Bender, A.

Cortes-Ciriano, I.

Lenselink, E.B.

Malliavin, T.E.

Murrell, D.S.

Westen, G.J.P. van

BMC Bioinformatics

PubMed

Leiden University Scholary Publications

MEETING ABSTRACT Open AccessApplications of proteochemometrics - fromspecies extrapolation to cell line sensitivitymodellingIsidro Cortes-Ciriano1*, Gerard JP van Westen2, Daniel S Murrell3, Eelke B Lenselink4, Andreas Bender3,Therese E Malliavin1From Third International Society for Computational Biology (ISCB) European Student Council Symposium2014Strasbourg, France. 6 September 2014BackgroundProteochemometrics (PCM) is a predictive bioactivitymodelling method which simultaneously models thebioactivity of multiple ligands against multiple targets.PCM permits exploration of the selectivity and promiscu-ity of ligands on biomolecular systems of different com-plexity. This includes proteins and even cell-line models[1,2]. The suitability of PCM to predict compound poly-pharmacology has been validated both retrospectively andin prospective experimental validation [1,2]. In practice,each ligand-target interaction is encoded by the concate-nation of ligand and target descriptor vectors used to traina single machine learning model. The inclusion of bothchemical and target information enables the extra- andinterpolation on the chemical and on the biological space.Therefore, PCM permits to predict compound bioactivitieson targets not present in the training phase [3].ResultsIn this contribution, we show a methodological advance-ment in the field [4], namely how Bayesian inference(Gaussian Processes) can be successfully applied in thecontext of PCM for (i) the prediction of compoundbioactivity along with the error estimation of the predic-tion; (ii) the determination of the applicability domainof a PCM model; and (iii) the inclusion of experimentaluncertainty of bioactivity measurements. We illustratehow the application of PCM can be useful in medicinalchemistry to concomitantly optimize compoundsselectivity and potency, in the context of two applicationscenarios: (a) modelling isoform-selective cyclooxygenaseinhibition; and (b) large-scale cancer cell line drug sensi-tivity prediction, where we benchmark the predictivesignal of basal gene expression, gene copy-number varia-tion, exome sequencing, and protein abundance data.We present the R package Chemically Aware ModelBuilder (camb) [5], which is able to perform the abovementioned modelling tasks. camb is an open sourceplatform for the generation of Structure-Activity andStructure-Property models. The functionalities of cambinclude: (i) standardisation of chemical structure repre-sentation, (ii) calculation of 905 one-dimensionaldescriptors and 14 fingerprints for small molecules, (iii)8 types of amino acid descriptors, (iv) 13 whole proteinsequence descriptors, and (iv) training, validation andvisualization of predictive models.ConclusionsOverall, the application of PCM in these two case sce-narios let us conclude that PCM is a suitable technique,on this data, to model the activity of ligands exhibitingdiverse bioactivity profiles across a panel of targets,which can range from protein binding sites (a), to can-cer cell-lines (b). The camb package constitutes a plat-form encompassing all steps for the generation ofpredictive models from chemical structures and theirassociated bioactivities/properties, which will providereproducibility and simplify the generation of predictivebioactivity/property models.1Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825;Département de Biologie Structurale et Chimie, 25, rue du Dr Roux, 75015,Paris, FranceFull list of author information is available at the end of the articleCortes-Ciriano et al. BMC Bioinformatics 2015, 16(Suppl 3):A4http://www.biomedcentral.com/1471-2105/16/S3/A4© 2015 Cortes-Ciriano et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Authors’ details1Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825;Département de Biologie Structurale et Chimie, 25, rue du Dr Roux, 75015,Paris, France. 2ChEMBL Group, European Molecular Biology LaboratoryEuropean Bioinformatics Institute, Wellcome Trust Genome Campus, CB101SD, Hinxton, Cambridge, UK. 3Centre for Molecular Science Informatics,Department of Chemistry, University of Cambridge, Cambridge, UK. 4Divisionof Medicinal Chemistry, Leiden Academic Center for Drug Research, Leiden,The Netherlands.Published: 13 February 2015References1. van Westen GJP, Wegner JK, Ijzerman AP, van Vlijmen HWT, Bender A:Proteochemometric Modeling as a Tool to Design Selective Compoundsand for Extrapolating to Novel Targets. Med Chem Commun 2011, 2:16-30.2. Cortes-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Mendez-Lucio O,Ijzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A:Polypharmacology Modelling Using Proteochemometrics (PCM): RecentMethodological Developments, Applications to Target Families, andFuture Prospects. Med Chem Commun .3. van Westen GJP, Wegner JK, Geluykens P, Kwanten L, Vereycken I,Peeters A, Ijzerman AP, van Vlijmen HWT, Bender A: Which Compound toSelect in Lead Optimization? Prospectively Validated ProteochemometricModels Guide Preclinical Development. PLoS ONE 2011, 6:e27518.4. Cortes-Ciriano I, van Westen GJP, Lenselink EB, Murrell DS, Bender A,Malliavin TE: Proteochemometric Modelling in a Bayesian framework. JCheminf 2014, 6:35.5. Murrell DS, Cortes-Ciriano I, van Westen GJP, Stott IP, Bender A, Malliavin TE,Glen RC: Chemically Aware Model Builder (camb): An R package forproperty and bioactivity modeling of small molecules.[http://www.github.com/cambDI/camb].doi:10.1186/1471-2105-16-S3-A4Cite this article as: Cortes-Ciriano et al.: Applications ofproteochemometrics - from species extrapolation to cell line sensitivitymodelling. BMC Bioinformatics 2015 16(Suppl 3):A4.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitCortes-Ciriano et al. BMC Bioinformatics 2015, 16(Suppl 3):A4http://www.biomedcentral.com/1471-2105/16/S3/A4Page 2 of 2

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

Isidro Cortes-Ciriano

Gerard JP van Westen

Daniel S Murrell

Eelke B Lenselink

Andreas Bender

Therese E Malliavin

Springer - Publisher Connector

MEETING ABSTRACT Open Access
Applications of proteochemometrics - from
species extrapolation to cell line sensitivity
modelling
Isidro Cortes-Ciriano1*, Gerard JP van Westen2, Daniel S Murrell3, Eelke B Lenselink4, Andreas Bender3,
Therese E Malliavin1
From Third International Society for Computational Biology (ISCB) European Student Council Symposium
2014
Strasbourg, France. 6 September 2014
Background
Proteochemometrics (PCM) is a predictive bioactivity
modelling method which simultaneously models the
bioactivity of multiple ligands against multiple targets.
PCM permits exploration of the selectivity and promiscu-
ity of ligands on biomolecular systems of different com-
plexity. This includes proteins and even cell-line models
[1,2]. The suitability of PCM to predict compound poly-
pharmacology has been validated both retrospectively and
in prospective experimental validation [1,2]. In practice,
each ligand-target interaction is encoded by the concate-
nation of ligand and target descriptor vectors used to train
a single machine learning model. The inclusion of both
chemical and target information enables the extra- and
interpolation on the chemical and on the biological space.
Therefore, PCM permits to predict compound bioactivities
on targets not present in the training phase [3].
Results
In this contribution, we show a methodological advance-
ment in the field [4], namely how Bayesian inference
(Gaussian Processes) can be successfully applied in the
context of PCM for (i) the prediction of compound
bioactivity along with the error estimation of the predic-
tion; (ii) the determination of the applicability domain
of a PCM model; and (iii) the inclusion of experimental
uncertainty of bioactivity measurements. We illustrate
how the application of PCM can be useful in medicinal
chemistry to concomitantly optimize compounds
selectivity and potency, in the context of two application
scenarios: (a) modelling isoform-selective cyclooxygenase
inhibition; and (b) large-scale cancer cell line drug sensi-
tivity prediction, where we benchmark the predictive
signal of basal gene expression, gene copy-number varia-
tion, exome sequencing, and protein abundance data.
We present the R package Chemically Aware Model
Builder (camb) [5], which is able to perform the above
mentioned modelling tasks. camb is an open source
platform for the generation of Structure-Activity and
Structure-Property models. The functionalities of camb
include: (i) standardisation of chemical structure repre-
sentation, (ii) calculation of 905 one-dimensional
descriptors and 14 fingerprints for small molecules, (iii)
8 types of amino acid descriptors, (iv) 13 whole protein
sequence descriptors, and (iv) training, validation and
visualization of predictive models.
Conclusions
Overall, the application of PCM in these two case sce-
narios let us conclude that PCM is a suitable technique,
on this data, to model the activity of ligands exhibiting
diverse bioactivity profiles across a panel of targets,
which can range from protein binding sites (a), to can-
cer cell-lines (b). The camb package constitutes a plat-
form encompassing all steps for the generation of
predictive models from chemical structures and their
associated bioactivities/properties, which will provide
reproducibility and simplify the generation of predictive
bioactivity/property models.
1Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825;
Département de Biologie Structurale et Chimie, 25, rue du Dr Roux, 75015,
Paris, France
Full list of author information is available at the end of the article
Cortes-Ciriano et al. BMC Bioinformatics 2015, 16(Suppl 3):A4
http://www.biomedcentral.com/1471-2105/16/S3/A4
© 2015 Cortes-Ciriano et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ details
1Institut Pasteur, Unité de Bioinformatique Structurale; CNRS UMR 3825;
Département de Biologie Structurale et Chimie, 25, rue du Dr Roux, 75015,
Paris, France. 2ChEMBL Group, European Molecular Biology Laboratory
European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10
1SD, Hinxton, Cambridge, UK. 3Centre for Molecular Science Informatics,
Department of Chemistry, University of Cambridge, Cambridge, UK. 4Division
of Medicinal Chemistry, Leiden Academic Center for Drug Research, Leiden,
The Netherlands.
Published: 13 February 2015
References
1. van Westen GJP, Wegner JK, Ijzerman AP, van Vlijmen HWT, Bender A:
Proteochemometric Modeling as a Tool to Design Selective Compounds
and for Extrapolating to Novel Targets. Med Chem Commun 2011, 2:16-30.
2. Cortes-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Mendez-Lucio O,
Ijzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A:
Polypharmacology Modelling Using Proteochemometrics (PCM): Recent
Methodological Developments, Applications to Target Families, and
Future Prospects. Med Chem Commun .
3. van Westen GJP, Wegner JK, Geluykens P, Kwanten L, Vereycken I,
Peeters A, Ijzerman AP, van Vlijmen HWT, Bender A: Which Compound to
Select in Lead Optimization? Prospectively Validated Proteochemometric
Models Guide Preclinical Development. PLoS ONE 2011, 6:e27518.
4. Cortes-Ciriano I, van Westen GJP, Lenselink EB, Murrell DS, Bender A,
Malliavin TE: Proteochemometric Modelling in a Bayesian framework. J
Cheminf 2014, 6:35.
5. Murrell DS, Cortes-Ciriano I, van Westen GJP, Stott IP, Bender A, Malliavin TE,
Glen RC: Chemically Aware Model Builder (camb): An R package for
property and bioactivity modeling of small molecules.[http://www.github.
com/cambDI/camb].
doi:10.1186/1471-2105-16-S3-A4
Cite this article as: Cortes-Ciriano et al.: Applications of
proteochemometrics - from species extrapolation to cell line sensitivity
modelling. BMC Bioinformatics 2015 16(Suppl 3):A4.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
Cortes-Ciriano et al. BMC Bioinformatics 2015, 16(Suppl 3):A4
http://www.biomedcentral.com/1471-2105/16/S3/A4
Page 2 of 2


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