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SAMPL6pKa Challenge
MehtapIsikChoderaLab,MSKCC
D3R2018WorkshopFebruary22nd,2018
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Why did we decide to organize a blind pKa prediction challenge?
SAMPL5 logD challenge indicated theimpact of prediction of the ionization statedistribution on the accuracy of logDpredictions.
Pickard,F.C.etal.BlindpredictionofdistributionintheSAMPL5challengewithQMbasedprotomer andpKa corrections.JournalofComputer-AidedMolecularDesign30,1087–1100(2016).
cyclohexane
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pKa predictions contribute to the errors in binding free energy predictions.
Case1:FailingtocorrectbindingfreeenergywithpKa penalty
P:LH+ LH+
L
ΔGbind,LH+
ΔGprotΔGbind =?
dominantaq.microstate
ΔGbind =ΔGbind,LH++ΔGprot
1.38kcal/mol errortofreeenergyper1unitoferrorinpKa
Case2:Failingtopredictligandprotonationstateinthecomplex
P:LH+ LH+
L
ΔGbind,LH+
ΔGprot
dominantaq.microstateP:L
Modelingthewrongionizationstateincomplexcancausesevereerrors.
ΔGbind,L
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Accurate prediction of pKas is a useful tool for computer aided drug design and lead optimization.
DuringleadoptimizationpKa valuesguide§ Improvingtargetpotency§ Reducingpotencyagainstundesiredtarget§ Modulatingsolubilityandlipophilicity§ ImprovingADMEproperties
PredictingpKas ofofdrug-likecompoundsarechallengingdueto
§ multipleprotonationsites
§ conjugatedsystemsandheterocycles
2nmicrostates
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We selected fragment-like molecules with heterocyclescommon in kinase inhibitors for SAMPL6.
Startingpoint:ZINC15kinasesubsetandanodyne compounds
Frequentheterocycles foundinFDA-approvedkinaseinhibitors
Murcko ringfragmentation N
pyridine
N
N
quinazoline
N
quinoline
N
N
pyrimidine
NHN
indazoleN
HN
imidazole
NHN
pyrazole
Otherheterocycles intheSAMPL6set
S
N
NH2
aminothiazole
S
thiophene
S
NN
1,3,4-thiadiazoleS
HN
OO
5-methylenethiazolidine-2,4-dione 4
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24 compounds are present in SAMPL6 pKa challenge
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In multiprotic compounds it is important to differentiate between macroscopic and microscopic pKas.
Fraczkiewicz R.(2013)InSilico PredictionofIonization.In:Reedijk,J.(Ed.)ElsevierReferenceModuleinChemistry,MolecularSciencesandChemicalEngineering.Waltham,MA:Elsevier.RuppM.etal.PredictingthepKa ofSmallMoleculesCombinatorial Chemistry&HighThroughputScreening,2011,14,307-327.
MacroscopicpKas
UV-metricpKasmayfailtocaptureallmacroscopicpKas.
MicroscopicpKas
+2+10-1
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Experimental pKa values for SAMPL6 were measured with Sirius T3.
§ Method:UV-absorbancespectrabasedpKa measurement§ Measurementrange:2-12§ 24smallkinaseinhibitorfragment-likemolecules§ Temperature:25°C§ Ionicstrength:150mM KCl solution§ 3independentreplicates(fromthesameDMSOstock)
DorothyLevorseTimothyRhodes
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PyridoxineHCl
UV-metric pKa measurements of multiprotic compounds lead to determination of macroscopic pKa values.
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pKas of water insoluble compounds were determined by extrapolation from multiple cosolvent experiments.
pKa isdeterminedbyYasuda-Shedlovsky extrapolation to0%cosolvent.
Acid/baseassignmentbasedonpKa shiftwithcosolvent doesnotprovidereliableevidenceforassigningpKa valuestoionizable groups,especiallyinmultiproticcompounds.
ApparentpKa ismeasuredat3cosolvent concentrations:30%,40%,and50%MeOH
Avdeef,A.etal.PH-metriclogP 11.pK adeterminationofwater-insolubledrugsinorganicsolvent–watermixtures.Journalofpharmaceuticalandbiomedicalanalysis20,631–641(1999). 9
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Suggestions for future pKa experimental data collection
§ ForfuturepKa challengeswithmultiprotic compounds,itisidealtouseexperimentalmethodsthatcanmeasuremicroscopicpKas,suchasNMR.
§ UV-metricpKa measurementswithSiriusT3donotprovideanystructuralinformationaboutmicrostates.
§ Monoprotic compoundsshouldbeprefered ifUV-metricorpotentiometricmethodsforpKa measurementswillbeused.
§ Acid/baseassignmentbasedonpKa shiftwithcosolvent isnotreliableinmultiprotic compounds.
§ Compoundpurityiscriticalforaccuracy.
§ CompoundsolubilityisthelimitingfactorforpKa measurementswithSiriusT3.
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TypeI- microscopicpKas andmicrostatesPredictingmicroscopicpKa valuesandrelatedmicrostatestructures.
32submissions
TypeII- microstatepopulationsasafunctionofpHPredictingfractionalmicrostatepopulationsbetweenpH2to12in0.1pHincrements.
27submissions
TypeIII- macroscopicpKasPredictingthevalueofmacroscopicpKas between2and12.
34submissions
Submission types and participation to pKa prediction challenge
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Analysis of macroscopic pKa predictions requires mapping of experimental pKas to predicted pKas.
ClosestMethod§ EachpredictedpKa ismatchedtoclosestexperimentalpKa value(minabsoluteerror).§ WhenmorethanonepredictedpKa matchtothesameexperimentalpKa,onlythe
predictedpka thathasthelowestabsoluteerroriskept.§ ExtrapredictedorexperimentalpKas areignored.
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HungarianMethod§ ExperimentalpKas andpredictedpKas arematchedfollowingHungarianalgorithm.§ Optimumglobalassignmentthatminimizeslinearsumofsquarederrorsofallpairwise
matches.
Kiril Lanevskij
ExperimentalpKas2.159.5811.02
PredictedpKas0.51.84
ExperimentalpKas2.159.5811.02
PredictedpKas0.51.84
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Overall performance of macroscopic pKa predictions
SubmissionID
RMSEvaluesspantherangeof0.7-5pKa units.
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Analysis of pKa predictions and future directions
MacroscopicpKa analysisresultscanbefoundinSAMPL6GitHub repository:
https://github.com/MobleyLab/SAMPL6
– Experimentalvs predictedpKa valuecorrelationplots– Errordistributionplotsforeachmolecule– Performancestatistics(RMSE,MAE,ME,R2,slope)
WewillkeepupdatingtheSAMPL6repositorywith:• AdditionalperformancecriteriaforpKa predictions• AnalysisofmicroscopicpKa valuesandmicrostatepopulations
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4 participants of the pKa challenge will present us their perspectives.
SubmissionID15
Qiao Zeng
NEXTSamarjeet Prasad(TypeI)
MarvinWaldman
Bogdan IorgaTOMORROW
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SAMPL6organizersandadvisors
DavidMobleyJohnChoderaAndreaRizziCaitlinBannanBasRustenburgMichaelChiuMichaelGilsonMichaelShirtsPaulCzodrowski
MerckPreformulation Department
TimothyRhodesDorothyLevorseBradSherborneHeatherWang
Acknowledgments
ParticipantsofpKa challengeCaitlinBannanRobertFraczkiewiczBogdan IorgaStefanKastKiril LanevskijChrisLoschenPhilippPrachtSamarjeet PrasadGeoffSkillmanRainerWilckenQiao Zeng
Tri-InstitutionalPhDPrograminChemicalBiologyDorisJ.HutchisonFellowship
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Chemical and Availability Criteria
Fragment-like Drug-like
• Tier1 compound• Availability of least 100 mg• Cheaper compounds in logP bins are prioritized.• Non-hazardous.• Anodyne (PAINs and reactive groups removed)
• At least 1 pKa in the interval 3 ≤ pKa ≤ 11• Multiple pKa’s at least 1 log unit part in selected pKa interval.• Minimum number of UV-chromophore unit: 8 • -1 < XlogP < 6
Number of rotatable bonds ≤ 3• 150 ≤ mw < 350
• Number of rotatable bonds ≤ 8• 350 ≤ mw ≤ 500
SMARTS: [n,o,c][c,n,o]cc
Selecting kinase inhibitor-like compounds
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pKas of water insoluble compounds were determined by extrapolation from multiple cosolvent experiments.
50%MeOH
40%MeOH
30%MeOH
pKa isdeterminedbyYasuda-Shedlovskyextrapolation to0%cosolvent.
Acid/baseassignmentbasedonpKa shiftwithcosolvent doesnotprovidereliableevidenceforassigningpKa valuestoionizable groups,especiallyinmultiprotic compounds.
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Analysis of macroscopic pKa predictionsOverall performance of macroscopic pKa predictions
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