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Evolutionary Analysis of HIV-1 Protease Inhibitors

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Evolutionary Analysis of HIV-1 Protease Inhibitors PROTEINS:Structure,Function,andGenetics48:63–74(2002) EvolutionaryAnalysisofHIV-1ProteaseInhibitors: MethodsforDesignofInhibitorsThatEvadeResistance DanielStoffler,MichelF.Sanner,GarrettM.Morris,ArthurJ.Olson,andDavidS.Goodsell* DepartmentofMolecularBiology,TheScrippsResearchInstitute,LaJolla,California ABSTRACT Drug-resistantstrainsarerapidly Inthisreport,wedescribeanevolutionarymethodto selectedduringAIDStherapybecauseofthehigh analyzetheperformanceofagiveninhibitoragainsta rateofmutationinHIV.Inthisreport,wepresent mutatingtarget.Theevolutionaryapproachtoinhibitor anevolutionarysimulationmethodforanalysisof designquantifiesthesusceptibilityofatrialinhibitorto viralmutationanditsuseforoptimizationofHIV-1 resistanceasthevirusmutatesandrevealstherelative proteasedrugstoimprovetheirrobustnessinthe importanceandrolesofindividualproteasemutantsin faceofresistancemutation.Wefirstpresentan thisevolution.Onthebasisofthesesimulations,we analysisoftherangeofresistantmutantsthatpro- predictoptimalcombinationsofdrugsforuseinAIDS duceviablevirusesbyusingavolume-basedviral therapyandsuggestalterationsofcurrently-usedinhibi- fitnessmodel.Then,weanalyzehowthisrangeof torstoimprovetheirefficiencyandultimatelyevadedrug mutantproteasesallowsdevelopmentofresistance resistance. toanoptimalinhibitorpreviouslydesignedbycom- putationalcoevolutiontechniques.Finally,weevalu- MATERIALSANDMETHODS atetheresistancepatternsofcommerciallyavail- Theevolutionarysimulationspresentedhererequire abledrugs,andwediscusshowresistancemightbe theevaluationoffitnessformillionsofmutantstrainsof overcomebyoptimizingthesizeofspecificside- theprotease.Tomakethiscomputationfeasible,wehave chainsoftheseinhibitors.Proteins2002;48:63–74. usedasimplefitnessevaluationmodelbasedonMichaelis- ©2002Wiley-Liss,Inc. Mentenkineticsusingbindingenergiesestimatedbya simplemeasureofvolumecomplementaritybetweeninhibi- Keywords:HIV-1protease;drugresistance;com- tors,substrates,andthemutantproteases.Thesimplifica- puter-aideddrugdesign;coevolution; tionsusedinthismodel,describedbelow,limittheapplica- AIDStherapy bilityoftheresults.Mutationsdistantfromtheactivesite INTRODUCTION andtheeffectsofflexibilityarenotmodeled.Atomic-level predictionsarebeyondthereachofthesesimulations, Traditionaldrugdiscoverytechniquesseekcompounds butthesimulationsdoprovidevaluableinformationon thatmaximallyinhibitasingletargetenzyme.Inmost optimalsizingofindividualside-chainsininhibitors cases,thisisaneffectiveapproach.Mostenzymesare andgeneralconceptsforthedesignofresistance-evading highlyspecificfortheirsubstrate,soaninhibitorofsimilar drugs. shapeandchemicalnaturewillbindtightlytotheactive site.Theseinhibitorswillalsoberelativelyrobustto ModelingofViralFitness resistancemutationintheenzyme,becausehighlyspecific Viralfitness,thelikelihoodthatagivenvirusmay enzymeshavelittlerangeforremodelingtheiractivesites reproduce,isevaluatedforeachmutatedproteasewhen withoutcompromisingbindingandcatalysis.Thisap- challengedbyagiveninhibitor.Asinpreviouswork,5,6 the proachhasbeenwidelyusedtodesigninhibitorsfor fitnessofamutantvirusisproportionaltotherateof diverseenzymatictargets,includingHIV-1protease.Pro- polyproteinprocessing.Foracompletedescription,please tease-inhibitingdrugscurrentlyintestingandinusehave seethepreviousreports;onlyashortsummaryisprovided beenoptimizedtobindtightlyandspecificallytothe here.Thereactionvelocityofamutantproteasecleavinga wild-typeprotease(reviewedinRef.1).HIV-1protease, givenpolyproteinprocessingsiteinthepresenceofan however,issomewhatdifferentfrommosttypicalen- inhibitorisestimatedbyusingMichaelis-Mentenkinetics. zymes:itbindstoandcleavesacollectionofdifferent Toevaluatetheoverallfitnessofthevirus,reaction peptides.Thisbroaderspecificitymakesresistancemuta- tionpossible,becausetheactivesiteisremodeledtoreject inhibitorswhileretainingtheabilitytobindtotheappro- Grantsponsor:NationalInstitutesofHealth;Grantnumber:PO1 priatepeptidesequencesneededforviralmaturation.This GM48870;Grantsponsor:SwissNationalScienceFoundation:Grant broadspecificity,combinedwiththelowfidelityofreverse number:823A-061225. 2,3 4 *Correspondenceto:DavidS.Goodsell,DepartmentofMolecular transcriptase andthehighreplicationrateofthevirus, Biology,TheScrippsResearchInstitute,LaJolla,CA92037,E-mail: makedrugresistanceamajorproblem.Whenpatientsare [email protected] treatedwiththewild-type-optimizeddrugscurrentlyused Received25September2001;Accepted8February2002 intherapy,drugresistantstrainsofvirusarerapidly Publishedonline00Month2002inWileyInterScience selected. (www.interscience.wiley.com).DOI:10.1002/prot.10130 ©2002WILEY-LISS,INC. 64 STOFFLER ET AL. velocities are estimated for nine processing sites, and the As in our previous work,5 we limited protease mutations slowest rate is taken to be the rate-limiting step, defining to those that directly contact the inhibitor in the active site the viability. Kinetic constants are evaluated by using a [Fig. 1(a) and (b)]. These residues are determined by number of simplifications. The Michaelis constant is as- searching for contacts between protease C␤ atoms and sumed to be equal to the dissociation constant of the substrate C␤ atoms that are Ͻ6 Å apart. Ten protease inhibitor to the mutant protease, which is estimated by residues satisfy this criterion: G27, A28, D29, D30, V32, using the volume-based binding energy described below, I47, G48, G49, I50, and I84. The catalytic aspartate residue, D25, also meets these criteria but is not allowed to and a constant Vmax is used throughout. In the current ϭ ϭ mutate. The commonly observed mutant V82 is omitted work, [I] 10[S] KM(wt), where KM(wt) is the estimated Michaelis constant of the uninhibited wild-type protease. from the list by this distance cutoff; however, this should Choice of different values generally changes the absolute have little effect on the results because I84 provides Ј 6 value of the fitness but retains a similar ranking of remodeling of the S1/S1 pockets similar to that of V82 particular mutant proteases relative to one another. It has and the volume-based model used here does not evaluate been estimated that the reduction of protease activity to directionality in the interaction between side-chains and ϳ2% that of wild type is sufficient to block viral replica- subsites. 7 ϳ tion, whereas restoration of protease activity to 26% Subsite-Volume Computation for Inhibitors that of wild type will yield a viable resistant strain.8 Binding energies are estimated by using an empirically The volume of nonstandard side-chains in each inhibitor derived binding model based on the complementarity of was evaluated in two steps: (i) side-chain atoms were protease subsite and inhibitor side-chain volumes. Poten- assigned to a given subsite by comparison to a modeled tials are calibrated against a set of 74 substrate sequences octapeptide substrate, and (ii) volumes were estimated by that are known to be cleaved, including the 63 used in using a numerical approximation based on molecular previous work5,6 and adding 11 sequences isolated by surfaces. A generic, non-native substrate (Ala)8 octapep- phage display techniques (personal communication from tide bound to HIV-1 protease was built by using the structure of the substrate-form of LP-149 complexed with Z. Beck and J. Elder, Department of Molecular Biology at 9 the Scripps Research Institute). A potential of mean force FIV D30N protease, PDB accession code 3fiv. These FIV-PR structures provided valuable experimental data is created by assuming Boltzmann-type statistics, which about the orientation of the scissile peptide bond. The postulates that side-chains that are commonly found in a backbone torsion angles of the substrate form of LP-149 given protease subsite will interact with favorable free were used to define the conformation of the octapeptide. energy. The potentials are parameterized on the basis of The conserved residues of the active site region of the FIV the volume of the subsite by using protein side-chain D30N mutant, LP-149 complex, were superimposed on the volumes evaluated from a sum of atomic volumes. The corresponding residues of HIV-1 protease. Once con- potentials were tested on a set of 55 cleaved peptides (not structed, the sequence of the (Ala)8 octapeptide was modi- used in the parameterization) and 1727 uncleaved pep- fied and energy minimized by using the AMBER force field 5,6 tides, correctly predicting 87% of the cases. and the Discover module of the InsightII 97.0 package The effects of protease mutation are evaluated by using (Molecular Simulations, Inc.). Two water molecules were an ad hoc method. The energetic consequences of these included in the model: (i) the water that bridges the tips of mutations are estimated by shifting the wild-type poten- the flaps and donates two hydrogen bonds to two backbone tials according to the magnitude of the volume change carbonyls of the substrate (“water 301”) and (ii) the lytic caused by the mutation. For example, a V32L mutation water that binds between the catalytic aspartic acids. increases the size of the protease residue by about 18 Å3, The model octapeptide complex was superimposed with decreasing the size of the S2 and S2Ј subsites. Thus, the S2 the crystallographic structures of the inhibitor complexes. and S2Ј potentials for this mutant are shifted by 18 Å3 Atoms of the drug molecule were assigned to the subsite toward lower values, shifting the minimum and favoring corresponding to the nearest C␤ atom in the modeled the binding of smaller inhibitor side-chains. octapeptide. In cases in which side-chains were
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