EMERGING RISKS FOR FOOD SAFETYAND PUBLIC PERCEPTION

MAY 15-17 2018, PARMA

In silico methods for food contaminants:the pesticides case study

Pietro Cozzini

Basicstatements

TOO MANY CHEMICALS EVERY YEARImpossible to test in a wet lab

More then 4000 are classified as pesticides

A 'pesticide' is something that prevents, destroys, or controls a harmfulorganism ('pest') or disease, or protects plants or plant products duringproduction, storage and transport.

An active substance is any chemical, plant extract, pheromone or micro-organism (including viruses), that has action against 'pests' or on plants, parts of plants or plant products.

Source: E.U Pesticides Database

Endocrine disruptors are chemicals which under certain conditions can impacton the hormonal system of humans and animals.

Cell

ReceptorEndogenous

Hormone

Cell

Receptor Agonist

Cell

Receptor Antagonist

Endocrine Disruptor

Normal Response Excessive Response No/Low Response

X

PESTICIDES IN THE ATMOSPHERE AND WATER

WHY COMPUTATIONAL

SIMULATIONS?

FAST & CHEAP

AIM: To predict the energy interaction between a small moleculeand a large protein as a marker

a two body interaction prediction(large molecule – small molecule)

or a n-body interaction prediction:(large molecule, small molecule, waters,metal ion and another molecule)

The problem:

wat301 at HIV-1 protease-ligand interface

IN SILICO

IN VITRO

IN VIVO?

Thousand…………

Millions

Tens

Few units

Food Chem Toxicol.2008 Mar;46 Suppl 1:S2-70. doi: 10.1016/j.fct.2008.02.008.Safety and nutritional assessment of GM plants and derived food and feed: the role of animal feeding trials.EFSA GMO Panel Working Group on Animal Feeding Trials.

The Lock and Key model (and the Locksmith)

+

STRUCTURE SOURCES

X-RAY and NEUTRON DIFFRACTION

NMR

HOMOLOGY MODELLING

�X-ray diffraction

�Neutron diffraction

�NMR

�Homology Modelling

�Resolution value

�Atom and Bond types attribution

�Hydrogens position

�Water orientation

�Differences between crystallization pH and Ki determination pH

�Incompatibility between different molecular modelling programs (SYBYL,

Insight, MacroModel…)

difficulties

STRUCTURAL DATA: BE CAREFUL!

TECHNIQUES STRUCTURAL LIMITS

THE DOCKING PROBLEM

HOW CAN WE BUILD RELIABLE MODELS AND OBTAIN GOOD PREDICTION IN TIME?

Positioning: a geometry problem

Evaluation energy: a chemistry problem

Screening/Docking engine Fast Scoring Function

UNBOUND STATE:BOTH LIGAND AND RECEPTOR ARE SOLVATED AND DO NOT INTERACT

BOUND STATE:LIGAND AND RECEPTOR ARE PARTIALLY DESOLVATED AND THEY INTERACT WITH OR WITHOUT THE SOLVENT CONTRIBUTION

Virtual Screening

Structure based

Ligand based

Docking and Consensus Scoring (Re-scoring)

GOLD HINT

Our computational approach

FLAP & GRID approach

3D DB

ELECTROSTATIC DG=DH-TDS

Energy estimation…. Scoring Functions

A SCORING FUNCTION MAPS AN ABSTRACT CONCEPT TO A

NUMERIC VALUE�The scoring function provides a way to rank placements of amolecule relative to one another.�All scoring methods consider and estimate the free energyas a sum of terms while biological processes are concertedevents

Is this reallyFree Energy?

YES it is!

BACK TO FUNDAMENTALS!

ΔG = ΔH - TΔS

ΔG° = -RTlnK

S ai = Log Po/w = - DG° / 2.303 RT

SS bij = f (DG°)

bij = interaction score between atoms i and ja = hydrophobic atomic constantS = solvent accessible surface areaRij = exponential function e-r

Tij = logic function: +1 or -1 depending on the character of the interacting poalr atomsrij = van der Waals terms

HINT score = SS bij = SS aiSi ajSj Rij Tij + rij

HINT Hidropathic INTeractions

Another definition of a docking machine(for someone)

NO!

Unsuccesfull dockings

PDB protein ligand failure

1aaq HIV-1 protease Hydroxyethylene isostere Very flexible ligand

1rne renin CGP38560 Very flexible ligand

1poc Phospholipase A2 phosphoenolamine Very flexible ligand

1lic LBP Hexadecanesulfonic acid Very flexible ligand

1eed endothiapepsin PD125754 Very flexible ligand

1eap Catalytic antibody phosphonateFlexible ligand,

symmetrical distribution of apolar groups

1glq Glutathione transferase glutathione Partially buried ligand

1mer IGD light chain N-acetyl-L-His-D-Pro-Oh Open/Shallow active site

1ghb Chymotrypsin N-acetyl D-Trp Short intemolecular dist

1ive Neuroaminidase Aminobenzoic acid Short intemolecular dist

Kellenberger et al. PROTEINS 57:225-242 (2004)

Docking Results Accuracy

IFthe interaction is predicted positive

THENthe compounds should be subjected to further experimental trials

ELSE IFthe prediction is negative

THENfurther experiments should be carried on in order to verify the

strength of the technique, improve method and deeply understandmolecular mechanisms of binding/activity.ENDIF

CASE STUDIES:drugs, food or contaminants it is always chemistry!

drug molecules or food molecules could act as xenoestrogens!

PESTICIDES:CLASSIFICATION BY USE AND CHEMICAL STRUCTURE

INSECTICIDESPyrethroidsOrganophosphorusCarbamatesOrganochlorineManganese compounds

FUNGICIDESThiocarbamatesDithiocarbamatesCupric saltsTiabendazolesTriazolesDicarboximidesDinitrophenolesOrganotin compoundsMiscellaneous

HERBICIDESBipyridylsChlorophenoxyGlyphosateAcetanilidesTriazines

FUMIGANTS Aluminium and zincphosphideMethyl bromideEthylene dibromide

RODENTICIDESWarfarines•ndanodiones

INSECT REPELLENTSDiethyltoluamide

Source: FAO

PESTICIDES FOUND IN WATERS (lacs, rivers)

Triazine

Triazole

Urea derivatives

Organophosphates

Propiconazole: fungicideCAS: 60207-90-1

Clorpirifos: insecticideCAS: 2921-88-2

Glifosate: herbicideCAS: 1071-83-6

Diuron:Erbicida330-54-1

AtrazineErbicida

1912-24-9

ER-B

H12 H12

H12H12

AR PPAR

ER-Α

NUCLEAR RECEPTORS

ER - 2YJA AR - 2AM9 PPAR�- 3NOA

Flexibility and conformational space

Macro Flexibility: αER-LBD

Active Inactive

OPEN (ANTAGONIST)CONFORMATION

CLOSE (AGONIST)CONFORMATION

H12

H12

BINDING SITE• Glu 353• Arg 394• Asp 351• His 524

How many conformations of the receptor: two or more?

Testo

LOCAL FLEXIBILITY: HIS 524

Even if a docking program could easily find an accurate pose for a ligand, accurate ranking sometimes remains

an issue

moreover….

Docking Problems and LimitsImpossible simulation of covalent interactions formation

and breakingDifficult evaluation of water molecules contribution

High MW ligands are difficult to dock High percentage of false-positive results in virtual

screening

LEU 349

GOLD

PROPICONAZOL

ALA 350

LEU 346

GOLD

CLORPIRIPHOS METHYL-PIRIMIPHOS

ESTROGEN RECEPTOR (alpha)

LEU 349

LEU 384

HINT

LEU 349

HINT

ALA 350

LEU 525

HINT

GOLD

LEU 525

ALA 350

LEU 707

LEU 701

THR 877

LEU 704

PROPICONAZOL CLORPIRIPHOS METHYL-PIRIMIPHOS

ANDROGEN RECEPTOR

GOLD GOLD

LEU 707

GLN 711

HINT

LEU 701

HINT

LEU 707

HINT

THR 877

ASN 705

GOLD

THR 877

ASN 705

PPAR (α; δ; γ)

The PPAR case

Cavity: 1300-1400Å3

3 binding sites

Macro flexibility (Ω-loop)

Arm I

Entrance

Arm II

PDB_Cluster II Resolution3NOA 1,98

PDB_Cluster III Resolution2ZK6 2,41

PDB_Cluster I Resolution3R8I 2,30

CONCLUSIONS

• Clorpiriphos, Propiconazol and Methyl-Pirimifos are estimated good binders for the estrogen receptor

• Clorpiriphos and Propiconazol are good binders for the androgen receptor.

• DOSE-DEPENDENT effects on cell proliferation with Propiconazol

• NO effects on cell proliferation with Methyl-Pirimiphos

• Down regulation of Estrogen Receptor and Up regulation of AndrogenReceptor with Methyl-Pirimiphos

FUTURE DEVELOPMENTS

• Define a reliable model for in silico study of PPAR endocrine disruptors

• Repeat in vitro tests with Clorpiriphos, Propiconazol and Methyl-Pirimiphos

• Do tests with cells T47D

1.Experience in several fields wins. 2.Go big early: Fill all the space in the cavity.3.More than one template can fill the space.4.Solubility matters.5. Integrated molecular biology is critical.6.Computational tools are inadequate.7.Physical and biological properties can be manipulated.8.Every water molecule is special.9.Not all hydrogen bonds are created equal.10.Small structural changes can result in major changes in

binding mode.

Structure Based Drug Design Take Home Messages

User Guide (because this is a school!)

EMERGING RISKS FOR FOOD SAFETYAND PUBLIC PERCEPTION

THANK YOU FOR YOUR ATTENTION!

Cell proliferation asssay through MTS on human breast adenocarcinoma cell line

Methyl-Pirimiphos (0.01-0.5-1-5-10-50-100 µM)

ACTELLIC

Propiconazol (0.01-0.5-1-5-10-50-100 µM)

NOVEL DUO

NOeffects

on cell proliferation!

DOSE-DEPENDENTeffects on cellproliferation

GI50 = 16 µM

Real time PCR

Progesteron Receptor (PR) àregulated through the transcriptional activity

of ER

Specifically androgen-regulated gene protein (SARG) à

regulated through the transcriptional activity of ER

Methyl-Pirimiphos Propiconazol

No Alteration!!!Down-regulation of PR

Up-regulation of SARG100 μM

AIM: To borrow drug design techniques for food science

Many compounds of interest for food science are able to mimic endogenous molecules

Toxins

Food additives

Natural compounds

Industrial or packaging contaminants

Many proteic targets has been identified......…too much to be identified!

TARGET STRUCTURE

LIGANDSTRUCTURES

LIGAND BASED SCREENING

QSAR

HTPSCOMBICHEM

NO

YESNO

A MOLECULAR MODELLING FLOW CHART

TARGET STRUCTURE

LIGANDSTRUCTURES

STRUCTURE BASED DRUG

DESIGN

DE NOVODESIGN

YES

YESNO

VALIDATION OF AN “IN SILICO” METHOD FOR THE SCREENING OF THE INTERACTION BETWEEN ESTROGEN RECEPTORS

ALPHA AND FOOD MOLECULES

The method resulted highly predictive for: Parabens and PCBs (0.74 and 0.65 r2 respectively) and alkylphenols (r2 0.42 and a 71% success rate).

DB of 126 ligands

TOOLS

Ligand and Binding SiteStructure Analisys

Virtual screening

Docking/scoring simulations

Re-scoring procedure

GOLD (Parma)

LIFE EDESIA

HINT(Hydropathic INTeractions)

HINT: empirical scoring function based on experimental LogPo/w values

MOLEGRO MolDock (Etnalead)

SCREENING OF FOOD POLLUTANTS

493 compounds

53 choosen

37 analized

13 potential xenoestrogens

1500 compounds

Database developed from JEFCA and EAFUS(Joint FAO/WHO Expert Committee on Food additives and

Evrythings Addes to the Food in U.S.A)

IN VITRO TESTS

LIFE EDESIA SCREENINGQSAR, DBASE, Literature

DOCKING/SCORING (54 CHEMICALS)

ER-aER-b

AR-wtAR-mt

PARMA/CATANIA Consensus Docking Report

• BISPHENOLS

• PHTHALATES

• PARABENS

• ACIDS

• MISCELLANEOUS

LIFE EDESIA SCREENING

Bisphenols

Common Name ER� ER� ARwt ARmutHT/MD HT/MD HT/MD HT/MD

Bisphenol A YES - YES- YES - Maybe -* bisphenol AF YES YES YESYES YES NO NO NO

bisphenol AP YES YES YESYES NO NO Maybe MaybeBADGE NO NO NO NO Maybe YES YESYESBFDGE NO NO NO NO Maybe Maybe YESYESPergafast201 NO NO NO NO NO NO NO NOBisOPP-A NO NO NO NO NO NO NOTGSA NO NO NO NO NO NO NO NOMBHA YES YES YESYES Maybe Maybe Maybe YESS-2,4-bisphenol YES YES YESYES NO NO NO NOBisphenol F YES YES YESYES Maybe YES NO NO

* BPS-MPE NO YES NO YES NO NO NO NO* Bisphenol C NO NO NO NO Maybe NO YESMaybe

BPS NO NO NO NO NO NO NO NO* 1,7-Bis(4-hydroxyphenylthio)-

3,5-dioxaheptane NO NO NO NO Maybe YES Maybe NO* D8 NO YES NO YES NO NO Maybe Maybe

BPS-MAE NO NO NO NO NO NO Maybe YES

* → indicates incongruence of prediction between docking methods

LIFE EDESIA RESULT(Unpublished data)

Now we are ready for in vitro tests in a wet lab

Saggio di proliferazione mediante MTS

Cellule di adenocarcinoma mammario

Risposta proliferativa in presenza di estrogeni

Pirimifos-metile (0.01-0.5-1-5-10-50-100 µM)

ACTELLIC

Propiconazolo (0.01-0.5-1-5-10-50-100 µM)

NOVEL DUO

NESSUNA INIBIZIONE

DELLA PROLIFERAZIONE

CELLULARE!

GI50 = 16 µM

INIBIZIONE DOSE DIPENDENTE

Real time PCR

CONCENTRAZIONI:

Pirimifos-metile: 50-100 µM

Propiconazolo: 0.5-1 µM

Trattamento per 24h delle cellule MCF-7

∆Ct = Ct gene di interesse – Ct gene house-keeping

∆∆Ct = ∆Ct trattato - ∆Ct non trattato

Fold change = 2 -∆∆Ct

>2 up-regolazione

<0.5 down-regolazione

50 μM 100 μM

PR 0,477977 0,138123

SARG 0,495539 4,854689

0,5 µM 1,0 µM

PR 0,640437 0,614399

SARG 0,675211 1,519948

Nessuna alterazione!

Livelli basali dei trascritti

Down-regolazione di PR e up-regolazione di SARG a 100 μM