Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free

34 Pages·2017·6.54 MB·English

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Predicting resistance of clinical Abl
1
mutations to targeted kinase inhibitors
2
using alchemical free-energy
3
calculations
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KevinHauser1,ChristopherNegron1,StevenK.Albanese2,3,SoumyaRay1,Thomas
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Steinbrecher4,RobertAbel1,JohnD.Chodera3,LingleWang1*
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1Schrödinger,NewYork,NY10036;2GerstnerSloanKetteringGraduateSchool,MemorialSloan
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KetteringCancerCenter,NewYork,NY10065;3ComputationalandSystemsBiologyProgram,
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SloanKetteringInstitute,MemorialSloanKetteringCancerCenter,NewYork,NY10065;
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4Schrödinger,GmbH,Q723,68161Mannheim,Germany
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*Forcorrespondence: [email protected](LW)
11
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Abstract Thetherapeuticeffectoftargetedkinaseinhibitorscanbesignificantlyreducedbyintrinsicor
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acquiredresistancemutationsthatmodulatetheaffinityofthedrugforthekinase.Incancer,themajorityof
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missensemutationsarerare,makingitdifficulttopredicttheirimpactoninhibitoraffinity.Thiscomplicates
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thepracticeofprecisionmedicine,pairingofpatientswithclinicaltrials,anddevelopmentofnext-generation
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inhibitors. Here,weexaminethepotentialforalchemicalfree-energycalculationstopredicthowkinase
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mutationsmodulateinhibitoraffinitiestoAbl,amajortargetinchronicmyelogenousleukemia(CML).We
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findthesecalculationscanachieveusefulaccuracyinpredictingresistanceforasetofeightFDA-approved
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kinaseinhibitorsacross144clinically-identifiedpointmutations, achievingarootmeansquareerrorin
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21 bindingfreeenergychangesof1.11.2kcal/mol(95%confidenceinterval)andcorrectlyclassifyingmutations
0.9
22 asresistantorsusceptiblewith8994%accuracy. SincethesecalculationsarefastonmodernGPUs, this
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benchmarkestablishesthepotentialforphysicalmodelingtocollaborativelysupporttherapidassessment
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andanticipationofthepotentialforpatientmutationstoaffectdrugpotencyinclinicalapplications.
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Targetedkinaseinhibitorsareamajortherapeuticclassinthetreatmentofcancer.Atotalof38selective
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smallmoleculekinaseinhibitorshavenowbeenapprovedbytheFDA[1],including34approvedtotreat
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cancer,andperhaps50%ofallcurrentdrugsindevelopmenttargetkinases[2]. Despitethesuccessof
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selectiveinhibitors,theemergenceofdrugresistanceremainsachallengeinthetreatmentofcancer[3–10]
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andhasmotivatedthedevelopmentofsecond-andthenthird-generationinhibitorsaimedatovercoming
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recurrentresistancemutations[11–15].
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Whileanumberofdrugresistancemechanismshavebeenidentifiedincancer(e.g.,inductionofsplice
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variants[16],oralleviationoffeedback[17]),inherentoracquiredmissensemutationsinthekinasedomain
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ofthetargetoftherapyareamajorformofresistancetotyrosinekinaseinhibitors(TKI) [10,18,19].Oncology
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isenteringanewerawithmajorcancercentersnowdeepsequencingtumorstorevealgeneticalterations
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thatmayrendersubclonalpopulationssusceptibleorresistanttotargetedinhibitors[20],buttheuseof
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thisinformationinprecisionmedicinehaslaggedbehind.Itwouldbeofenormousvalueinclinicalpractice
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ifanoncologistcouldreliablyascertainwhetherthesemutationsrenderthetargetoftherapyresistantor
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susceptibletoavailableinhibitors;suchtoolswouldfacilitatetheenrollmentofpatientsinmechanism-based
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a Rare Recurrentb
Missense mutations
from MSK-IMPACT
% % %
5 1 4
8. 5. 7.
6 8 8
mutation recurrence
c Abl kinase mutations
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24 wild-type 45 31
e 21 mutant
c18 residues within 5 Å
n
e15 residues within 8 Å
ur12
cc 9
o 6
3
0
Cys Gln Pro Ser Trp Arg Asn Asp His Ala Glu Gly Lys Met Thr Tyr Phe Ile Leu Val
Figure1. Relativealchemicalfree-energycalculationscanbeusedtopredictaffinitychangesofFDA-approved
selectivekinaseinhibitorsarisingfromclinically-identifiedmutationsintheirtargetsoftherapy. (a)Missense
mutationstatisticsderivedfrom10,336patientsamplessubjectedtoMSK-IMPACTdeepsequencingpanel[20]showthat
68.5%ofmissensekinasemutationsincancerpatientshaveneverbeenobservedpreviously,while87.4%havebeen
observednomorethantentimes.(b)Tocomputetheimpactofaclinicalpointmutationoninhibitorbindingfreeenergy,
athermodynamiccyclecanbeusedtorelatethefreeenergyofthewild-typeandmutantkinaseintheabsence(top)
andpresence(bottom)oftheinhibitor.(c)Summaryofmutationsstudiedinthiswork.Frequencyofthewild-type(dark
green)andmutant(green)residuesforthe144clinically-identifiedAblmutationsusedinthisstudy(seeTable1fordata
sources).Alsoshownisthefrequencyofresidueswithin5Å(lightblue)and8Å(blue)ofthebindingpocket.Thenumber
ofwild-typePheresidues(n=45)andmutantValresidues(n=31)exceededthelimitsofthey-axis.
baskettrials[21,22],helpprioritizecandidatecompoundsforclinicaltrials,andaidthedevelopmentof
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next-generationinhibitors.
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Thelongtailofrarekinasemutationsfrustratespredictionofdrugresistance
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While some cancer missense mutations are highly recurrent and have been characterized clinically or
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biochemically,a“longtail”ofraremutationscollectivelyaccountsforthemajorityofclinicallyobserved
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missensemutations(Figure1a),leavingcliniciansandresearcherswithoutknowledgeofwhetherthese
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uncharacterizedmutationsmightleadtoresistance. Whilerules-basedandmachinelearningschemes
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arestillbeingassessedinoncologycontexts,workinpredictingdrugresponsetomicrobialresistancehas
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shownthatraremutationspresentasignificantchallengetoapproachesthatseektopredictresistance
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totherapy[23]. Clinicalcancermutationsmayimpactdrugresponsethroughavarietyofmechanisms
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byalteringkinaseactivity, ATPaffinity, substratespecificities, andtheabilitytoparticipateinregulatory
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interactions,compoundingthedifficultiesassociatedwithlimiteddatasetsthatmachinelearningapproaches
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face.Inparallelwithcomputationalapproaches,high-throughputexperimentaltechniquessuchasMITE-
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Seq[24]havebeendevelopedtoassesstheimpactofpointmutationsondrugresponse. However,the
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complexityofdefiningselectionschemesthatreliablycorrelatewithinvivodrugeffectivenessandlong
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turn-aroundtimesmightlimittheirabilitytorapidlyandreliablyimpactclinicaldecision-making.
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Alchemicalfree-energymethodscanpredictinhibitorbindingaffinities
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Physics-based approaches could be complementary to machine-learning and experimental techniques
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inpredictingchangesinTKIaffinityduetomutationswithfewornopriorclinicalobservations. Modern
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atomisticmolecularmechanicsforcefieldssuchasOPLS3[25],CHARMM[26],andAMBERFF14SB[27]have
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reachedasufficientlevelofmaturitytoenabletheaccurateandreliablepredictionofreceptor-ligandbinding
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free energy. Alchemical free-energy methods permit receptor-ligand binding energies to be computed
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rigorously,includingallrelevantentropicandenthalpiccontributions[28].Encouragingly,kinase:inhibitor
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bindingaffinitieshavebeenpredictedusingalchemicalfree-energymethodswithmeanunsignederrorsof
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1.0kcal/molforCDK2,JNK1,p38,andTyk2[29,30].Beyondkinases,alchemicalapproacheshavepredicted
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thebindingaffinityofBRD4inhibitorswithmeanabsoluteerrorsof0.6kcal/mol[31].Alchemicalmethods
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havealsobeenobservedtohavegoodaccuracy(0.6kcal/molmeanunsignederrorforTyk2tyrosinekinase)
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in the prediction of relative free energies for ligand transformations within a complex whose receptor
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geometrywasgeneratedusingahomologymodel[32].
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Alchemicalapproachescanpredicttheimpactofproteinmutationsonfreeenergy
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Alchemicalfree-energycalculationshavealsobeenusedtopredicttheimpactofmutationsonprotein-
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proteinbinding[33]andproteinthermostabilities[34].Recentworkhasfoundthatproteinmutationscan
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bepredictedtobestabilizingordestabilizingwithaclassificationaccuracyof71%acrosstenproteinsand62
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mutations [35].TheimpactofGlytoD-Alamutationsonproteinstabilitywaspredictedusinganalchemical
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approach with a similar level of accuracy [36]. Recently, one study has hinted at the potential utility of
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alchemicalfree-energycalculationsinoncologybypredictingtheimpactofasingleclinicalmutationonthe
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bindingfreeenergiesoftheTKIsdasatinibandRL45[37].
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AssessingthepotentialforphysicalmodelingtopredictresistancetoFDA-approvedTKIs
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Here,weaskwhetherphysicalmodelingtechniquesmaybeusefulinpredictingwhetherclinically-identified
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kinasemutationsleadtodrugresistanceordrugsensitivity.Weperformstate-of-the-artrelativealchemical
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free-energycalculationsusingFEP+[29],recentlydemonstratedtoachievesufficientlygoodaccuracytodrive
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thedesignofsmall-moleculeinhibitorsforabroadrangeoftargetsduringleadoptimization[28–30,38].We
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comparethisapproachagainstafastbutapproximatephysicalmodelingmethodimplementedinPrime[39]
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(an MM-GBSA approach) in which an implicit solvent model is used to assess the change in minimized
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interactionenergyoftheligandwiththemutantandwild-typekinase.Weconsiderwhetherthesemethods
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canpredictaten-foldreductionininhibitoraffinity(correspondingtoabindingfreeenergychangeof1.36
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kcal/mol)toassessbaselineutility.Asabenchmark,wecompileasetofreliableinhibitorbindingdatafor
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144clinically-identifiedmutantsofthehumankinaseAbl,animportantoncologytargetdysregulatedin
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cancerslikechronicmyelogenousleukemia(CML),forwhichsix[1]FDA-approvedTKIsareavailable. The
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resultsofthisbenchmarkdemonstratethepotentialforFEP+topredicttheimpactthatmutationsinAbl
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90 kinasehaveondrugbinding,andaclassificationaccuracyof8994%(forallstatisticalmetricsreportedinthis
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91 paper,the95%confidenceintervals(CI)isshownintheformof(𝑥𝑢𝑝𝑝𝑒𝑟)),anRMSEof1.11.2kcal/mol,andan
𝑙𝑜𝑤𝑒𝑟 0.9
92 MUEof0.760.88kcal/molwasachieved.
0.65
Results
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FreeenergycalculationscanrecapitulatetheimpactofclinicalmutationsonTKIaffinity
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Alchemicalfree-energycalculationsutilizeaphysics-basedapproachtoestimatethefreeenergyoftransform-
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ingonechemicalspeciesintoanother,incorporatingallenthalpicandentropiccontributionsinaphysically
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consistentmanner[28,40–42].Whilerelativealchemicalfree-energycalculationshavetypicallybeenem-
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ployedinoptimizingsmallmoleculesforincreasedpotencyorselectivity[29,38,42,43],acomplementary
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alchemicalapproachcanbeusedtocomputetheimpactofpointmutationsonligandbindingaffinities.
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Figure1bdepictsthethermodynamiccyclethatillustrateshowweusedrelativefreeenergycalculationsto
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computethechangeinligandbindingfreeenergyinresponsetotheintroductionofapointmutationinthe
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kinase.Intheboundlegofthecycle,thewild-typeprotein:ligandcomplexistransformedintothemutant
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protein:ligandcomplex.Intheunboundlegofthecycle,theapoproteinistransformedfromwild-typeinto
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mutant.Toachievereliablepredictionswithshortrelativefree-energycalculations,areliablereceptor:ligand
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complexstructureisrequiredwiththeassumptionthatthebindingmodeofwild-typeandmutantare
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similar.Inthiswork,high-resolutionco-crystalstructuresofwild-typeAblboundtoaninhibitorwereutilized
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whenavailable.Toassessthepotentialforusingdockedinhibitorposes,wealsoexaminedtwosystemsfor
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whichco-crystalstructureswerenotavailable(Abl:erlotinibandAbl:gefitinib)anduseddockingtogenerate
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initialcoordinates.
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Table1. PublicΔpIC datasetsfor144Ablkinasemutationsandeighttyrosinekinaseinhibitors(TKIs)with
50
correspondingwild-typeco-crystalstructuresusedinthisstudy.
(kcal/mol) (kcal/mol)
TKI Nmut R S PDB |Δ𝐺max−Δ𝐺min| Source Δ𝐺WT
axitinib 26 0 26 4wa9 𝟤.𝟢𝟧 [44] −𝟪.𝟥𝟧
bosutinib 21 4 17 3ue4 𝟤.𝟩𝟫 [45] −𝟫.𝟪𝟣
dasatinib 21 5 16 4xey 𝟧.𝟢𝟪 [45] −𝟣𝟣.𝟫𝟦
imatinib 21 5 16 1opj 𝟤.𝟣𝟨 [45] −𝟫.𝟣𝟫
nilotinib 21 4 17 3cs9 𝟥.𝟪𝟪 [45] −𝟣𝟢.𝟩𝟦
ponatinib 21 0 21 3oxz 𝟣.𝟢𝟢 [45] −𝟣𝟣.𝟩𝟢
subtotal 131 18 113
erlotinib 7 1 6 Dockto3ue4 𝟣.𝟩𝟥 [46] −𝟫.𝟩𝟩
gefitinib 6 0 6 Dockto3ue4 𝟣.𝟩𝟫 [46] −𝟪.𝟪𝟦
total 144 19 125
N :TotalnumberofmutantsforwhichΔpIC datawasavailable.
mut 50
NumberofResistant,Suscepticalmutantsusing10-foldaffinitychangethreshold.
PDB:SourcePDBID,orDockto3ue4,whichused3ue4asthereceptorforGlide-SPdockinginhibitorswithoutco-crystal
structure.
Δ𝐺 :Bindingfreeenergyofinhibitortowild-typeAbl,asestimatedfromIC data.
WT 50
110 CompiledΔpIC50 dataprovidesabenchmarkforpredictingmutationalresistance
111 Toconstructabenchmarkevaluationdataset,wecompiledatotalof144ΔpIC50measurementsofAbl:TKI
affinities,summarizedinTable1,takingcaretoensureallmeasurementsforanindividualTKIwerereported
112
113 inthesamestudyfromexperimentsrununderidenticalconditions.131ΔpIC50measurementswereavailable
acrossthesixTKIswithavailableco-crystalstructureswithwild-typeAbl—26foraxitiniband21forbosutinib,
114
115 dasatinib,imatinib,nilotinib,andponatinib. 13ΔpIC50measurementswereavailableforthetwoTKIsfor
whichdockingwasnecessarytogenerateAbl:TKIstructures—7forerlotiniband6forgefitinib.Foradded
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diversity,thissetincludesTKIsforwhichAblisnottheprimarytarget—axitinib,erlotinib,andgefitinib.
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118 Experimental ΔpIC50 measurements for wild-type and mutant Abl were converted to ΔΔG in order
to make direct comparisons between physics-based models and experiment. However, computation
119
of experimental uncertainties were required to understand the degree to which differences between
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predictionsandexperimentaldataweresignificant.SinceexperimentalerrorestimatesformeasuredIC s
121 50
werenotavailableforthedatainTable1,wecomparedthatdatatoothersourcesthathavepublished
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IC s for the same mutations in the presence of the same TKIs (Figure 2a,b,c). Cross-comparison of 97
123 50
124 experimentallymeasuredΔΔGsderivedfromcellviabilityassayIC50dataledtoanestimateofexperimental
125 variabilityof0.320.36kcal/molroot-meansquareerror(RMSE)thatdescribedtheexpectedrepeatabilityof
0.28
themeasurements. BecausemultiplefactorsinfluencetheIC asidefromdirecteffectsonthebinding
126 50
127 affinity—thefocusofthisstudy—wealsocomparedΔΔGsderivedfromΔpIC50swiththosederivedfrom
128 bindingaffinitymeasurements(ΔK𝑑)forwhichdataforalimitedsetof27mutationswasavailable(Figure2d);
129 thelargercomputedRMSEof0.811.04kcal/molrepresentsanestimateofthelowerboundoftheRMSEto
0.59
130 theIC50-derivedΔΔGsthatwemighthopetoachievewithFEP+orPrimewhencomparingsamplestatistics
directly.
131
MostclinicalmutationsdonotsignificantlyreduceTKIpotency
132
Themajorityofmutationsdonotleadtoresistancebyour10-foldaffinitylossthreshold: 86.3%ofthe
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co-crystalset(n=113)and86.8%ofthetotalset(n=125).Resistancemutations,whicharelikelytoresultina
134
failureoftherapy,constitute13.7%oftheco-crystalset(n=18)and13.2%ofthetotalsetofmutations(n=19).
135
136 TheΔpIC50sforall144mutationsaresummarizedinTable5—Table10intheSupplementaryInformation.
137 Twomutationsexceededthedynamicrangeoftheassays(IC50>10,000nM);asthesetwomutationsclearly
raiseresistance,weexcludedthemfromquantitativeanalysis(RMSEandMUE)butincludedthemintruth
138
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inter-lab ∆IC ∆K & ∆IC
50 d 50
a b c d
N (samples) 39 N (samples) 29 N (samples) 29 N (samples) 27
RMSE (kcal/mol) 0.4500..5345 RMSE (kcal/mol) 0.4100.5.248 RMSE (kcal/mol) 0.4900..5470 RMSE (kcal/mol) 0.8110.0.549
Figure2. Cross-comparisonoftheexperimentallymeasuredeffectsthatmutationsinAblkinasehaveonligand
binding,performedbydifferentlabs.ΔΔGwascomputedfrompubliclyavailableΔpIC orΔpK measurementsand
50 𝑑
thesevaluesofΔΔGwerethenplottedandtheRMSEbetweenthemreported.(a)ΔpIC measurements(X-axis)from
50
[45]comparedwithΔpIC measurements(Y-axis)from[47].(b)ΔpIC measurements(X-axis)from[45]comparedwith
50 50
ΔpIC measurements(Y-axis)from[48].(c)ΔpIC measurements(X-axis)from[47]comparedwithΔpIC measurements
50 50 50
(Y-axis)from[46].(d)ΔpIC measurements(X-axis)from[45]comparedwithΔpK measurements(Y-axis)from[46]using
50 𝑑
non-phosphorylatedAblkinase.Scatterploterrorbarsina,b,andcare±standarderror(SE)takenfromthecombined97
inter-labΔΔGsderivedfromtheΔpIC measurements,whichwas0.320.36;theRMSEwas0.450.51kcal/mol.Scatterplot
50 0.28 0.39
errorbarsindarethe±standarderror(SE)ofΔΔGsderivedfromΔpIC andΔpK fromasetof27mutations,whichis
50 𝑑
0.580.74;theRMSEwas0.811.04kcal/mol.
0.42 0.59
tableanalysesandclassificationmetrics(accuracy,specificity,sensitivity).
139
HowaccuratelydoesphysicalmodelingpredictaffinitychangesforclinicalAblmutants?
140
Frompriorexperiencewithrelativealchemicalfree-energycalculationsforliganddesign,goodinitialreceptor-
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ligandgeometrywascriticaltoobtainingaccurateandreliablefreeenergypredictions[29],sowefirstfocused
142
onthe131mutationsinAblkinaseacrosssixTKIsforwhichwild-typeAbl:TKIco-crystalstructureswere
143
144 available. Figure3summarizestheperformanceofpredictedbindingfree-energychanges(ΔΔG)forall
131 mutants in this set for both a fast MM-GBSA physics-based method that only captures interaction
145
energiesforasinglestructure(Prime)andrigorousalchemicalfree-energycalculations(FEP+).Scatterplots
146
147 compareexperimentalandpredictedfree-energychanges(ΔΔG)andcharacterizetheabilityofthesetwo
techniquestopredictexperimentalmeasurements.Statisticaluncertaintyinthepredictionsandexperiment-
148
to-experimentvariabilityintheexperimentalvaluesareshownasellipseheightandwidthsrespectively.
149
Thevalueforexperimentalvariabilitywas0.32kcal/mol,whichwasthestandarderrorcomputedfromthe
150
cross-comparisoninFigure2.ForFEP+,theuncertaintywastakentobethestandarderroroftheaverage
151
fromthreeindependentrunsforaparticularmutation,whilePrimeresultsaredeterministicandarenot
152
contaminatedbystatisticaluncertainty(seeMethods).
153
154 TobetterassesswhetherdiscrepanciesbetweenexperimentalandcomputedΔΔ𝐺ssimplyarisefor
knownforcefieldlimitationsormightindicatemoresignificanteffects,weincorporatedanadditionalerror
155
156 modelinwhichtheforcefielderrorwastakentobearandomerror𝜎FF≈0.9kcal/mol,avalueestablished
form previous benchmarks on small molecules absent conformational sampling or protonation state
157
issues[25].ThinerrorbarsinFigure2representtheoverallestimatederrorduetoboththisforcefielderror
158
andexperimentalvariabilityorstatisticaluncertainty.
159
Toassessoverallquantitativeaccuracy,wecomputedbothroot-mean-squarederror(RMSE)—whichis
160
rathersensitivetooutliers,andmeanunsignederror(MUE).ForPrime,theMUEwas1.191.43kcal/moland
161
0.93
theRMSEwas1.752.04kcal/mol.FEP+,thealchemicalfree-energyapproach,achievedasignificantlyhigher
162
1.45
levelofquantitativeaccuracywithanMUEof0.790.92 kcal/molandanRMSEof1.081.29 kcal/mol. Notably,
163
0.66 0.89
alchemicalfreeenergycalculationscomesubstantiallycloserthanMMGBSAapproachtotheminimum
164
165 achievableRMSEof0.811.04kcal/mol(duetoexperimentalerror;Figure2)forthisdataset.
0.59
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Abl:TKI Co-Crystal Structures
Abl:axitinib Abl:bosutinib Abl:dasatinib Abl:imatinib Abl:nilotinib Abl:ponatinib
PDB: 4WA9 PDB: 3UE4 PDB: 4XEY PDB: 1OPJ PDB: 3CS9 PDB: 3OXZ
Prime FEP+
Bootstrap
MUE 1.43 0.92
1.19 0.79
(kcal/mol) 0.93 0.66
RMSE 2.04 1.29
1.75 1.08
(kcal/mol) 1.45 0.89
Bayesian Analysis
MUE 1.61 0.89
∗1.42 0.77
(kcal/mol) 1.25 0.66
RMSE 2.02 1.11
1.78 0.96
(kcal/mol) 1.57 0.83
Prime FEP+
Bootstrap
Prediction Prediction
0.78 0.93
Accuracy 0.70 0.89
0.63 0.83
s r Specificity 0.730.81 0.950.98 s r
0.65 0.90
nt Sensitivity 0.500.75 0.500.71 nt
0.27 0.25
e e
S 83 30 S 107 6
m m
Bayesian Analysis
ri ri
e Accuracy 0.710.75 0.910.94 e
p R 9 9 0.68 0.86 p R 9 9
x 0.75 0.95 x
E Specificity 0.73 0.71 0.93 0.90 E
0.77 1.00
Sensitivity 0.57 0.70
0.38 0.47
Figure3. Comparisonofexperimentally-measuredbindingfree-energychanges(ΔΔG)for131clinicallyobserved
mutationsand6selectivekinaseinhibitorsforwhichco-crystalstructuresofwild-typekinasewithinhibitorare
available.Toppanel:Abl:TKIco-crystalstructuresusedinthisstudywithlocationsofclinicalmutantsforeachinhibitor
highlighted(bluespheres)inrelationtoTKI(greensticks)onthecorrespondingAbl:TKIwild-typecrystalstructure.Middle
panel:ScatterplotsshowPrimeandFEP+computedΔΔGcomparedtoexperiment,withellipsewidthsandheights(±𝜎)
forexperimentandFEP+respectively. Thereddiagonallineindicateswhenpredictionequalsexperiment,whilethe
yellowshadedregionindicatesareainwhichpredictedΔΔGiswithin1.36kcal/molofexperiment(correspondingto
aten-folderrorinpredictedaffinitychange). ΔΔG<0denotesthemutationincreasesthesusceptibilityofthekinase
totheinhibitor,whileΔΔG>0denotesthemutationincreasestheresistanceofthekinasetotheinhibitor. Thetwo
mutationsthatwerebeyondtheconcentrationlimitoftheassay(T315I/dasatinib,L248R/imatinib)werenotplotted;129
pointswereplotted. Truthtablesofclassificationaccuracy,sensitivityandspecificityusingtwo-classes. Bottompanel:
TruthtablesandclassificationresultsincludeT315I/dasatinibandL248R/imatinib;131pointswereused.ForMUE,RMSE,
andtruthtableperformancestatistics,sub/superscriptsdenote95%CIs.Variabilityintheexperimentaldataisshown
asellipsewidthsanduncertaintyinourcalculationsisshownasellipseheights.Experimentalvariabilitywascomputed
asthestandarderrorbetweenIC -derivedΔΔ𝐺measurementsmadebydifferentlabs,0.32kcal/mol. Thestatistical
50
uncertaintyinthePrimecalculationswaszerobecausethemethodisdeterministic(𝜎 =0),whiletheuncertaintyinthe
cal
FEP+calculationswasreportedasthestandarderror,𝜎 ,ofthemeanofthepredictedΔΔ𝐺sfromthreeindependent
cal
runs.Tobetterhighlighttrueoutliersunlikelytosimplyresultfromexpectedforcefielderror,wepresumeforcefielderror
(𝜎 ≈0.9kcal/mol[25])alsobehavesasarandomerror,andrepresentthetotalestimatedstatisticalandforcefielderror
√FF
( 𝜎2 +𝜎2 )asverticalerrorbars.Thehorizontalerrorbarsfortheexperiment(𝜎 )wascomputedasthestandard
FF exp∕cal exp
errorbetweenΔpIC andΔK measurements,0.58kcal/mol.ForPrime,*MUEhighlightsthattheBayesianmodelyields
50 𝑑
avalueforMUEthatisnoticeablylargerthanMUEforobserveddataduetothenon-GaussianerrordistributionofPrime.
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Howaccuratelycanphysicalmodelingclassifymutationsassusceptibleorresistant?
166
While quantitative accuracy (MUE, RMSE) is a principle metric of model performance, an application of
167
potentialinterestistheabilitytoclassifymutationsascausingresistancetoaspecificTKI.Tocharacterizethe
168
accuracywithwhichPrimeandFEP+classifiedmutationsinamannerthatmightbetherapeuticallyrelevant,
169
weclassifiedmutationsbytheirexperimentalimpactonthebindingaffinityassusceptible(affinityformutant
170
171 isdiminishedbynomorethan10-fold,ΔΔ𝐺≤1.36kcal/mol)orasresistant(affinityformutantisdiminished
172 byleast10-fold,ΔΔ𝐺>1.36kcal/mol).Summarystatisticsofexperimentalandcomputationalpredictionsof
theseclassesareshowninFigure2(bottom)astruthtables(alsoknownasconfusionmatrices).
173
Thesimpleminimum-energyscoringmethodPrimecorrectlyclassified9ofthe18resistancemutations
174
inthedatasetwhilemerely83ofthe113susceptiblemutationswerecorrectlyclassified(30falsepositives).
175
Incomparison,thealchemicalfree-energymethodFEP+,whichincludesentropicandenthalpiccontributions
176
aswellasexplicitrepresentationofsolvent,correctlyclassified9ofthe18resistancemutationswhilea
177
vastmajority,107,ofthesusceptiblemutationswerecorrectlyclassified(merely6falsepositives). Prime
178
achievedaclassificationaccuracyof0.700.78,whileFEP+achievedanaccuracythatissignificantlyhigher(both
179
0.63
inastatisticalsenseandinoverallmagnitude),achievinganaccuracyof0.890.93.Sensitivity(alsocalledtrue
180
0.83
positiverate)andspecificity(truenegativerate)arealsoinformativestatisticsinassessingtheperformanceof
181
abinaryclassificationscheme.ForPrime,thesensitivitywas0.500.75,whilethespecificitywas0.730.81.Toput
182
0.27 0.65
thisinperspective,aCMLpatientbearingaresistancemutationinthekinasedomainofAblhasanequal
183
chanceofPrimecorrectlypredictingthismutationwouldberesistanttooneoftheTKIsconsideredhere,
184
185 whileifthemutationwassusceptible,thechanceofcorrectpredictionwouldbe∼73%. Bycontrast,the
classificationsensitivityandspecificityofFEP+aresubstantiallybetter.ForFEP+,thesensitivitywas0.500.71
186
0.25
whilethespecificitywas0.950.98.ThereisaveryhighprobabilitythatFEP+willcorrectlypredictthatoneof
187
0.90
theeightTKIsstudiedherewillremaineffectiveforapatientbearingasusceptiblemutation.
188
Howsensitiveareclassificationresultstochoiceofcutoff?
189
Previousworkby[49]utilizedTKI-specificthresholdsfordasatinib,imatinib,andnilotinib[49],whichwere
190
191 ∼2 kcal/mol. Supplementary Figure 6 shows that when our classification threshold was increased to a
20-foldchangeinbinding(1.77kcal/mol),FEP+correctlyclassified8ofthe13resistantmutationsandwith
192
athresholdof100-foldchangeinbinding(2.72kcal/mol),FEP+correctlyclassifiedtheonlytworesistant
193
mutations (T315I/dasatinib and T315I/nilotinib). With the extant multilayered and multinodal decision-
194
makingalgorithmsusedbyexperiencedoncologiststomanagetheirpatients’treatment,orbymedicinal
195
chemiststoproposecandidatecompoundsforclinicaltrials,theresistantorsusceptiblecutoffscouldbe
196
selectedwithmorenuancethanthesimple10-foldaffinitythresholdweconsiderhere.Withalargeraffinity
197
changecutoff,forexample,theaccuracywithwhichphysicalmodelspredictresistancemutationsincreases
198
199 beyond90%(Figure6). Forthealchemicalapproach,thetwo-classaccuracywas0.930.97 whenanaffinity
0.89
changecutoffof20-foldwasusedwhileusinganaffinitychangecutoffof100-foldfurtherimprovedthe
200
201 two-classaccuracyto0.981.00.
0.96
Bayesiananalysiscanestimatethetrueerror
202
Thestatisticalmetrics—MUE,RMSE,accuracy,specificity,andsensitivity—discussedabovearebasedon
203
analysis of the apparent performance of the observed modeling results compared with the observed
204
experimentaldataviasamplestatistics. However, thisanalysisconsidersalimitednumberofmutants,
205
andbothmeasurementsandcomputedvaluesarecontaminatedwithexperimentalorstatisticalerror.
206
Toobtainanestimateoftheintrinsicperformanceofourphysicalmodelingapproaches, accountingfor
207
knownpropertiesoftheexperimentalvariabilityandstatisticaluncertainties,weusedahierarchicalBayesian
208
model(detailedintheMethods)toinferposteriorpredictivedistributionsfromwhichexpectationsand95%
209
predictiveintervalscouldbeobtained.TheresultsofthisanalysisarepresentedinFigure3(centraltables).
210
FEP+issignificantlybetterthanPrimeatpredictingtheimpactofmutationsonTKIbindingaffinities,asthe
211
apparentperformance(usingtheoriginalobservations)aswellastheintrinsicperformance(whereBayesian
212
analysiswasusedtocorrectforstatisticaluncertaintyorexperimentalvariation)werewell-separatedoutside
213
their95%confidenceintervalsinnearlyallmetrics.ApplyingtheBayesianmodel,theMUEandRMSEfor
214
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215 FEP+was0.770.89kcal/moland0.961.11kcal/molrespectively(N=129).Fortheclassificationmetricsaccuracy,
0.66 0.83
216 specificity,andsensitivity,themodelyields0.910.94,0.930.95,and0.701.00 respectively(N=131). Theintrinsic
0.86 0.90 0.47
217 RMSEandMUEofPrimewas1.782.02kcal/moland1.421.61kcal/mol(N=129)respectively,andtheclassification
1.57 1.25
218 accuracy,specificity,andsensitivitywas0.710.75,0.730.75,and0.570.77respectively(N=131).TheintrinsicMUEof
0.68 0.71 0.38
PrimeobtainedbythisanalysisislargerthantheobservedMUEreflectingthenon-Gaussian,fat-tailederror
219
distributionsofPrimeresults.
220
Istheimpactofpointmutationsondrugbindingequallywell-predictedforthesixTKIs?
221
TheimpactofpointmutationsondrugbindingarenotequallywellpredictedforthesixTKIs. Figure4
222
expandstheresultsinFigure3onaTKI-by-TKIbasistodissecttheparticularmutationsinthepresenceof
223
aspecificTKI.PrimeandFEP+correctlypredictedthatmostmutationsinthisdataset(N=26)donotraise
224
resistancetoaxitinib,thoughFEP+predicted2falsepositivescomparedwith5falsepositivesbyPrime.The
225
226 MUEandRMSEofFEP+wasexcellentforthisinhibitor,0.550.75 kcal/moland0.760.98 kcal/molrespectively.
0.37 0.50
Whiletheclassificationresultsforbosutinib(N=21)wereequallywellpredictedbyPrimeasbyFEP+,FEP+was
227
stillabletoachievesuperior,butnothighlysignificant,predictiveperformanceforthequantitativemetrics
228
229 MUEandRMSE,whichwere0.961.45 kcal/moland1.412.02 kcal/molrespectively(FEP+)and1.131.78 kcal/mol
0.55 0.78 0.60
230 and1.802.52kcal/molrespectively(Prime).Fordasatinib,FEP+achievedanMUEandRMSEof0.761.12kcal/mol
0.90 0.49
231 and1.071.56kcal/molrespectivelywhereastheresultswere,asexpected,lessquantitativelypredictivefor
0.58
Prime(N=20).Theresultsforimatinibweresimilartothoseofdasatinibabove,wheretheMUEandRMSE
232
233 forFEP+were0.821.14 kcal/moland1.091.42 kcal/molrespectively(N=20). Nilotinib,aderivativeofimatinib,
0.52 0.70
234 ledtonearlyidenticalquantitativeperformanceresultsforFEP+withanMUEandRMSEof0.821.14kcal/mol
0.54
235 and1.061.41kcal/molrespectively(N=21).Similartoaxitinib,ponatinibpresentedaninterestingcasebecause
0.63
therewerenomutationsinthisdatasetthatraisedresistancetoit,perhapsduetoitspromiscuousnature
236
237 asamulti-targetedinhibitor. DespitethewidedynamicrangeinthecomputedvaluesofΔΔGforother
238 inhibitors,FEP+correctlypredictedaverynarrowrangeofΔΔGsforthisdrug.ThisisreflectedintheMUE
239 andRMSEof0.871.17kcal/moland1.091.48kcal/molrespectively,whicharein-linewiththeMUEsandRMSEs
0.61 0.70
fortheotherTKIs.
240
Understandingtheoriginofmispredictions
241
Resistancemutationsthataremispredictedassusceptible(falsenegatives)areparticularlycriticalbecause
242
theymightmisleadtheclinicianordrugdesignerintobelievingtheinhibitorwillremaineffectiveagainst
243
thetarget.WhichresistancemutationsdidFEP+mispredictassusceptible?Ninemutationswereclassified
244
245 byFEP+tobesusceptiblewhenexperimentallymeasuredΔpIC50dataindicatethemutationsshouldhave
increasedresistanceaccordingtoour10-foldaffinitycutoffforresistance. Notably,the95%confidence
246
intervalsforfiveofthesemutationsspannedthe1.36kcal/molthreshold,indicatingthesemisclassifications
247
arenotstatisticalsignificantwhentheexperimentalerrorandstatisticaluncertaintyinFEP+areaccountedfor:
248
249 bosutinib/L248R(ΔΔG𝐹𝐸𝑃+=1.3210..9740kcal/mol),imatinib/E255K(ΔΔG𝐹𝐸𝑃+=0.433−.20.519kcal/mol),imatinib/Y253F
250 (ΔΔG𝐹𝐸𝑃+=0.9510..6246kcal/mol),andnilotinib/Y253F(ΔΔG𝐹𝐸𝑃+=0.8910..6099kcal/mol).Thebosutinib/V299Lmutation
251 wasalsonotsignificantbecausetheexperimentalΔΔG,1.702.33kcal/mol,includedthe1.36kcal/molcutoff;
1.08
252 thevalueofΔΔGpredictedbyFEP+forthismutationwas0.911.02kcal/mol,theupperboundofthepredicted
0.79
valuewaswithin0.06kcal/molofthelowerboundoftheexperimentalvalue.
253
Fourmutations,however,weremisclassifiedtoadegreethatisstatisticallysignificantgiventheir95%con-
254
fidenceintervals:dasatinib/T315A,bosutinib/T315I,imatinib/E255V,andnilotinib/E255V.Fordasatinib/T315A,
255
althoughtheT315Amutationsforbosutinib,imatinib,nilotinib,andponatinibwerecorrectlyclassifiedas
256
susceptible,thepredictedfreeenergychangesforthesefourTKIswereconsistentlymuchmorenegative
257
thanthecorrespondingexperimentalmeasurements,justasfordasatinib/T315A,indicatingtheremightbea
258
genericdrivingforcecontributingtotheerrorsinT315AmutationsforthesefiveTKIs.Ablisknowntobeable
259
toadoptmanydifferentconformations(includingDFG-inandDFG-out),anditisverylikelythattheT315A
260
mutationwillinduceconformationalchangesintheapoprotein[50],whichwasnotadequatelysampledin
261
therelativelyshortsimulations,leadingtotheerrorsforT315AmutationsfortheseTKIs.Bycomparison,
262
theT315Imutationsforaxitinib,bosutinib,imatinib,nilotinib,andponatinibwereallaccuratelypredicted
263
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withtheexceptionofbosutinib/T315Ibeingtheonlymisprediction,suggestinganissuespecifictobosutinib.
264
Thecomplexelectrostaticinteractionsbetweenthe2,4-dichloro-5-methoxyphenylringinbosutinibandthe
265
adjacentpositivelychargeamineofthecatalyticLys271maynotbeaccuratelycapturedbythefixed-charge
266
OPLS3forcefield,leadingtothemispredictionforbosutinib/T315Imutation.
267
Insufficientsamplingmightalsobelietheimatinib/E255Vandnilotinib/E255Vmispredictionsbecause
268
theyresideinthehighlyflexibleP-loop.SinceE255Vwasachargechangemutation,weutilizedaworkflow
269
thatincludedexplicitions(seeMethods).Thedistributionoftheseionsinthesimulationboxaroundthe
270
solutemightnothaveconvergedtotheirequilibriumstateontherelativelyshorttimescaleofoursimulations
271
(5ns),andtheinsufficientsamplingofiondistributionscoupledwithP-loopmightleadtomispredictionof
272
thesetwomutations.
273
HowaccuratelycantheimpactofmutationsbepredictedfordockedTKIs?
274
Toassessthepotentialforutilizingphysics-basedapproachesintheabsenceofahigh-resolutionexperimen-
275
talstructure,wegeneratedmodelsofAblboundtotwoTKIs—erlotinibandgefinitib—forwhichco-crystal
276
structures with wild-type kinase are not currently available. In Figure 5, we show the Abl:erlotinib and
277
Abl:gefitinibcomplexesthatweregeneratedusingadockingapproach(Glide-SP,seeMethods).Thesetwo
278
structureswerealignedagainsttheco-crystalstructuresofEGFR:erlotinibandEGFR:gefinitibtohighlightthe
279
structuralsimilaritiesbetweenthebindingpocketsofAblandEGFRandtheTKIbindingmodeinAblversus
280
EGFR.AsanadditionaltestofthesensitivityofFEP+tosystempreparation,asecondsetofAbl:erlotiniband
281
Abl:gefitinibcomplexeswasgeneratedinwhichcrystallographicwatercoordinatesweretransferredtothe
282
dockedinhibitorstructures(seeMethods).
283
Alchemicalfree-energysimulationswereperformedon13mutationsbetweenthetwocomplexes;7
284
mutationsforerlotiniband6mutationsforgefitinib. ThequantitativeaccuracyofFEP+inpredictingthe
285
286 valueofΔΔGwasexcellent—MUEandRMSEof0.580.86kcal/moland0.801.09kcal/molrespectivelyifcrystal
0.33 0.44
287 watersareomitted,and0.500.78kcal/moland0.690.96kcal/molifcrystalwaterswererestoredafterdocking.
0.26 0.33
Encouragingly,theseresultsindicatethatourinitialmodelsofAblboundtoerlotinibandgefitinibwere
288
reliablebecausetheaccuracyanddependabilityofourFEP+calculationswerenotsensitivetocrystallographic
289
waters.Oursecondaryconcernwastheaccuracywithwhichtheapproachclassifiedmutationsasresistant
290
orsusceptible.
291
Whiletheresultspresentedin(Figure5)indicatethatFEP+iscapableofachievinggoodquantitative
292
accuracy when a co-crystal structure is unavailable, it is important to understand why a mutation was
293
predictedtobesusceptiblebutwasdeterminedexperimentallytoberesistant.F317Iwastheonemutation
294
thatincreasedresistancetoerlotinib(orgefitinib)becauseitdestabilizedbindingbymorethan1.36kcal/mol—
295
296 1.351.67kcal/mol(gefitinib)and1.581.90kcal/mol(erlotinib),butthemagnitudeoftheexperimentaluncertainty
1.03 1.26
meansweareunabletoconfidentlydiscernwhetherthismutationinducesmorethan10-foldresistance
297
toeitherTKI.Therefore,theonemisclassificationbyFEP+inFigure5isnotstatisticallysignificantandthe
298
classificationmetricspresentedthereunderestimatethenominalperformanceofthisalchemicalfree-energy
299
method.
300
Discussion
301
Physics-basedmodelingcanreliablypredictwhenamutationelicitsresistancetotherapy
302
TheresultspresentedinthisworkaresummarizedinTable2. Theperformancemetricssummarizedin
303
Table2indicatesthatthesetof131mutationsforthesixTKIsinwhichco-crystalstructureswereavailableis
304
onparwiththecompleteset(144mutations),whichincludedresultsbasedonAbl:TKIcomplexesgenerated
305
fromdockingmodels.Theperformanceresultsforthe13mutationsforthetwoTKIs(erlotinibandgefitinib)
306
inwhichco-crystalstructureswereunavailableexhibitedgoodquantitativeaccuracy(MUEandRMSE)and
307
goodclassificationpower.
308
309 Overall(N=144),theMM-GBSAapproachPrimeclassifiedmutationswithgoodaccuracy(0.720.79)and
0.64
310 specificity (0.740.82) while the alchemical approach FEP+ was a significant improvement in classification
0.66
311 accuracy(0.890.94)andspecificity(0.950.98).ThequantitativeaccuracywithwhichPrimewasabletopredictthe
0.83 0.91
312 experimentallymeasuredchangeinAbl:TKIbinding(N=142)characterizedbyRMSEandMUEwas1.721.99
1.45
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Prime FEP+
axitinib
Prediction Prediction
s r s r
nt nt
me S 21 5 me S 24 2
eri eri
p R 0 0 p R 0 0
Ex Ex
Accuracy 0.81 Accuracy 0.92
Specificity 0.81 Specificity 0.92
MUE(kcal/mol) 1.19 Sensitivity NA MUE(kcal/mol) 0.55 Sensitivity NA
RMSE(kcal/mol) 1.96 RMSE(kcal/mol) 0.76
bosutinib
Prediction Prediction
s r s r
nt nt
me S 14 3 me S 15 2
eri eri
p R 1 3 p R 3 1
Ex Ex
Accuracy 0.81 Accuracy 0.76
Specificity 0.82 Specificity 0.88
MUE(kcal/mol) 1.13 Sensitivity 0.75 MUE(kcal/mol) 0.96 Sensitivity 0.25
RMSE(kcal/mol) 1.80 RMSE(kcal/mol) 1.41
dasatinib
Prediction Prediction
s r s r
nt nt
me S 14 2 me S 15 1
eri eri
p R 1 4 p R 1 4
Ex Ex
Accuracy 0.86 Accuracy 0.90
Specificity 0.88 Specificity 0.94
MUE(kcal/mol) 1.05 Sensitivity 0.80 MUE(kcal/mol) 0.76 Sensitivity 0.80
RMSE(kcal/mol) 1.48 RMSE(kcal/mol) 1.07
imatinib
Prediction Prediction
s r s r
nt nt
me S 8 8 me S 16 0
eri eri
p R 4 1 p R 3 2
Ex Ex
Accuracy 0.43 Accuracy 0.86
Specificity 0.50 Specificity 1.00
MUE(kcal/mol) 1.32 Sensitivity 0.20 MUE(kcal/mol) 0.82 Sensitivity 0.40
RMSE(kcal/mol) 1.69 RMSE(kcal/mol) 1.09
nilotinib
Prediction Prediction
s r s r
nt nt
me S 9 8 me S 16 1
eri eri
p R 3 1 p R 2 2
Ex Ex
Accuracy 0.48 Accuracy 0.86
Specificity 0.53 Specificity 0.94
MUE(kcal/mol) 1.50 Sensitivity 0.25 MUE(kcal/mol) 0.82 Sensitivity 0.50
RMSE(kcal/mol) 1.86 RMSE(kcal/mol) 1.06
ponatinib
Prediction Prediction
s r s r
nt nt
me S 17 4 me S 21 0
eri eri
p R 0 0 p R 0 0
Ex Ex
Accuracy 0.81 Accuracy 1.00
Specificity 0.81 Specificity 1.00
MUE(kcal/mol) 0.94 Sensitivity NA MUE(kcal/mol) 0.87 Sensitivity NA
RMSE(kcal/mol) 1.57 RMSE(kcal/mol) 1.09
Figure4. PhysicalmodelingaccuracyincomputingtheimpactofclinicalAblmutationsonselectiveinhibitor
binding.LigandinteractiondiagramsforsixselectiveFDA-approvedtyrosinekinaseinhibitors(TKIs)forwhichco-crystal
structureswithAblwereavailable(left).Comparisonsforclinically-observedmutationsareshownforFEP+(right)and
Prime(left).Foreachligand,computedvs.experimentalbindingfreeenergies(ΔΔG)areplottedwithMUEandRMSE(units
ofkcal/mol)depictedbelow.Truthtablesareshowntotheright.Rowsdenotetruesusceptible(S,ΔΔG≤1.36kcal/mol)or
resistant(R,ΔΔG>1.36kcal/mol)experimentalclassesusinga1.36kcal/mol(10-foldchange)threshold;columnsdenote
predictedsusceptible(s,ΔΔG≤1.36kcal/mol)orresistant(r,ΔΔG>1.36kcal/mol).Correctpredictionspopulatediagonal
elements(orangetext),incorrectpredictionspopulateoff-diagonals.Accuracy,specificity,andsensitivityfortwo-class
classificationareshownbelowthetruthtable.Ellipticalpointsizesanderrorbarsinthescatterplotsdepictestimated
uncertainty/variabilityanderrorrespectively(±𝜎)ofFEP+values(verticalsize)andexperimentalvalues(horizontalsize).
Note:ThesensitivityforaxitinibandponatinibisNA,becausethereisnoresistantmutationforthesetwodrugs.
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