Roaming through natural products space with JChem & KNIME · ChemAxon UGM 2016. Nestlé Institute of Health Sciences (NIHS) Developing the scientific foundation of how nutrition can

Roaming through natural products

space with JChem & KNIME

Jasna Klicic Badoux

ChemAxon UGM 2016

Nestlé Institute of Health Sciences (NIHS)

Developing the scientific foundation of how nutrition can be used to

empower people to improve and maintain their health

• founded in 2011

• based near Lausanne, Switzerland

• over 150 scientists

Confidential

Exploring the link between nutrition and health

molecular level

Nakaya H., Nat. Immun. 2011, 12, 786

cpd libs

+ HTS

Confidential

Confidential

Chemical space of natural products

Chemical space of natural products: size

10641081063105

est. d

rug-lik

e m

ole

cule

s

public

data

bases o

f know

n c

pds

Chem

Spid

er, P

ubC

hem

typic

al big

pharm

a c

pd c

olle

ction

ChE

MB

L

data

bases o

f natu

ral pro

ducts

and

meta

bolit

es:

DN

P,

HM

DB

,

Lip

idM

aps

number of molecules

Confidential

Natural products vs. drug-like molecules: properties

Grabowski K., Nat. Prod. Rep. 2008, 25, 892

17%

property DNP drug-like

property variability +++ (size, polarity) ---

HB donors & acceptors +++ ---

aromatic atoms 1 in 6 1 in 2

heteroatoms 6 O & 1 N per mol 3 O & 3 N per mol

chiral centers 6 per mol 1 per mol

Lipinski rule-of-five 74% comply 100% (by definition)

Confidential

Molecular frameworks

Confidential

keep rings and ring linkers

keep exocyclic double bonds

mutate all atoms to C

make all bonds single

molecule

atomic scaffold

graph scaffold

G.W. Bemis & M.A. Murcko

J. Med. Chem. 1996, 39, 2887

Natural products vs. drug-like molecules: scaffolds

overall broader scaffold diversity for druglike molecules than for

natural products !

68%

1 mol/scaff

51 338 atomic scaffolds(2.4 mol/scaff)

121 593 drug-like molecules


128 600 natural products

acyclic

2%

≥ 2

mo

l/sca

ff

acyclic

9%

59%

1 mol/scaff

≥ 2

mo

l/sca

ff

11%

accounts for

half the library

4%

Gra

bo

wskiK

., N

at.

Pro

d. R

ep

.2

00

8, 2

5, 8

92

Confidential

molecular and phys-

chem properties very similar

to DNP averages

Property DNP NIHS

Aromatic atoms 5.1 (7.2) 6.6 (6.8)

Chiral atoms 5.9 (6.6) 6.1 (6.9)

Heavy atoms 30.6 (17) 31.3 (16)

Oxygen atoms 6.5 (5.9) 7.6 (5.9)

Nitrogen atoma 0.5 (1.5) 0.5 (1.3)

HB acceptors 6 (5.5) 7.1 (5.6)

HB donors 3.1 (3.6) 3.6 (3.6)

Lipinski 0.8 (1.1) 0.9 (1.2)

MW 430 (246) 440 (230)

Rigid bonds 26.8 (16) 27.9 (15)

Rotatable bonds 6.1 (6.7) 6 (5.3)

Rings 3.4 (2.3) 3.6 (2.1)

logP 2.9 (3.2) 2.1 (2.7)

TPSA 109 (96) 125 (94)

CRC Dictionary of Natural Products

(DNP 24.2, Taylor & Francis Group)

~ 250'000 cpds

NIHS samples library

~20'000 pure cpds

NIHS sample library compared to DNP: properties

Confidential

NIHS sample library compared to DNP: scaffolds

relatively more scaffold singletons & higher scaffold diversity for NIHS vs DNP



acyclic

8%

77%

1 mol/scaff

≥ 2

mo

l/sca

ff

4%

accounts for

half the library

17%



acyclic

9%

59%

1 mol/scaff

≥ 2

mo

l/sca

ffConfidential

Favorite JChem KNIME nodes

Confidential

Favorite JChem KNIME nodes: Standardizer

Confidential

• strip salts

• neutralize

• tauromerize

• aromatize

• ...

Favorite JChem KNIME nodes: Chemical Terms

Confidential

• molecular and phys-chem properties

• Bemis-Murcko framework

Favorite JChem functions: conversion

Confidential

in particular: name to structure

Library subset selection

Confidential

How to look for a representative subset ?

Objective: select 15% of representative and "best chance" compounds

from the in-house pure compound collection

NIHS collection 15% subset?

Select molecules that:

a) are bioavailable (cellular assay)

b) have a better chance to have a good binding energy

c) representative of diverse natural product chemical classes

Confidential

How to look for a representative subset ?

Objective: select 15% of representative and "best chance" compounds

from the in-house pure compound collection

Select molecules that:

a) are bioavailable (cellular assay)

b) have a better chance to have a good binding energy

c) representative of diverse natural product chemical classes

Confidential

15% subsetNIHS collection bioavailability

filter

"best chance"

filtersdiversity

subsets

Lipinski's "rule of five"

empirical rule formulated by Christopher A. Lipinski in 1997

most orally administered drugs are relatively small and moderately lipophilic molecules

indication of compound's bioavailability

MW < 500

clogP ≤ 5

HBD ≤ 5

HBA ≤ 10

No more than one violation

Confidential

Protein-ligand binding step-by-step

+ΔG

ΔGbind = ΔGComplex* – ( ΔGProt* + ΔGLig* )

* = hydrated

ΔGbind = ΔHbind – TΔSbind

Confidential


+

Confidential

forming protein-ligand

bonds ΔH


+

breaking ligand-

water bonds ΔH

ordered water to

bulk water ΔS

+ +

constraining ligand

and protein ΔS

What leads to good binding energy:

a) unfavorable ligand hydration in free state

b) low increase of entropy upon adapting the binding conformation

c) favorable protein-ligand interactions

Confidential


What leads to good binding energy:

a) unfavorable ligand hydration in free state

b) low increase of entropy upon adapting the binding conformation

c) favorable protein-ligand interactions

non-specific

interaction

highly planar

molecules

few polar atoms

(HBD/A, aromaticity)

low permeabilityvery polar/charged

molecules

low binding affinity long flexible chains

+ ΔG

Confidential

"Best chance" filters - excluded molecules

1) number of aromatic rings < 5

2) no more than 90% of aromatic (heavy) atoms

3) at least 2 HB donors or acceptors

Confidential

Additional filters – examples of excluded molecules

4) clogP at least -1

5) contains at least 1 ring or fewer than 8 rotatable bonds

TBD

Confidential

Chemical class assignment - method

Chemical class analysis of Dictionary of Natural Products (DNP, version 24.2),

250k cpds

DNP entry

atomic scaffold

flavonoid

chemical class

scaffold –

chem. class45'000 entries

13% entries have multiple

class assignments per

scaffold

Confidential

Chemical class assignment - results

DNP

NIHSscaffold –

chem. classNIHS collection

1) chemical class assignment of compounds in NIHS collection

2) chemical class distribution of NIHS collection, compared to DNP

Legend

Confidential

Compond selection method - implementation

1) bioavailability filter: Lipinski rule-of-five

2) additional filters to prioritize "best-chance" compounds

3) division into subsets and class assignment; final selection of 25% per subset/class

based on chemical diversity

16'500 cpds

NIHS collection2500 cpds

12'400

cpds10'700

cpds

a) class assignment

b) multiple class assign.

c) no class assignment

Lipinskidiversity

(3 subsets)

"best

chance"

filters

Confidential

Bioavailability of CNS bioactives - CNS-MPO

Wager T.T., ACS Chem. Neurosci. 2010, 1, 435

analyzed physchem/molecular properties of 119

marketed CNS drugs and 108 Pfizer CNS

candidates (2010):

majority of the CNS drugs have properties in

the specific range

Confidential

CNS-MPO score

monotonic decreasing "bump"

Wager T.T., ACS Chem. Neurosci. 2010, 1, 435

Confidential

Implementation in

JChem/KNIME

CNS-MPO score of Dotmatics collection

favorable score for

44% of the library

1 2 3 4 5 6

177540

4247 4185

34523853

≤

16'500 cpds

NIHS

collection

5000 cpds7'708

cpds6'754

cpds

CNS

MPOdiversity3 subsets

7'730

cpds

Lip

inski

Compound selection

"best

chance"

filters

Confidential

JChem & KNIME: the perfect marriage

Confidential

Targeted metabolites and their analogs

Lipinski, additional filters

295

metabolites

from DNP

137

similar cpds in NIHS

library (Tan. 0.75)

725

Confidential

Targeted metabolites and analogs: implementation

Confidential

Confidential

T H A N K Y O U !

NIHS

Jaroslaw Szymczak

Terry Reilly

Laurent Dobler

Denis Barron

Sofia Moço

Yann Ratinaud

Loraine Merimod

Radovan Chytracek

Bruce O'Neel

ChemAxon

Akos Tarcsay

Anna Forró

Ivan Harmath

Árpád Figyelmesi

Tamas Pelcz

KNIME

Jon Fuller

Roaming through natural products space with JChem & KNIME · ChemAxon UGM 2016. Nestlé Institute of Health Sciences (NIHS) Developing the scientific foundation of how nutrition can

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