qsar development to describe hiv-1 integrase inhibition

QSAR development to describe HIV-1 integrase inhibition

H. Yuan, A.L. Parrill*

Department of Chemistry, University of Memphis, Memphis, TN 38152, USA

Received 3 December 1999; accepted 15 March 2000

Abstract

HIV-1 integrase(IN) is one of three viral enzymes required for replication. IN mediates integration of viral DNA into the hostgenome in two steps: 30-processing and strand transfer. It is currently recognized as an important target for therapeuticdevelopment against AIDS. QSAR (Quantitative Structure-Activity Relationship) modeling was utilized to study HIV-1integrase inhibition. QSAR models were constructed to predict the IC50 values for the two structural classes (salicyhydrazinesand tyrphostins) independently and in combination. The results showed that the models for different structural classes havedifferent dependence on the same descriptors. It suggests that salicylhydrazines and tyrphostins might have different bindingsites in HIV-1 integrase.q 2000 Elsevier Science B.V. All rights reserved.

Keywords: Human immunodeficiency virus; Integrase; Quantitative structure–activity relationship; Inhibition; Multiple linear regression

1. Introduction

Human immunodeficiency virus type 1 (HIV-1) isthe causal agent of the acquired immunodeficiencysyndrome (AIDS) (reviewed in Refs. [1–4]). Threeenzymes important to the replication cycle of thisvirus, reverse transcriptase, protease, and integrase,are considered to be promising targets for the develop-ment of anti-AIDS drugs [1,2,4,5]. Reverse transcrip-tase and protease have been the focus of intenseresearch for therapeutic development against AIDS.Integrase is currently recognized as another attractivetarget. Integrase catalyzes the second enzymatic step ofthe viral life cycle, integration of viral DNA into thehost DNA [2]. It includes two reactions: 30-processingand strand transfer. In the first reaction, integrase cata-lyzes the cleavage of the last two nucleotides from each

30-end of the linear viral DNA. The subsequent DNAstrand transfer reaction involves the nucleophilic attackof these 30-ends on host chromosomal DNA. Repair ofthe gaps between the viral and target DNA is likelyperformed by host repair machinery.

Quantitative Structure–Activity Relationship(QSAR) modeling is a statistical analysis, developedby Hansch [6], to elucidate a quantitative correlationbetween chemical structure and biological activity.QSAR methods have been widely applied to manydrug design problems. The fundamental hypothesisof QSAR is that biological properties are functionsof molecular structure. It is reasonable to assumethat certain conformations can bind to the specificenvironment at the active site of the receptor protein.Only compounds that have both the necessary func-tional groups and correct conformations will interactwith target protein. Thus molecules with similar struc-tures can reasonably be expected to show similar bio-logical activity. A numerical representation can beconstructed to characterize the structure of a molecule,

Journal of Molecular Structure (Theochem) 529 (2000) 273–282

0166-1280/00/$ - see front matterq 2000 Elsevier Science B.V. All rights reserved.PII: S0166-1280(00)00553-4

www.elsevier.nl/locate/theochem

* Corresponding author. Tel.:11-901-678-2638; fax:11-901-678-3447.

E-mail address:[email protected] (A.L. Parrill).

this numerical representation is called a descriptor. Soit is possible to investigate the biochemical structure–activity relationships by a quantitative approach.

This paper describes our current development ofQSARs to describe HIV-1 integrase inhibition.

2. Methods

In our research, QSAR studies are performed withthe Moe program [7] orCerius2 program [8].

2.1. Molecular modeling and low-energyconformational search

To understand why ligands have different activityagainst HIV-1 integrase, molecular modeling wasperformed for salicylhydrazines and tyrphostins forwhich accurate biological activities had been determined[9,10]. These compounds are shown in Tables 1 and 2.After each structure was built, energy minimization wasperformed using the MMFF94 force field [11]. These

energy-minimized structures were used as startingpointsfor molecular modeling studies. Conformationalsearches were conducted by using the Hybrid MonteCarlo (HMC) and Random Incremental Pulse Search(RIPS) methods. Low-energy conformations of thesemolecules were used for subsequent study.

2.2. Calculation of numerical descriptors

In order to build QSAR models it is necessary toconstruct a numerical representation, or descriptor, ofa molecule. A descriptor can be any quantitative prop-erty that depends on the molecule’s structure such asmolecular weight, van der Waals surface area, dipolemoment or number of hydrogen atoms. These descrip-tors can be classified into three groups [12]. First,topological descriptors are derived solely from con-nectivity and composition of the structure: examplesinclude Kier’s molecular shape index and the Wienerindex. Second, geometrical descriptors are derivedfrom the 3D molecular geometry: examples include

H. Yuan, A.L. Parrill / Journal of Molecular Structure (Theochem) 529 (2000) 273–282274

Table 1Inhibition of HIV-1 integrase by salicylhydrazines [9]

Compound No. NSC No. Substituent Antiviral dataIC50 (mm)

1 NSC 408200 – 0.12 NSC 653029 2-OH 8.23 NSC 653035 4-OH 10.34 NSC 653039 3,4,5-(OCH3)3 4.45 NSC 652173 2-OH 0.0626 NSC 652174 4-OCH3 0.0537 NSC 652175 4-NO2 0.0918 NSC 652176 3-NO2 0.0989 NSC 652177 4-OH 0.059

10 NSC 652178 3-OH 0.1311 NSC 652179 3-OCH3, 4-OH 0.09412 NSC 652180 3,4-(OCH3)2 0.1513 NSC 652182 3,4,5-(OCH3)3 0.2414 NSC 652181 – 0.6

molecular volume and the solvent-accessible surfacearea. Finally, electronic descriptors reflect the elec-tronic structure of the molecule and overallcharacteristics of the partial charge distribution:examples include the dipole moment and the sum ofpartially positive surface area.

2.3. Model-building and cross validation

In theMoe program, model building is an operationto determine optimal parameters for the prediction ofmolecular properties such as biological activity againsta particular target or physico-chemical properties suchas boiling point and chromatographic retention. Statis-tical terms (correlation coefficient, RMSE, percentaccuracy, etc.) are used to evaluate the quality of amodel. There are two types of models built to predictmolecular properties inMoe. The first is MultipleLinear Regression, in which the experimental result isexpressed as a linear combination of the descriptors plusa constant. The parameters or coefficients for the modelare determined to minimize the mean squared error(MSE) between the experimental data and the model’sresults. The second model type is binary, in which abinary value (1 or 0) is used to represent activity, andthe model estimates the probability that a new moleculewill be active (1) or inactive (0). Once a model has beenbuilt, its predictivity can be assessed by cross-validationin which some experimental data are reserved from themodel building process and are predicted as “new”molecules.

3. QSAR studies of salicylhydrazines andtyrphostins

Some salicylhydrazines and tyrphostins exhibitbiological activity against the HIV-1 integrase[9,10]. Qualitative SAR studies demonstrated thatthe aromatic hydroxyl group and hydrogen bondacceptors such as a carbonyl group are important totheir inhibition. QSAR models have been constructedto predict the IC50 (50% inhibitory concentration)values for the two structural classes independently,and in combination.

The relative importance of over one hundreddescriptors has been compared. Five descriptors thatrepresent hydrophobic effects, electronic effects andsteric effects were chosen to build models for salicyl-hydrazines and tyrphostins. The first of these is(log P)2, the squared log of the octanol/water partitioncoefficient. The second is vdw_area, the area of vander Waals surface. The third is PEOE_VSA_NEG, thesum of the van der Waals surface area of atoms whosepartial charges are negative. The final descriptors areKier1 and KierA1, the Kier Kappa Shape Indices,which compare the molecular graph with minimaland maximal molecular graphs and demonstratedifferent aspects of molecular shape. The formulasto calculate Kier1 and KierA1 aren�n 2 1�2=m2 and�n 1 a��n 1 a 2 1�2=m2

; respectively, in whichn isthe number of heavy atoms,m denotes the numberof bonds among heavy atoms anda is the sum over�ri =rc 2 1�whereri is the covalent radius of atomi and

H. Yuan, A.L. Parrill / Journal of Molecular Structure (Theochem) 529 (2000) 273–282 275

Fig. 1. Experimental vs. predicted log(1/IC50) of salicylhydrazines.


Table 2Inhibition of HIV-1 integrase by tyrphostins [10]

Drug No. Basic Structure Substituents Antiviral data

X R IC50 (mm)

AG537 A –NH(CH2)3NH– H 40.9AG 550 A –NH(CH2)4NH– H 28.6AG 575 A –NH(CH2)4NH– OH 49.8AG 588 A –NH(CH2)8NH– H 13.4AG 589 A –NH(CH2)10NH– H 2.7AG 638 A –NH(CH2)5NH– H 13AG 1075 A –NH(CH2)3NH– Br 113AG 1717 A –NH(CH2)3NH– OH 17.3AG 1718 A –NH(CH2)5NH– OH 38.3AG 1292 A –NH(CH2)3NH– NO2 72.2AG 590 A m (di-NHCH2)benzene H 10.6AG 591 A H 23.1

AG 593 A H 16.8

AG 1093 A H 8.5

AG 1136 A H 11.8

AG 490 B –NH-benzyl H 33.7AG 538 B –3,4-dihydroxyphenyl H 36.2AG 555 B –NH(CH2)3phenyl H 24.3AG 556 B –NH(CH2)4phenyl H 11.8AG 822 B H 36.5

AG 921 B –NH(CH2)2(3,4-dihydroxyphenyl) H 29.8AG 1387 B –NH(CH2)3phenyl I 9.6

rc is the covalent radius of a carbon atom [13]. Kier1reflects the number of heavy atoms and the degree ofunsaturation in the molecule. KierA1 includes influ-ences from atomic identity, thus reflecting heteroatomcontent, in addition to the number of heavy atoms anddegree of unsaturation.

4. QSAR model of salicylhydrazines

Eq. (1) shows the linear model developed todescribe the biological activity of the salicylhydrazines.

log�1=IC50� � 18:632 0:142× vdw_area2 0:0792

× PEOE_VSA_NEG1 0:478

× �log P�2 1 0:125× KierA1 1 2:133

× Kier1: �1�The correlation coefficientR2 for this model is 0.93. Weuse the relative importance to evaluate the contributionof each descriptor to the model. Among the five descrip-tors described above, vdw_area has the highest relative

importance, 1.0. The next highest is Kier1’s 0.88.PEOE_VSA_NEG and× (log P)2 have similar relativeimportances, 0.30 and 0.28, respectively. KierA1 is theleast important and the relative importance is 0.05.

This model has an average percent absoluteresidual of 5.2% under a leave-one-out cross valida-tion scheme. Fig. 1 was plotted to compare the realand predicted activity data. The predicted activity datais shown in Table 3.

5. QSAR model of tyrphostins

Eq. (2) shows the linear model developed todescribe the biological activity of the tyrphostins.

log�1=IC50� � 4:2201 0:0102× vdw_area2 0:0092

× PEOE_VSA_NEG1 0:0643

× �log P�2 2 0:199× KierA1 1 0:0456

× Kier1: �2�This model has a correlation coefficientR2 of 0.77.


Table 2 (continued)

Drug No. Basic Structure Substituents Antiviral data

X R IC50 (mm)

AG 1661 B H 10.9

AG 82 C –CN OH 31.8AG 982 C H 25.7

AG 1007 C H 31.7

AG 946 D – 33.3

AG 1233 E – – 16.8

Again the vdw_area is the most importantdescriptor for this model, with a relative importanceof 1.0. The KierA1 relative importance is 0.88.The PEOE_VSA_NEG, (logP)2 and Kier1 havesimilar relative importance, 0.37, 0.33 and 0.30,respectively.

An average percent absolute residual of 3.6% isobtained for this model under a leave-one-out crossvalidation scheme. Fig. 2 shows the comparison of theexperimental and predicted activity data. Thepredicted activity data is shown in Table 4.

6. Models for salicylhydrazines and tyrphostins incombination

The Genetic Functional Approximation (GFA) inthe Cerius2 program was employed to look forgood models representing the biological activity ofthe combined salicylhydrazine and tyrphostin groups.Eqs. (3) and (4) each with correlation coefficients of0.91 were found to be able to predict their biologicalactivities. Four descriptors were selected by the GFAmethod for each equation. The first of these new


Fig. 2. Experimental vs. predicted log(1/IC50) of tyrphostins.

Table 3Experimental and predicted activities of salicylhydrazines

NSC No. Log (1/IC50) Predicted Activity: Log (1/IC50)a % Residuals

Eq. (1) Eq. (3) Eq. (4) Eq. (5) Eq. (7) Eq. (1) Eq. (3) Eq. (4) Eq. (5) Eq. (7)

NSC 408200 7.0 6.3 5.5 5.6 6.2 7.3 10.3 23.4 25.7 13.5 5.4NSC 653029 5.1 5.4 5.3 5.1 5.1 5.2 5.6 3.2 0.5 1.0 3.2NSC 653035 5.0 5.2 5.0 4.8 5.2 5.1 3.8 0.6 3.9 4.2 1.4NSC 653039 5.4 4.8 5.2 5.2 5.0 4.9 10.9 4.2 3.6 6.4 9.6NSC 652173 7.2 7.0 7.0 7.0 6.9 7.0 3.0 3.3 3.6 4.8 2.3NSC 652174 7.3 7.1 7.0 7.0 6.7 7.1 2.7 3.5 3.8 7.5 2.2NSC 652175 7.0 6.9 7.3 7.4 6.9 7.0 2.0 3.5 5.2 2.0 0.0NSC 652176 7.0 7.2 7.2 7.3 6.9 7.0 2.1 2.2 4.6 1.6 0.1NSC 652177 7.2 6.8 6.9 6.8 6.9 7.0 6.0 5.4 5.5 5.0 3.2NSC 652178 6.9 7.0 6.8 6.8 6.9 6.9 1.5 1.1 1.3 0.6 0.0NSC 652179 7.0 7.8 6.8 6.9 6.9 6.8 10.5 2.4 2.5 1.2 3.2NSC 652180 6.8 6.9 7.0 7.0 7.0 7.0 1.2 2.5 3.1 2.7 3.0NSC 652182 6.6 6.8 7.0 7.1 7.3 7.1 2.4 5.1 6.4 9.3 6.4NSC 652181 6.2 6.9 7.0 6.5 7.2 6.5 10.4 10.7 4.6 14.7 4.3

Average 5.2 5.1 5.3 5.3 3.2

a Leave-one-out cross validated data.

descriptors is ASA_H, the water accessible surfacearea of all hydrophobic atoms. The second one isPEOE_VSA-2, van der Waals surface area of atomswhere the partial charge is in the range of[20.15,2 0.10]. The remaining two are E_str andE_ang, components of the ligand internal energy.

log�1=C� � 4:0892 0:0606KierA11 0:00436ASA_H

10:0518E_str2 0:0180�PEOE_VSA–2� �3�

log�1=C� � 4:1141 0:00383ASA_H2 0:0126E_ang

1 0:0659E_str2 0:0355Kier1: �4�In order to determine if the descriptors used in these

models were equally suitable for both structuralclasses, models for salicylhydrazines and tyrphostinswere developed independently with these descriptors.Eq. (5)�r2 � 0:89� shows the model developed usingthe GFA-selected descriptors from Eq. (3) for thesalicylhydrazines. Eq. (6)�r2 � 0:55� shows themodel developed for the tyrphostins using the samedescriptors. Eqs. (7)�r2 � 0:96� and Eq. (8)�r2 �0:44� show the models developed for the salicyl-hydrazines and tyrphostins, respectively, using theGFA-selected descriptors from Eq. (4).

log�1=C� � 6:6211 0:0166KierA11 0:00025ASA_H

20:0612�PEOE_VSA–2�2 0:00014E_str �5�


Table 4Experimental and predicted activities of tyrphostins

Drug No. Log (1/IC50) Predicted Activity: Log (1/IC50)a % Residuals

Eq. (2) Eq. (3) Eq. (4) Eq. (6) Eq. (8) Eq. (2) Eq. (3) Eq. (4) Eq. (6) Eq. (8)

AG 537 4.4 4.5 4.7 4.7 4.6 4.6 3.7 6.6 6.0 4.8 5.6AG 550 4.5 4.6 4.8 4.7 4.7 4.7 1.1 4.9 4.2 3.4 3.6AG 575 4.3 4.5 4.2 4.3 4.3 4.4 4.8 1.8 0.8 0.6 1.6AG 588 4.9 5.1 4.9 4.8 4.9 4.9 4.3 0.2 1.1 0.2 0.1AG 589 5.6 5.3 5.0 5.0 5.0 5.0 4.1 10.5 10.1 10.2 10.6AG 638 4.9 4.7 5.0 4.9 4.9 4.9 4.8 1.6 0.8 0.8 0.3AG 1075 3.9 4.3 4.5 4.7 4.6 4.7 9.6 15.1 19.7 16.3 20.1AG 1717 4.8 4.4 4.4 4.4 4.4 4.4 7.7 6.6 6.6 8.2 6.8AG 1718 4.4 4.5 4.3 4.3 4.3 4.4 2.9 3.6 2.0 2.5 0.0AG 1292 4.1 4.2 4.2 4.2 4.4 4.4 0.2 1.9 2.5 5.6 6.3AG 590 5.0 4.9 4.6 4.6 4.6 4.5 2.0 8.2 8.9 7.3 8.9AG 591 4.6 4.8 4.7 4.7 4.8 4.8 3.1 0.8 0.3 4.3 2.7AG 593 4.8 4.7 4.8 4.8 4.9 4.8 2.1 0.7 1.0 3.0 1.2AG 1093 5.1 5.3 4.9 4.9 4.9 4.8 5.3 4.0 3.9 2.8 5.4AG 1136 4.9 4.8 4.8 4.9 4.8 4.8 2.4 1.8 1.3 2.8 2.0AG 490 4.5 4.7 4.9 4.9 4.9 4.7 4.8 10.2 8.6 8.7 5.1AG 538 4.4 4.5 4.5 4.5 4.5 4.8 1.4 1.4 0.4 0.3 7.1AG 555 4.6 4.8 5.0 4.9 4.8 4.8 3.6 7.0 5.9 4.2 2.9AG 556 4.9 4.9 5.2 5.1 5.0 4.9 0.6 5.4 3.8 0.6 0.6AG 822 4.4 4.5 4.6 4.5 4.5 4.5 0.9 3.8 0.9 2.3 0.5AG 921 4.5 4.5 4.7 4.7 4.6 4.6 0.9 3.9 3.9 2.4 1.8AG 1387 5.0 4.7 5.0 5.1 4.8 4.9 7.2 0.2 1.6 3.6 2.2AG 1661 5.0 4.6 4.8 4.7 4.8 4.6 6.8 3.2 5.4 3.5 8.1AG 82 4.5 4.4 4.1 4.1 3.9 3.9 1.6 9.7 10.0 14.3 15.2AG 982 4.6 4.3 4.6 4.7 4.5 4.6 7.0 1.4 1.7 1.3 0.6AG 1007 4.5 4.7 4.8 4.8 4.7 4.9 5.0 6.6 6.2 4.8 7.6AG 946 4.5 4.6 4.5 5.0 4.5 4.9 3.2 1.5 11.1 0.0 8.5AG 1233 4.8 4.8 4.8 4.8 4.6 4.6 0.6 0.1 0.0 3.5 4.5

Average 3.6 4.4 4.6 4.4 5.0

a Leave-one-out cross validated data.

log�1=C� � 3:9692 0:0626KierA11 0:00366ASA_H

20:0174�PEOE_VSA–2�1 0:11067E_str �6�

log�1=C� � 6:1001 0:00563ASA_H1 0:0273E_str

2 0:0278E_ang2 0:07352Kier1 �7�

log�1=C� � 3:9441 0:00315ASA_H1 0:0681E_str

2 0:00298E_ang2 0:0263Kier1 �8�Table 3 shows the experimental log(1/IC50) and dif-ferent predicted results of salicylhydrazines from Eqs.(1), (3)–(5) and (7). Table 4 shows the experimental

Log(1/IC50) and different predicted results of tyrphos-tins from Eqs. (2)–(4), (6) and (8). The calculatedresiduals for Eqs. (5) and (6) demonstrate that thedescriptors from Eq. (3) more consistently describethe biological activity for the tyrophostins. Althoughthe average residuals favour the tyrophostins (5.3 forthe salicylhydrazines by Eq. (5) and 4.4 for thetyrphostins by Eq. (6)), the correlation coefficients(0.89 and 0.55) favor the salicylhydrazines. Thecalculated residuals for Eqs. (7) and (8) demonstratethat the descriptors from Eq. (4) better describe thebiological activity of the salicylhydrazines. In thispair, the average percent residual is noticeably betterfor Eq. (7) (3.2) than for Eq. (8) (5.0). These datashow that the descriptors selected by the GFA methodto describe the biological activities of the combinedgroup of salicylhydrazines and tyrphostins are moresuitable generally to describe the biological activitiesof the salicylhydrazines. The methods used to gener-ate and validate the QSAR models lend more weightto the more active salicylhydrazines. Figs. 3 and 4describe the relative importance of descriptors usedin these equations. The same descriptors played differ-ent roles in the equations for salicylhydrazines andtyrphostins.

7. Conclusions

It had been proposed that the inhibitors competewith HIV-1 integrase to chelate the divalent cation


Fig. 3. Relative importance of descriptors used in Eqs. (3), (5) and (6).

Fig. 4. Relative importance of description used in Eqs. (4), (7) and(8).

in the active site and gain potency against integrase[10,14,15]. The QSAR models shown as Eqs. (1) and(2) for two different classes of inhibitors havecommon descriptors but different coefficients andsigns. This means that the structural features charac-terized by these descriptors have different effects onthe interaction between inhibitor and protein for thetwo different structural groups. This conclusion issupported by the results obtained in applying descrip-tors selected by the GFA methodology inCerius2 forthe combined groups (Eqs. (3) and (4)) back to theseparate groups (Eq. (5) through (8)). The descriptorsused to generate the combined linear models gavesatisfactory results, correlation coefficients greaterthan 0.85, for the salicylhydrazines as shown in Eqs.(5) and (7), but very poor results, correlation coeffi-cients less than 0.6, for the tyrphostins as shown inEqs. (6) and (8). These differences point to a possibledifference in the manner in which these structuralclasses interact with the integrase enzyme. This differ-ence can be manifested either through interactionswith different amino acid residues that line a commoninhibitor binding pocket, or it could arise from thesestructurally different inhibitors interacting at twocompletely non-overlapping sites. Two crystallo-graphic studies on the interactions of integraseproteins with HIV-1 integrase inhibitors providesupport for the latter possibility. The earlier studydescribed the complex formed between an aromaticdisulfonic acid, Y3, and the catalytic domain of theavian sarcoma virus integrase [16]. Y3, an inhibitor ofboth the avian sarcoma virus integrase and HIVintegrase, was found to occupy a site adjacent to thecatalytic loop, but distant from the metal ion. A morerecent study described the complex formed between achloroindole, 5ClTEP, and the catalytic domain ofHIV integrase [17]. This inhibitor was found tooccupy a site adjacent to the catalytic loop and themetal ion. The different sites may reflect differencesbetween the structure of the integrases of the twodifferent viruses, but it is equally likely that differentinhibitors may show preferential binding for one orthe other sites. In fact, docking studies reported by ourgroup on the salicylhydrazines support binding to HIVintegrase at the site corresponding to the Y3 site inASV integrase [18]. Docking studies are underway todetermine if tyrphostins prefer the site observed for5ClTEP. If different binding sites are observed, it is

not difficult to explain why the models for differentstructural classes have significantly different depen-dence on the same descriptors. Future QSAR studieswill work with different combinations of integraseinhibitor structural classes in order to shed additionallight on which inhibitors share common binding sites.

Acknowledgements

Support from NIH/NIAID (Grant R15 AI 45984-01)and NSF (STI-9602656, CHE-9708517) are gratefullyacknowledged. We thank the Chemical ComputingGroup for their donation of theMOE program. Thiswork was also supported by the funds from theUniversity of Memphis, Department of Chemistry,College of Arts and Sciences, and the Vice Provostfor Research.

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qsar development to describe hiv-1 integrase inhibition

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