 (95%CI) MUE R2 (95%CI) MUE R2 (95%CI) MUE

Thrombin 0.82 (0.67-0.91) 0.20 ± 0.05 0.50

(0.23-0.71) 0.42 ± 0.11 0.57(0.31-0.76) 0.37 ± 0.11 0.06

(0.01-0.29) 0.66 ± 0.11

TYK2 0.53(0.32-0.69) 0.70 ± 0.14 0.79

(0.67-0.88) 0.44 ± 0.09 0.33(0.13-0.53) 0.89 ± 0.18 0.47

(0.26-0.65) 0.87 ± 0.11

JNK1 0.52(0.35-0.67) 0.76 ± 0.13 0.71

(0.58-0.81) 1.05 ± 0.10 0.22(0.07-0.40) 0.90 ± 0.15 0.64

(0.49-0.76) 0.80 ± 0.12

CDK2 0.54(0.34-0.71) 0.69 ± 0.14 0.23

(0.06-0.44) 0.88 ± 0.14 0.22(0.07-0.40) 0.91 ± 0.15 0.72

(0.56-0.83) 0.47 ± 0.12

MCL1 0.55(0.43-0.65) 1.18 ± 0.15 0.60

(0.49-0.69) 0.84 ± 0.10 0.42(0.30-0.54) 1.24 ± 0.12 0.59

(0.48-0.68) 0.86 ± 0.11

p38 0.40(0.26-0.53) 0.94 ± 0.14 0.42

(0.29-0.55) 0.86 ± 0.08 0.15(0.05-0.28) 1.28 ± 0.18 0.46

(0.33-0.59) 0.62 ± 0.08

BACE 0.19(0.08-0.32) 0.76 ± 0.10 0.61

(0.50-0.71) 0.67 ± 0.09 0.19(0.08-0.32) 1.03 ± 0.14 0.21

(0.09-0.34) 0.74 ± 0.08

PTP1B 0.48(0.31-0.63) 0.72 ± 0.13 0.65

(0.50-0.76) 0.60 ± 0.11 0.50(0.33-0.64) 0.76 ± 0.14 0.52

(0.35-0.66) 0.77 ± 0.10

MUE = per compound MUE [kcal/mol]

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Results of Flare final implementation on standard data sets

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Results of Flare final implementation on standard data sets

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Limitations

> Ring size changes

> Aromaticity changes

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Link direction

> In default mode, we compute both A→B and B→A> For fast mode, we only do one of the two. Which one?

Target R², all R², S2L R², L2S MUEc, all MUEc, S2L MUEc, L2S

Thrombin 0.82 0.91 0.27 0.20 0.14 0.40CDK2 0.54 0.53 0.52 0.69 0.70 0.70TYK2 0.53 0.19 0.51 0.70 1.10 0.72JNK1 0.52 0.49 0.54 0.76 0.77 0.84

PTP1b 0.48 0.49 0.11 0.72 0.75 1.10MCL1 0.55 0.51 0.57 1.18 1.24 1.14BACE 0.19 0.12 0.25 0.76 0.80 0.74

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> How reproducible are FEP calculations?

> Repeated several transformations multiple times to get stats> Cl ↔ Br> O ↔ CH2

> H ↔ cyclopropyl> H ↔ cycloheptyl

> Unsurprisingly, variance is larger for larger transformations

Repeatability of calculations

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AI charging models

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> DNN-based method for computing electrostatic potentials> Builds an underlying charge model that

is similar to XED

> Can we utilise this to replace/augment XED charging?

Improved charge models for field-based similarity

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Field patterns and surface potentials are similar

Additional charge points on Cl giveneutral electrostatics along bond axis XED polarizes the system slightly more

Astex AstexXED XED

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> ‘Proper’ electrostatics on cations/anions are useless

> Heavily influenced by (unknown) protein environment

> Heuristic: Scale formal charges down (equivalent to a higher dielectric)> XED uses 0.125 or 0.25> Astex use 0.4

> Issues with unrealistic charge distributions – not solved yet

Charged systems are an issue for both methods

Astex XED

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> 119 proteins> ~1450 ligands> Generate unbiased confs for each

ligand, align using every other ligand as a reference

> Preliminary numbers (chirality problems)

Tested on AZ/CCDC alignment test data set

Formal charge scale factor 0.4

Formal charge scale factor 0.25

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Future research plans

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Continue Astex charge model investigations

> Does the Astex charge model improve electrostatic complementarity calculations?> Does it give sensible results for proteins?

> Is it worth re-training the model on the XED geometries?

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> HPC-Europa 3 grant> Hosted by Dr. Piero Procacci of the

Department of Chemistry, University of Florence

> 250K hours of GPU time on MARCONI100 cluster at CINECA

> 980 nodes, 4 x Tesla V100 cards per node> Difficulty is the PowerPC architecture…

FEP experiments

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> Running several large-scale experiments> What is the reproducibility of FEP calculations?> Can we speed the calculations up without sacrificing accuracy?

> Water box buffer size> Hydrogen mass repartitioning> Improved OpenMM integrator

> Improved handling of ‘difficult’ transformations> Split steric/electronic perturbations

> Adaptive lambda schedules

FEP experiments

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More

Docking in Spark (see Giovanna’s talk)

GIST / GCT as alternative water analysis methods

Improvements to ligand alignment scoring function

Improvements to docking algorithm (protein flexibility, waters)

Very high-throughput (billions) 3D virtual

screening of libraries

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cressetgroup

mark@cresset-group.com

Mark Mackey

Thanks to Paolo Tosco, Giovanna Tedesco, Stuart Firth-Clark, Nathan Kidley, Peter Cherry, Max Kuhnand Julien Michel, Antonia Mey at the University of Edinburgh