Computational modeling of

protein-ligand interactionsO. Taboureau, AC. Camproux, P. Tuffery

UMR-S 973

Club Bioinfo Ocotber 11, 2018olivier.taboureau@univ-paris-diderot.fr

Molecules Therapeutiques in Silico (MTi) Unit

http://www.mti.univ-paris-diderot.fr/

Peptide

design

Profiling

Virtual

screening

& ADMET

Team 3

M.Miteva

B.Villoutreix

Team 2

AC. Camproux

O. Taboureau

Team 1

P. Tuffery

UMR-S 973

(Dir : B. Villoutreix)

2014-2018

Computational modeling of protein-ligand interactions

2019-2023

Computational modeling of protein-ligand interactions

Plate-forme RPBS (IBiSA) (P. Tufféry, S. De Vries, J. Rey)

(Ressource Parisienne en Bioinformatique Structurale)

PAAS-SdV

http://bioserv.rpbs.univ-paris-diderot.fr/index.html

Targets

Proteins

Protein-protein

Interactions

ADN, ARN, ...

Molecules

Small compounds

Peptides

AntibodiesMulti targets profiling

Pathway and ppi

Clinical effects

Gene expression

and toxicogenomic

Genetic variations

2 research areas :

- Pharmacological profiling

- Protein-peptide interactions

and peptides design

Genomic information

Computational modeling of proteine-ligand interactions

Objectives

Peptide design: Recognition

bioserv.rpbs.univ-paris-diderot.fr/services/BCSearch Guyon et al., Bioinformatics 2014; NAR 2015

Input: gapped amino acid sequence

Output: 3D fragments matching flanks & size 3D

Fast geometric search in protein structures Accurate loop modeling

Karami et al., Sci. Rep. 2018

Over 50% of successful loop models are derived from unrelated proteins, indicating that fragments under

similar constraints tend to adopt similar structure,

beyond mere homology.

Peptide design: Modeling

Forward-backtrack, K-best, Taboo sampling3D encoding of structures

Using Hidden Markov Models

Protein binding site identification Folding peptide at protein binding site

http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-SiteFinder/

http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD/

http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/

Maupetit et al., NAR, 2009

Thevenet et al., NAR, 2012

Shen et al., J. Chem. Theor. Comput., 2014

Lamiable et al. NAR, 2016

Lamiable et al, J. Comput Chem., 2016

Saladin et al, Nucleic Acids Res., 2014

Camproux et al. J. Mol. Biol., 2004

Peptide structure modeling

Protein-protein interactions

Peptide discovery: BactPepDB: a database of predicted peptides in prokaryotic genomes

Rey et al., Database, 2015

Peptide design, off-target: Non sequential alignments

èFar more difficult problem but useful for protein surface comparisons. atoms involved in a

function, : interaction with a drug, interaction with other proteins

ARA H201

10 20 30

40 50 60

MLTILVALAL FLLAAHASAR QQWELQGDRR

CQSQLERANL RPCEQHLMQK IQRDEDSYER

70 80 90

100 110 120

DPYSPSQDPY SPSPYDRRGA GSSQHQERCC

NELNEFENNQ RCMCEALQQI MENQSDRLQG

130 140 150

RQQEQQFKRE LRNLPQQCGL RAPQRCDLDV ESGGRDRY

l We use graph theory : search for cliques or quasi-cliques in product graphs

l We developped a similarity measure already used in image analysis for face or object recognition

(Binet-Cauchy Kernel)

Rasolohery I. et al., J. Chem. Inf. Model. 2017

Comparison of atom positions independently of the amino-acid sequence order

Membrane proteins motions

Molecular DynamicSimutations

Coarses grain

Synthetic channels

Pharmacological profiling: Target based

Conclusions & perspectives

Pockdrug extension using dynamic molecular simulations:

Characterization and validation of proteins as therapeutic targets and prediction of

drug-target interactions based on statistical & bioinformatics structural approaches

Prediction of ligand profiles using biostatistical approaches

Further deepen the profiling study: propose ligand candidates

that can bind with high affinity druggable pockets

Current applications: NS1 (non structural protein of influenza virus)

Proteins flexibility and transient pocket analysis

coA catalytic pocket

Prediction of druggable targets:

Proteins able to bind drug-like molecules, using

statistical approaches: Pockdrug websiteBorrel et al, 2015, Hussein et al, 2015

Hussein et al, 2017, Regad et al, 2017, Alam et al, 2018 Cerisier et al, 2017a, Cerisier et al, 2017b

Inhibition of HIV-2 protease: study of the resistance mechanism using in silico

approaches

LS

3D structures : 15 PR1

13 PR2unbound, bound forms

(indinavir, darunavir, amprenavir)

Tâche 1 : Récolte des

données sur PR1, PR2 et les IPs

Tâche 2 : Analyse et

comparaison de la structure des PR1 et

PR2

Tâche 3 : Analyse

et comparaison des IPs

Tâche 4 : Analyse et

comparaison des modes de liaison (=

site de liaison + IPs)

Construire des modèles statistiques

pour prédire l’activité d’une molécule vis à vis de PR2 sauvage

ou mutante

Mieux comprendre les mécanismes

de la résistance naturelle et acquise du VIH-1 et VIH-2

Tâche 5 : Prédiction

des effets des mutations de

résistance sur PR2

SA-Conf

AA

LS

Triki et al, 2018a, Triki et al, 2018b, Triki et al, 2018c, Triki et al, 2018d accepted

Pharmacological profiling: Target based – Application on HIV-2 proteases

Projet VIH2-Bioinfo

Pharmacological profiling: Data integration

Chemical

Bioactivity

Disease Pathway

Gene Ontology

Therapeutic

effect(ATC code)

Disease

complex network

Structural chemical

similarity and QSAR

Sequences protein similarity

Disease

Adverse drugreaction

Fingerprints, SEA

BLASTProtein

Integration of genetic variation (SNPs) -pharmacogenomics

Integration of gene expression

Protein-ADR prediction

Drug-target prediction

Enrichment analysis

http://potentia.cbs.dtu.dk/ChemProt/ Kringelum J. Database 2016

Promiscuity

ChemProt: focused on Drug-Target-Biological outcomes profiling

Many data not always linked together Need of data integration

Pharmacological profiling: Systems chemical toxicology

Toxicogenomics data analysis

More than 10000samples

WY-14643

Gene pvalNom(invitro)

pvalNom(invivo)

Acot1 4.45e-04 6.74e-08

Aig1 2.82e-04 2.14e-06

Ehhadh 4.43e-05 3.18e-06

Acox1 5.31e-04 4.97e-06

Cpt1b 1.53e-03 5.95e-06

Cpt2 2.09e-03 6.82e-06

Slc27a2 6.04e-04 1.04e-05

Fatty acid transporter

Lipid metabolism

Acetaminophen

Gene Set Enrichment Analysis approach (GSEA)

Com

pounds

Genes

Analysis of large scale microarray data

Taboureau O. J. Pharmacogenom. Pharmacoprot. 2012

Ligand-protein interaction Cell deregulation Metabolic pathways affected

+ + =- Suggestion of gene’s

biomarkers (ALDH7A1,

OSBL9 …)

- Suggestion of molecules

with a risk.

Such analysis can be used for computational phenotypic screen.

Prediction of DILI compounds and genes associated to steatosis

Pharmacological profiling: Example with Steatosis and DILI

Aguayo-Orozco et al., Front Genet, 2018

Chemical)disease,associa.ons,

Strong,evidence, Good,evidence, Limited,evidence,

Disease)disease,associa.ons,

Chemical,linkage,networks,

Disease, Disease, Score,

A, C, 0.50,

B, C, 0.38,

A, C, 0.21,

A,

C,

B,

A,

D,

B,

A,

C,E,

E,G,

F,

A,

C,

Chemical)disease,predic.on,

?,

Strong,evidence, Good,evidence, Limited,evidence,

Parkinson’s disease

Uterine cancer

Genito−urinary malformations

Cervical cancer

Vaginal cancer

Cholestasis

Altered time to sexual maturation

Reduced Fertility − male

Abnormal sperm

Adult−onset Leukemias

Reduced fertility − female

Hepatoma

Fetotoxicity

Immune suppression

Testicular toxicity

Renal cancer

Pancreatic cancer

Red blood cell damage

Stomach cancer

Acute non−lymphocytic

leukemia

Aplastic anemia

Menstrual disorders

Childhood Leukemias

Brain cancer − childhood

Salivary gland cancer

Brain cancer (adult)

Multiple myeloma

Thrombocytopenia

Thyroid cancer

Myelodysplastic syndrome

Nasopharyngeal cancer

Low birth weight

Pancreatitis

Porphyria

Sudden Infant Death Syndrome

Laryngeal cancer

Delayed growth

Fetal Alcohol Syndrome

Skeletal malformations Cardiac

congenital malformations

Esophageal cancer

Cerebrovascular disease

Pre−term delivery

Breast cancerDecreased vision

Cranio− Facial malformations

Hormonal changes

Systemic Lupus Erythematosus

Methemoglobinemia Raynaud’s phenomenon

Arrhythmias

Silicosis

Scleroderma

Angiosarcoma

Anemia

Diabetes − Type II

Autoimmune antibodies Acroosteolysis

Steatosis

Hepatitis

Cirrhosis

ADD/ADHD

Behavioral problems

Psychiatric disturbances

Berylliosis

Scrotal cancer

Skin cancer

Ovarian cancer

Mesothelioma

Pleural disease

Cognitive impairment

Hypertension

Cataracts

Gout

Melanoma (skin cancer)

Itai−itai disease

Chronic renal disease

Osteoporosis

Renal stones

Bronchitis − chronic

COPD

Cardiomyopathy

Asthma − irritant

Rhinitis − irritant

Bronchiolitis obliterans

Asbestosis Pneumoconiosis

Hard metal disease Congenital

malformations

Seizures

Decreased Coordination

Spasticity

Cerebral palsy

Hearing loss

Nasal septal perforation Nasal polyps

Myocardial infarction

Coronary artery disease

Minamata disease

Cancer

Cardiovascular

Connective tissue

Dermatological

Developmental

Ear, Nose, Throat

Endocrine

Gastrointestinal

Hematological

Immunological

Liver

Metabolic

Musculo-skeletal

Neurological

Ophthalmological

Psychiatric

Renal

Reproductive

Respiratory

20

Node size

Disease class

10

1

Development of network-based analysis tools to predict chemical-chemical

interactions to diseases and other biological outcomes

Pharmacological profiling: Network-based Analysis

Audouze K et al. PLoS One, 2014; Taboureau et al. ALTEX, 2017

Current collaborations with teams at P7

Alzheimer’s disease,DYRK1A, CBS (N. Janel)

NAT exploration with MD

simulation (F. Rodrigues-Lima)

Docking study into the CERT

transporter (C. Magnan)

RNAseq study on diabetes. Gut Microbiota peptides(C. Magnan)

Docking study: HSF2 (A. de Thonel and V. Lallemand-Mezger)

11-members including 7 permanents Permanent staff

PR. AC. Camproux

PR. O. Taboureau

CR. M. Petitjean

MCF. A. Badel

MCF. D. Flatters

MCF. L. Regad

Tech. C. Geneix

Post doc and Ph.D in 2018

ATER. V. Leroux

Ph.D N.. Cerisier

PhD. B. Boezio

PhD. P. Laville

Permanent staff

DR. P. Tuffery

MCF. G. Moroy

MCF. S. Murail

IG. J. Rey

IG. S. De Vries

Post doc and Ph.D in 2018Post doc: G. Postic

Team members

Thank you