.1.1 Introduction
.1.1.1 Available bioactivity data is growing: but can we make sense of it?
T
he cost of developing new drugs has been continuously increasing in recent years and it is now estimated to be in the order of $1.8 billion per drug. In addition, price pressure from health care providers has been increasing and there is a growing relevance of more targeted medicine. Hence, the blockbuster model of the pharmaceutical industry is being challenged [Akella and DeCaprio (2010); Paul et al. (2010)]. However, at the same time the amount of bioactivity data available both inside companies as well as in the public domain has significantly increased, for example with introduction of ChEMBL and PubChem Bioassay [Gaulton et al.(2012); Wang et al. (2012)]. This trend can be expected to only gain further speed in the future. The question now arises how this growing amount of bioactivity data can be used in real-world drug discovery and chemical biology projects, both to make drug discovery in commercial settings more efficient, but also to understand on a more fundamental level how we can use data in order to design a ligand with desired properties in a biological system.
Predictive bioactivity methods, such as Quantitative Structure-Activity Relation-ship (QSAR) models, are based upon the compound similarity principle [Bender and Glen (2005); Willett (2009)]. However, it has been shown that the activity of a compound against a single target is not sufficient to understand its actions in a bio-logical system. In fact, promiscuity is intrinsic to chemical compounds [Mestres et al.
(2008, 2009)], bioactivity against related targets frequently needs to be considered for efficacy ofe.g. CNS-active drugs and anti-cancer drugs [Bianchi and Botzolakis (2010);
Shoshan and Linder (2004)], and promiscuity has been used to anticipate side-effects [Bender et al. (2007)]. Hence, only the simultaneous modelling of both the chemical and the target domain, across a series of protein targets, permits the meaningful mining of the compound-target interaction space [Bieler and Koeppen (2012)].
The term chemogenomics comprises techniques capable to capitalize on this huge amount of bioactivity data by considering compound and target information, in order to find unknown interactions between (new) compounds and their (new) targets [Bredel and Jacoby (2004); Jacoby (2013)]. Proteochemometrics (PCM) modelling describes methods where a computational description from the ligand side of the system is combined with a description of the biological side being studied and both are related to a particular readout of interest [Lapinsh, Prusis, and Gutcaits (2001);
Westen et al. (2011a)].
In this context, ligands are typically small molecules although biologics also have been explored, whereas the biological parameters in the model can comprise protein binding sites, but also e.g. gene expression levels of particular cell-lines. The readout describes the biological effect of a particular ligand on the protein or cell-line of interest (such as an IC50 value of this particular combination of compound and biological system). Additionally, PCM relates to personalized medicine as it can predict the effect of a ligand on a complex biological system, e.g. cell-line, from genotypic information [Cortes-Ciriano, I et al. (2015)].
.1.1.2 Synergy between ligand and target space
An analysis of the drug-target interaction network demonstrated that a given ligand interacts with six protein targets on average at therapeutic concentrations [Mestres et al. (2009)]. Targets with correlated bioactivity profiles might be related or dis-tant from a sequence similarity standpoint. It has been recently shown that the classification of class AGPCRs based on ligand activity differs considerably from that obtained when using a classic description of proteins based upon sequence alignments [Lin et al. (2013); Westen and Overington (2013)]. Hence, full sequence similarity from multiple sequence alignments would not generally correlate with similar ligand affinity. Nevertheless, kinases exhibiting a sequence identity higher than60% tend to have similar ATP-binding sites and hence they tend to be inhibited by similar compounds [Vieth et al. (2005)]. Similarly, compound binding is more conserved between human and rat orthologous proteins with respect to paralogues [Kruger and Overington (2012); Westen et al. (2012)]. Thus, to better understand intra-family and inter-species selectivity both the target and the compound space need to be considered simultaneously.
In ligand space, chemogenomic approaches relying only on ligand data have shown that there is an unequal distribution of ligand data. This is due to the fact that some target classes (e.g. GPCRs or kinases) have been traditionally regarded as more interesting from a medicinal chemistry standpoint, and are thus overrepresented in bioactivity databases [Gregori-Puigjané and Mestres (2008b)]. Moreover, while
some chemogenomic methods implicitly consider target information using bioactivity profiles of groups of similar ligands,i.e. the interaction between these compounds and a panel of targets, they are outperformed by techniques that explicitly consider target information [Gregori-Puigjané and Mestres (2008a); Rognan (2007)]. In addition, bioactivity profiles for related compounds are not always available.
In target space, techniques were employed which benefit from the structural or sequence information available and rely on groups of related targets with the aim to identify possible off-target effects and drug specificity for a particular target of interest [Rognan (2007)]. Based on the inverse similarity principle, related proteins are likely to interact with similar compounds. As in the previous case, the unavailability of data also constitutes a limitation for target-based chemogenomics.
The combination of ligand and target data allows the creation of predictive models that can rationalizee.g. viral or cancer cell-line selectivity, whereas models exclusively based on ligands cannot explain the role of the target in selectivity [Westen et al.
(2011b)]. Merging data from ligand and target sources into the frame of a single machine learning model allows the prediction of the most suitable pharmacological treatment for a given genotype (personalized medicine), which ligand-only and protein-only approaches are not able to perform. This is precisely the underlying principle in proteochemometrics (PCM), which employs both ligand and target features simultaneously, and which therefore enables the deconvolution of both the target and the chemical spaces in parallel [Lapinsh, Prusis, and Gutcaits (2001);
Westen et al. (2011a)].
Dataset(data-points)ReceptorLigandDe- scriptorsTargetDescriptorBioactivity typeMachine Learning Tech- nique Insilico Model Validation Prospective Valida- tion?
Remarks,InferencesReference PDBbind[Wangetal.(2004)] (1,300)
1,300protein-ligandcom- plexesAtom-type basedAtom-typebasedKd,KiRFY-sc, OoBV,EVNoIncreasingthetrainingset sizeimprovesmodelpre- dictability Ballesterand Mitchell(2010) ProLINTdatabase [ShandarA](3,595)
62KinasesStructuralfrag- mentsand2D autocorrelation vectors Sequence-based structuralfragments andaminoacid sequenceautocorre- lation IC50SVM3-foldCV, EVNoSVMbasedonautocor- relationdescriptorsper- formbetterthanfragment- basedapproaches Fernandez,Ahmad, andSarai(2010) PDBbind[Wangetal.(2004)] (1,255)DiverseProteinsProperty- encodedshape distributions
Property-encoded shapedistributionsKd,KiSVM5-foldCV, EVNoTrainingsetenrichment andexpansionenhances predictionaccuracy Das,Krein,andBren- eman(2010) StanfordHIVDrugResis- tancedatabase[Rheeetal. (2003)](4,495)
728ReversetranscriptasesDragondescrip- tors[AMauri (2006)]
z-scales[Sandberget al.(1998)]IC50PLS7-foldCV, EVNoReceptor-ligandand receptor-receptorcross- termsimprovedmodel performance Junaidetal.(2010) ImmuneEpitopeDatabase [Vitaetal.(2010)](31,992)
12HLA-DRB1proteinsz-scales[Sand- bergetal. (1998)]
z-scales[Sandberget al.(ibid.)]IC50PLS7-foldCV, EVNoIdentifiedproteinresidues andpeptidepositionsfor bindingpredictions DimitrovandGarnev (2010) Karamanetal.(2008)(12,046)317humanKinasesDragondescrip- tors[AMauri (2006)]
z-scales,[Sandberg etal.(1998)]Amino- acidcomposition, sequenceorderand CTD KdPLS,SVM, KNN,DTDouble CVNoSVMoutperformsallma- chinelearningapproachesLapinshandWik- berg(2010) CSAR-NRCHiQ[Kramerand Gedeck(2011b)]
346protein-ligandcomplexesAtomcountsAtomcountsKdMLRRSNoDistancedependentatom descriptorsmakethere- gressionmodelsmorero- bust.
KramerandGedeck (ibid.) Goldstandardset(1,933)313diseases(OMIM) [Hamoshetal.(2002)]DiverseDrug- Drugsimilarity measures
Disease-Diseasesimi- laritymeasureClassifier scoreLogistic regression classifier 10-fold CV,EVNoPossibilitiestoinclude patient-specificgeneex- pressionprofilesmakethe modelssuitableforphar- macogenomicsstudies Gottlieb,Ruppin, andSharan(2011) Sc-PDB[Kellenbergeretal. (2006)](2,882)
581targetsHashedfinger- printsProteinsequenceand 3-DstructurebasedActives/ inactivesSVM5-foldCV, EVNoStructure-basedap- proachesperformbet- terthansequence-based approaches [MeslamaniandRog- nan(2011)] GLIDAdatabase[Okunoetal. (2006)](5,207)andGVKKi- nasedatabase(15,616)
317GPCRsand143KinasesDragondescrip- tors[AMauri (2006)]
Proteinsequenceand feature-basedKi,IC50, EC50SVM5-fold9com- pounds for ADRB2 Highlyactivecompounds predictedbySVMnot identifiedby Yabuuchietal.(2011) 5in- hibitors forEGFR
ligand-based/structure- basedapproaches TibotecBVBA(4,024)14HIVRTCircularfinger- printsHashedfingerprintsEC50SVMY-Sc,E CV,
317novel predic- tionswere experi- mentally verified ViralmutantsPCMmod- elscanassistthedevelop- mentdrugsforHIVinfec- tion
Westenetal.(2013b) LosoV Bioinfo-DB[Weilletal.(2011)] (336,678)OxytocinreceptorMACCSstruc- turalkeysFingerprintsbased onthepropertiesof aminoacidsinactive site
Actives/ inactivesRF10-fold CV,EVBiological evaluation of37com- pounds PCMmodelsyieldbet- terhitsthanthecon- ventionalvirtualscreening procedures.
Weilletal.(ibid.) (2hits)
Dataset(data-points)ReceptorLigandDe- scriptorsTargetDescriptorBioactivity typeMachine Learning Tech- nique Insilico Model Validation Prospective Valida- tion?
Remarks,InferencesReference PDBbindrefinedset(1,387)23proteinfamilies(1,387pro- teins)Atom-type basedAtom-typebased, Distance-dependent proteinligandatom typepairs
KdMLR,PLS5-foldCV, LCONoInclusionofdescriptors fromPCMmodelspredict freeenergiesmoreaccu- ratelythandockingpro- grams KramerandGedeck (2011a) StanfordHIVDrugResis- tanceDatabase(4,794pro- teaseand4,495RTsequence- inhibitorcombinations)
828HIV-1proteasevariantsGRINDalign- mentindepen- dentdescrip- tors[Pastor etal.(2000)] IC50,Kd, KiDCSVM and DCNB RS,EVNoDCSVMsprovidebetter activitypredictionNiijima,Shiraishi, andOkuno(2012) BindingDB[Liuetal.(2007)] (1,275)
PCADTV,EVVEGFR2 inhibition byMeben- dazole andCad- herin11 inhibition byCele- coxibwere verified.
TFMSPCMapproachcan assistindrugreposition- ingstudies
6novel com- pounds were experi- mentally identified Additionoforthologue informationincreased modelquality
Westenetal.(2012) CHEMBL8[Gaultonetal. (2012)](81,689;43,965)
136GPCRsand176KinasesMACCSkeysSequencedescriptorsKi,IC50SVM5-foldCV, EVNoFeatureselectionim- provedthepredictive accuracyofthemodels Chengetal.(2012) GVKBiosciencesdatabase (GVKBiosciencesPrivate Limited.Hyderabad.India., 2007)(628,120)
238ClassAGPCRsChemicalker- nelsbased onECFP-6 fingerprints andDragon descriptors Proteinkernelsbased onfulllength,TM andloopsequences
Dataset(data-points)ReceptorLigandDe- scriptorsTargetDescriptorBioactivity typeMachine Learning Tech- nique Insilico Model Validation Prospective Valida- tion?
Remarks,InferencesReference Virco(300,000)HIVmutants(10,700NNRTI, 10,500NRTI,27000PI)Circularfinger- printsz-scales[Sandberget al.(1998)]IC50SVMY-Sc,5- foldCV, EV
NoPhenotypicresistancefor novelmutantscanbepre- dictedviaPCM Westenetal.(2013a) GPCRDB[Vrolingetal. (2011)](310)
9humanamineGPCRsPhysicalprop- ertiesandtopo- logicalindices ofcompounds z-scales[Sandberget al.(1998)]andTM identitydescriptors KiSVRand GP
10-fold CV,EVNoSVRissuperiortoGP.Gaoetal.(2013) TMidentitydescriptors performbetterthanz- scalesdescriptors PubChemBioAssaydataset [Wangetal.(2012)](63,391)
5CYP450isoformsMolecular signaturesCTDAC50KNN, SVMand RF CV,EVNoNon-linearmethods(SVM andRF)performbetter.Lapinshetal.(2013) BindingandPDSPKi database[Liuetal.(2007)] (13,079)
514humantargetsTopologicalfin- gerprintsAmino-acidcomposi- tionandCTDKiRFand NBOOB,5- foldCV, EV NoRandomforestsoutper- formKNN,SVM,NBand BPN Caoetal.(2013a) InvitroOATPmodulation data(2,000)OATP1B1andCircularfinger- printsz-scales[Sandberg etal.(1998)]and feature-basedProtFP
KiRFOOB,EVAgreement between experi- mentand prediction 4classmodelsaresuperior to2-classmodelsandpro- videinformationaboutse- lectivity
DeBruynetal.(2013) OATP1B3 [Davisetal.(2011);Karaman etal.(2008);Metzetal.(2011)] datasets
50KinasesMold2[Hong etal.(2008)], OpenBabel [O’Boyleetal. (2011)]andvol- surf[Cruciani etal.(2000)] descriptors Knowledge-based fields123andwa- termap124derived fields Kd/KiPLS7-fold CV,EV ,LOTO, Y-Sc NoField-basedmodelsaresu- periortosequence-based models
Subramanianetal. (2013) Table.1.1:PCMstudiespublishedbetween2010and2013.ThewideapplicabilityofPCMisevidencedbythe increasedcoverageofdrugtargetsinthestudiesofthelastthreeyears.Althoughtraditionaldrugtargets, suchasGPCRsorkinases,arestillwidelyrepresented,newapplications(e.g.themodellingofviral genotypesorpharmacogenomics)aregaininggroundsteadily.BPN-BackPropagationNetworks,BS -BootstrappingValidation,CTD-Compositionandtransitionofaminoacidproperties,CV-Cross- Validation,DCNB-DualComponentNaiveBayes,DCSVM-DualComponentSupportVectorMachines, DT-DecisionTrees,DTV-DecoyTestValidation,ENR-ElasticNetRegression,EV-ExternalValidation, GP-GaussianProcesses,KNN-k-NearestNeighbors,LCO-Leave-Cluster-OutValidation,LOTO- Leave-One-Target-OutValidation,NB-Naive-Bayes,NN-NeuralNetwork,MLR-MultipleLinear Regression,OOB-Out-Of-BagValidation,PCA-PrincipalComponentAnalysis,PLS-PartialLeast Squares,RandomForest-RF,RS-RandomSplitting,SVM-SupportVectorMachines,SVR-Support VectorRegression,Y-Sc-Y-Scrambling.
.1.1.3 PCM as a practical approach to use chemogenomics data
PCM modelling is a computational technique which combines both ligand infor-mation and target inforinfor-mation within a single predictive model in order to predict an output variable of interest (usually the activity of a molecule in a particular bio-logical assay) [Lapinsh, Prusis, and Gutcaits (2001); Westen et al. (2011a)]. It is this combination of orthogonous information that sets PCMapart from both QSARand chemogenomics [Horst et al. (2011); Rognan (2007)]. Generally, the term targetrefers to proteins since the majority of PCM models in the literature have been devoted to the study of the activity of compounds on protein targets. Yet, target can also refer to a certain protein binding pocket (to allow distinction between binding modes, protein conformations, or allosteric/orthosteric binding) [Bahar, Chennubhotla, and Tobi (2007)], or even to a cell-line [Menden et al. (2013)]. Each binding site and each binding mode can be regarded (computationally) as a different target.
A PCMmodel is trained on a data set composed of a series of targets and com-pounds, where compounds have been measured on as many targets as possible (illustrated in Figure.1.1). The simultaneous modelling of the target and the ligand space permits to better understand complex drug-target interactions (e.g. selectivity) [Keiser et al. (2007); Ning, Rangwala, and Karypis (2009); Paolini et al. (2006); Wasser-mann, Geppert, and Bajorath (2009)] than it would be possible with chemogenomics.
Indeed, the simultaneous modelling of compound and target data allows to assess the effect of target and chemical variability can be evaluated (e.g. protein mutations or the effect of chemical substructures on bioactivity). Thus, the aim ofPCM is the complete modelling of the compound-target interaction space (Figure.1.1), including also the prediction of the bioactivity of novel compounds on yet untested targets.
Initial attempts to incorporate description of several proteins and their ligands in a singleQSAR model involved modelling of the interaction between mutated glu-cocorticoid receptors andDeoxyribonucleic Acid (DNA)[Tomic, Nilsson, and Wade (2000); Zilliacus et al. (1992)]. The first full scalePCMstudy involving different pro-teins was devoted to the interaction of chimeric melanocortin receptors with chimeric peptides at Uppsala University [Prusis et al. (2001)]. The name proteochemometrics was coined later by the same research group [Lapinsh, Prusis, and Gutcaits (2001)].
Since thenPCM has been applied on various diverse data sets (Table.1.1) [Bock and Gough (2005); Lapinsh et al. (2002)]. While the current chapter will focus on recent developments in the field between 2010 and 2013, a comprehensive discussion of PCM-related work before2010has been presented in a previous review by Westen et al. (2011a), to which we would like to refer the reader.