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Computational approaches applied to pharmacological profiling

Team 2 led by Prs A-C.Camproux & O. Taboureau (Université Paris Diderot)


Team's publications / Organization Chart

Acquiring knowledge of the complete pharmacology profile has inspired new strategies to predict and to characterize drug-target interactions in order to improve the success rates of current drug discovery paradigms, i.e. increase the efficacy and reduce toxicity and adverse effects. With the production of massive amount of omics data (proteomics, transcriptomics, genomics, …) and the increase accessibility for academic to large biological and clinical data, the development of computational approaches to evaluate the drug safety and drug pharmacology is now possible.
Our team focuses on the analysis of the interactions between small molecules and proteins as well as the assessment of their effect on systems biology, i.e. Systems Chemical Biology. Our main objective is to gain a better understanding of the mechanism of action of small molecules across multiple scales of complexity in human health, with the development of computational approaches applied to pharmacological profiling.

Three complementaries tasks are driven in our team :
i) Chemical space (chemoinformatics, drug-design)
ii) Target space and pockets (structural bioinformatics)
iii) Biological space (data integration, systems pharmacology, pharmacogenomics)

Some recent studies outlined the need to combine protein target information (3D) with their binding sites (protein pockets or channels) together with chemical information (small molecules), in order to characterize and to predict the target-ligands interactions and the profiles of the partners of these interactions. Furthermore, the information obtained from i) and ii) are then combined with the biological and clinical data collected in iii)

Figure 1 : Our project is divided in three complementary themes.

(i) Chemical space (chemoinformatics, drug-design)

Ligands space profiling: optimal encoding of bioactive compounds molecular structures into relevant descriptors in order to analyse molecular diversity. Several softwares compute thousands molecular descriptors or fingerprints, but their physical meaning is often unclear. Some crucial ones are lacking: it is a bottleneck to get robust QSAR results and get reliable pockets-ligands interactions models.
We regularly develop new descriptors for the community, see http://petitjeanmichel.free.fr/itoweb.petitjean.freeware.html and references cited. A recent study concluded that spherical ligand shapes were physically unrealistic and that cylindrical shapes were better: either minimal height cylinders (relevant for most molecules), or minimal radius cylinders (Petitjean M., Appl. Alg. Eng. Comm. Comp., 2012), relevant for rather elongated molecules. This approach leads to select possible trajectories of potential active substrates in protein channels (Benkaidali et al., Bioinformatics 2014), which are useful in a virtual screening context.


(ii) Target space and pockets (Structural bioinformatics)

It was pointed out that the key properties of a good drug target had not been discussed sufficiently or well-defined and that «druggability» (i.e. it can be bound and modulated by a drug-like) have to be taken into account early in the drug discovery process. Indeed despite advances in both experimental and computational fields, around 60% of drug discovery projects fail because the target is not druggable.
Our main focus is to optimally characterize 3D targets and their pockets and to identify therapeutically-relevant drug targets that meet the criteria of being druggable. To simplify 3D structure analysis, we have developed a Structural Alphabet (SA-tools, Camproux et al, J. Mol. Biol. 1999, 2004) encoding any 3D structure into a 1D sequence of structural letters, relevant to analyze and to compare complete 3D structures or local fragments such as binding sites (Regad et al, BMC Bioinformatics, 2011, NAR, 2011).
This can be used to analyse functional site as it is expected their comparison can detect off-targets. In the aims to enhance understanding of the pocket, we develop tools to (i) estimate pockets (Benkaidali et al., Bioinformatics 2014), (ii) characterize pockets by original descriptors (Pérot et al, DDT 2011), (iii) cluster and compare pockets. Resulting descriptors are selected and statistically combined to predict «druggability». Our results to distinguish druggable pockets are very efficient using a set of «free» descriptors and different pocket estimation.


Pocket-ligand joint space analysis and statistical profiling (proteochemometrics, chemogenomics)

If a pocket is druggable, we can try to predict which geometrical and physico-chemical windows are required by the ligands (ligand profiles) to correspond to the properties of the pockets. The descriptors extracted from the target space can be used to propose target similarity and matched with ligand similarity extracted from the ligand space in order to predict potential interactions. By the statistical modeling of pocket-ligand pairs and the use of correlated pairs descriptors, a correlation between the nature of the pocket and its propensity to bind some ligand profiles and conversely, a correlation between the nature of the ligands and its propensity to bind some pocket profile has been established using machine learning methods (Pérot et al. Plos One 2013). This pocket-ligand profiling modeling aims to profile prediction from one partner of the interaction could open the ways to the prediction of interactions with off-target, toxic effects, side effects or facilitating the design of a small molecule for a given pocket.


(iii) Biological space (data integration, systems pharmacology, pharmacogenomics)

Data integration : Nowadays it is possible to get access to massive amount of data from proteomic, transcriptomic, toxicogenomic and genomic repositories. With the integration of such data, we can study the effect of a small molecule (drugs, foods, environmental chemicals) in a more holistic way, across multiple scales of complexity, from molecular, cellular to tissue and systems and assess the benefit and the risk of small molecules in human health. For example, we are involved in the development of databases such as ChemProt [Kjaerulff et al. NAR 2013] et HExpoChem [Taboureau et al. Bioinformatics 2013], which through the bioactivity profile of small molecules allow to explore and to predict the human disease phenotypes, the clinical effects and the human exposure associated to chemicals (and mixture of chemicals). One key issue in these approaches is to identify not only targets, but also pathways, biological networks (through protein-protein interactions) and the genes deregulations intensity in different cells or tissues contributing to the drug-phenotype associations. Therefore, in addition to the data integration and curation, we develop biostatistical approaches that are suitable to exploit the potential complementarities of such large and sparse data (Audouze K. et al , PLoS Comput. Biol. 2010).



Pharmacogenomics : One of the challenges in drug discovery is the development of a safe drug with a limited of side effects and adverse drug reactions. Unfortunately, a large variability in response to a drug is quite often seen in patients. Some patients will show a positive response whereas no effect (or even worse a strong side effect) will be seen for others patients. One of the explication is related to the polymorphism of drug targets as well as their complex interconnected biological networks.Therefore, with the acquisition of preclinical and clinical data combining with human genomics variations, we aim to study genetic variations (SNP, CNV) susceptible to be involved to the patient’s response to drug treatment. For example, we are actually working in this direction in collaboration with Danish universities and hospitals for patients with Attention Deficit Hyperactivity Disorder (ADHD).


Overall, our objective is to combine all theses data collected through several repositories and predicted by our computational approaches in order to provide insights into the drug pharmacology, drug safety and differentiation of patient drug response toward a personalized medecine.


Databases, freewares and webserver

- A disease chemical biology database : ChemProt-2 (Sonny Kim Kjærulff, et al.,Nucleic Acids Res. 2013)
- Identification of functional and structural motif : SA-Mot : (Regad L., et al., Nucleic Acids Research 2011)
- Estimation of canals and proteins cavities : CCCPP : (Benkaidali et al., Bioinformatics 2014)
- Ligand and protein Pocket descriptors : http://petitjeanmichel.free.fr/itoweb.petitjean.freeware.html

Teaching responsabilities at the University Paris-Diderot

Anne Badel - Responsible of la Licence de Biologie Informatique
Anne-Claude Camproux - Responsible of Master In Silico Drug Design (ISDD) - Responsible of module Statistique en expérimentation biologique (STAB) du DIU CESAM
Delphine Flatters - Co-responsible of Master 1 de Biologie Informatique
Leslie Regad - Co-responsible of Master 2 In Silico Drug Design (ISDD)
Olivier Taboureau - Co-responsible of Master 1 In Silico Drug Design (ISDD)

Selected recent publications

[1] Triki D , Cano Contreras ME , Flatters D , Visseaux B, Descamps D, Camproux AC , Regad L .
Analysis of the HIV-2 protease's adaptation to various ligands: characterization of backbone asymmetry using a structural alphabet.
scientific reports / 2018 /  8(1):710
[2] Taboureau O, Audouze K.
Human environmental disease network: a computational model to assess toxicology of contaminants
ALTEX / 2017 / 34(2):289-300
[3] Hiba Abi Hussein , Colette Geneix , Michel Petitjean , Alexandre Borrel , Delphine Flatters , Anne-Claude Camproux
Global vision of druggability issues, applications and perspectives
Drug Discovery Today / 2017 /  22(2):404-415
[4] Kugathas S, Audouze K , Ermler S, Orton F, Rosivatz E, Scholze M, Kortenkamp A.
Effects of common pesticides on prostglandin D2 (PGD2) inhibition in SC5 mouse sertoli cells, evidence of binding at the COX-2 active site, and implications for endocrine disruption.
Environ Health Perspect. / 2016 / 124(4):452-9.
[5] Ainoleena Turku, Alexandre Borrel, Teppo O. Leino, Lasse Karhu, Jyrki P. Kukkonen, and Henri Xhaard
Pharmacophore Model To Discover OX1 and OX2 Orexin Receptor Ligands
J. Med. Chem. / September 22, 2016 / 59(18):8263-75
[6] Orozco AA, Audouze K , Brunak S, Taboureau O.
In silico systems pharmacology to assess drugs therapeutic and toxic effects.
Current Pharmaceutical Design / September 06, 2016 / 22(46):6895-6902.
[7] Margineanu A, Chan JJ, Kelly DJ, Warren SC,Flatters D, Kumar S, Katan M, Dunsby CW, French PM
Screening for protein-protein interactions using Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM
scientific reports / June 24, 2016 / 6:28186
[8] Petitjean M., Camproux A.-C.
In Silico Medicinal Chemistry: Computational Methods to Support Drug Design
ChemMedChem / 2016 / 11[13] 1480-1481 Edited by Nathan Brown
[9] Sun H, Yue PY, Wang SR, Huo L, Zhao Y, Xie S, Kringelum JV, Lund O, Taboureau O , Zhou J, Wong RN, Fang WS.
Synthesis and Biological Evaluations of Cytotoxic and Antiangiogenic Triterpenoids-Jacaranone Conjugates.
Medicinal chemistry / 2016 / 12(8):775-785.
[10] Nzabonimpa GS, Rasmussen HB, Brunak S, Taboureau O
Investigating the impact of missense mutations in hCES1 by in silico structure-based approaches.
Drug Metabol Personal Ther. / February 25, 2016 / in press
[11] Kringelum J, Kjaerulff SK, Brunak S, Lund O, Oprea TI, Taboureau O.
ChemProt-3.0: a global chemical biology diseases mapping.
database / February 13, 2016 /  2016 Feb 13
[12] H. Abi Hussein , A. Borrel , L. Regad , D. Flatters , A. Badel , C. Geneix , M. Petitjean , A.-C. Camproux and O. Taboureau
System Biology : a new paradigm for drug discovery
The Practice of Medicinal Chemistry-Fourth Edition / 2015 / ISBN : 9780124172050 (Academic Press)
[13] Hiba Abi Hussein, Alexandre Borrel, Colette Geneix, Michel Petitjean, Leslie Regad, Anne-Claude Camproux
PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins
Nucleic Acids Research / 2015 /  i43(W1):W436-42.
[14] Borrel Alexandre,Regad Leslie, Xhaard Henri, Petitjean Michel, Camproux Anne-Claude
PockDrug: a model for predicting pocket druggability that overcomes pocket estimation uncertainties
J. Chem. Inf. Model. / April 27, 2015 / 55(4):882-95
[15] Benkaidali L., André F., Maouche B., Siregar P., Benyettou M., Maurel F., Petitjean M.
Computing cavities, channels, pores and pockets in proteins from nonspherical ligands models.
Bioinformatics / 2014 /  30[6]- 792800

Computational approaches applied to pharmacological profiling

Team leader :

CAMPROUX Anne-Claude
    (+33)1 57 27 83 77
    (+33)1 57 27 82 79

Permanent Members :

    (+33)1 57 27 83 78
    (+33)1 57 27 83 93
GENEIX Colette
    (+33)1 57 27 83 79
    (+33)1 57 27 84 34
REGAD Leslie
    (+33)1 57 27 82 72

Non Permanent Members :

BOEZIO Baptiste
    (+33)1 57 27 83 87
    (+33)1 57 27 82 81
    (+33)1 57 27 83 96
Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72

Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72

Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72