Peeking into the heart of new Cresset science · 2019-06-25 · Protein interaction potentials...

Peeking into the heart of new Cresset scienceMark Mackey

© CressetCONFIDENTIAL

Overview

> New science in Flare

> Protein interaction potentials

> 3D-RISM

> WaterSwap

> Lead Finder

> Ongoing collaborations


Protein interaction potentials


Protein interaction potentials

Reveal the hidden

electrostatic character of

protein binding sites

Compare related

proteins to identify

selectivity opportunities

Understand SAR trends and ligand

binding from the protein’s perspective

Inform ligand design

through use of protein

electrostatics


> Basic idea is the same as ligand

field

> Field value is the computed interaction

energy with a charged probe, using

XED

> Field surfaces found to be more useful

than field points

> XED force field electrostatics

emphasizes signal from aromatic

residues

> Visual method at this stage – no

scoring

> Challenges

> Solvation

> Use modified dielectric function

> Screened Coulombic Potential model of

Mehler et al.

Protein interaction potentials - Challenges

Mehler, E. L., in “Molecular Electrostatic Potentials:

Concepts and Applications” pp371-405


SCP model

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12 14 16 18 20

D(r

)

r (Å)

SCP dielectric function

0.1

1

10

100

1000

0 2 4 6 8 10 12 14 16 18 20

E/q

q (

kcal/m

ol)

r (Å)

Coloumbic energy

D(r) D=1 D=78


> Basic idea is the same as ligand

field

> Field value is the computed interaction

energy with a charged probe, using

XED

> Field surfaces found to be more useful

than field points

> XED force field electrostatics

emphasizes signal from aromatic

residues

> Visual method at this stage – no

scoring

> Challenges

> Solvation

> Use modified dielectric function

> Screened Coulombic Potential model of

Mehler et al.

> Protein Prep

> PIPs are highly sensitive to where the

hydrogens are!

> Build Model (BioMolTech)

Protein interaction potentials - Challenges


PIP example – 4M7I (PERK)

= Positive = Negative

Protein Electrostatics - Dry

Contour 3 Kcal/mol

Ligand Electrostatics


Adding crystal water to the calculation of protein electrostatics

Contour 3 Kcal/mol

Protein Electrostatics – with crystallographic water Ligand Electrostatics


> TSAR algorithm (thermodynamic

sampling of amino acid residues)

Build Model

Proteins, 2011, 79, 2693-2710

Validated by

prediction of

experimentally-

measured amino

acid pKa values

Interaction graph

treated as a belief

network, and solved

for the partition

function for each node

The state of K99

depends on the

state of Y93, as

they interact with

each other


Using PIPs to explain SAR

negativeTNNI3K 7.3; bRaf 6 TNNI3K 7.5; bRaf 6.2 TNNI3K 7.1; bRaf 6

TNNI3K 6.7; bRaf 6.6 TNNI3K 6.4; bRaf 6.4 TNNI3K 5.6; bRaf 5.2

11.0 10.511.2

10.5 9.310.1

4 3.24.5

3.4 1.52.8

y = 0.8853x + 5.0458R² = 0.9428

9.8

10

10.2

10.4

10.6

10.8

11

11.2

11.4

11.6

11.8

5 5.5 6 6.5 7 7.5 8

Edge Field size vs TNNI3K activity

No correlation for bRaf – why?

Lawhorn et al. J. Med. Chem. 2016, 59, 10629−10641


Selectivity - TNNI3K vs B-Raf

Lawhorn et al. J. Med. Chem. 2016, 59, 10629−10641

TNNI3K B-Raf

Ring in +ve PIP, so e-

donating goodRing in -ve PIP, so e-

donating bad


3D-RISM – water position and stability


3D-RISM

> Analytical method for working out where water goes (Ornstein-Zernike equation)

> Conceptually equivalent to running an infinite-time MD simulation on the solvent and extracting the solvent particle densities

ℎ 𝑟12 = 𝑐 𝑟12 +න𝑑𝑟3𝑐 𝑟13 𝜌 𝑟3 ℎ(𝑟23)

Total correlation

function

'What is the

distribution of solvent

around the solute?'

Direct correlation

function

'How does a solvent

molecule interact with

the solute?'

Indirect influence through all possible

chains of mediating third particles

'What is the effect of a solvent

molecule interacting with another

solvent molecule which is interacting

with the solute?'


3D-RISM

> Analytical method for working out where water goes (Ornstein-Zernike equation)

> Conceptually equivalent to running an infinite-time MD simulation on the solvent and extracting the solvent particle densities

> Output is grid containing particle densities (for water, O and H densities)

> Thermodynamic analysis to assign 'happiness' to each position on the grid


Limitations

> Fixed solute> No accounting for protein movement

> Can’t solve equations exactly> Need to use a 'bridge function' – unclear what the correct functional form is

> Results depend on the interaction potential U(r) used by the closure function> In practise, this means vdW + electrostatics

> Results only as good as your potential functions

> Need to pay attention to total formal charge of the system

> An improved description of electrostatics will give better results!


How to compute RISM water G values?

> Raw output from RISM calculation is density and G grids for H

and O

Place oxygen on highest-

density grid point

O H




and O

O H

Compute G by

integrating G

grids on atomic

volumes




and O

O H

Repeat with multiple

orientations and

small translations




and O

O H

Final G estimate is

weighted average of

the MC samples

(weighted by the

product of the O and

H density values)


3D RISM example - 4ZLZ (BTK)


RISM calculation on 4ZLZ

> Bridging water correctly placed

> Computed G is very slightly

favourable

> Water is displaceable, but you need to

match it pharmacophorically


Calculate 3D-RISM for bridging water molecule


WaterSwap – ligand energetics


> WaterSwap uses a λ-coordinate to swap a ligand and a water cluster

between a protein box and a water box

WaterSwap - Method

Water box Protein box Protein box Water box


Parallel calculation of WaterSwap results

> WaterSwap calculations are compute-intensive

> MC simulations are not amenable to distributed computing or

GPGPUs

> Runs on one machine – speed determined by number of cores

> Can we get sufficient sampling by combining independent runs?


Ongoing collaborations


> Accurate pose prediction and detailed scoring> See new molecule designs within the

cavity of your protein target

> Accurate pose prediction

> Detailed scoring function

> Flexible deployment

% Correct pose prediction Top 10 Top 3 Top 1

Build model and Lead Finder 88 86 82

Performance on the Astex diverse set with success counted only if the RMSD

between X-ray and docked ligand was less than 2.0Å in 5 or more of 10 runs

Lead Finder (BioMolTech)

Lead Finder successfully finds the correct ligand

position (sticks) with 1.2Å RMSD from an X-ray

structure (electron density as mesh) in this difficult

HIV-1 protease example


Lead Finder

Lead Finder successfully finds the correct ligand

position (sticks) with 1.2Å RMSD from an X-ray

structure (electron density as mesh) in this difficult

HIV-1 protease example

> Template docking

> Fast docking with constrained poses

> Field-guided docking

> Combine ligand and protein information

> Improved ring sampling

> Use the XedeX ring library from the

CSD to improve ring sampling

> XED electrostatics

> Use the XED force field electrostatics


Working with the best academic and commercial science

> WEGA (iPrecision Medicine)

> Improved Gaussian shape similarity

> CPU + GPU

> Aim to launch a shape similarity tool in

2017

> Others

> Ongoing or upcoming collaborations

with ICL, Sheffield, TMCS etc.

> XED improvements

> Adding metal support to the XED force

field

> General parameter improvements

> BioSimSpace

> Collaboration between Edinburgh,

Bristol, UCL, and Nottingham

> Develop workflow components for

molecule design

> Cresset is the commercial partner


cressetgroup

[email protected]

Peeking into the heart of new Cresset science

Peeking into the heart of new Cresset science · 2019-06-25 · Protein interaction potentials...

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