drug resistant mechanism of diaryltriazine analog inhibitors of hiv-1 reverse transcriptase using...

Drug Resistant Mechanism of DiaryltriazineAnalog Inhibitors of HIV-1 Reverse TranscriptaseUsing Molecular Dynamics Simulation and3D-QSAR

Zeng Li1, Hao Zhang1, Yixue Li2,*, JianZhang3,* and Hai-Feng Chen1,2,*

1College of Life Sciences and Biotechnology, Shanghai JiaotongUniversity, 800 Dongchuan Road, Shanghai 200240, China2Shanghai Center for Bioinformation Technology, 100 Qinzhou Road,Shanghai 200235, China3Department of Pathophysiology, Key Laboratory of CellDifferentiation and Apoptosis of Chinese Ministry of Education,School of Medicine, Shanghai Jiaotong University, 280 ChongqingRoad, Shanghai 200025, China*Corresponding authors: Hai-Feng Chen, [email protected];Yixue Li, [email protected]; Jian Zhang, [email protected]

Diaryltriazine inhibitors have highly potent andeffective bioactivities for the wild type of HIV-1reverse transcription. To design new drug of an-timutant HIV-1 reverse transcriptase, the mecha-nism of drug resistance for four types of mutantswas revealed. Molecular dynamics simulationssuggest that Lys101, Leu100, Lys103, Tyr181, andTyr188 are key residues. Different mutants of keyresidues may have different interaction modes andlead to different drug resistances. Then, CoMFAand CoMSIA methods were employed to construct3D quantitative structure–activity relationshipmodels. These models were evaluated by test setcompounds. These models can be used to makequantitative prediction of their bioactivities forlead compounds before resorting to in vitro andin vivo experimentation.

Key words: diaryltriazine analog, drug resistance, HIV-1 reversetranscriptase inhibitor, molecular dynamics simulation, three-dimensionalquantitative structure–activity relationship

Received 15 March 2010, revised 16 September 2010 and accepted forpublication 19 September 2010

Human immunodeficiency virus type 1 (HIV-1) is one of the mosthazardous viruses for human and still a risk of a worldwide HIV-1pandemic (1). Many different drugs and treatments have success-fully lowered the death rate of acquired immune deficiencysyndrome (AIDS) in advanced countries during the past decade (2).

HIV-1 reverse transcriptase (HIVRT) as a key enzyme plays anessential role in the virus life cycle for the replication of the RNAgenome into DNA form (3). As one of the components of cocktailtreatment for AIDS, the inhibitors of HIVRT are developed foranti-HIV pharmaceuticals (4). However, there is no simple andefficient method to fully cure AIDS because of the drug resistanceof HIVRT mutants. Mutations of L100I, K103N, Y181C, and Y188Lwere frequently found in clinic researches (5,6). The diaryltriazineanalogs (DATAs), one category of non-nucleoside reverse transcrip-tase inhibitors (NNRTIs), are highly potent and effective againstwild-type and some mutant strains of HIV-1 (7–9).

Many DATAs are synthesized and tested in laboratory (9–11). How-ever, there is not yet a system study on the drug resistance mecha-nism for this type inhibitor. Until recently, Monte Carlo-extendedlinear response calculations are used to predict the binding freeenergy between efavirenz analogs and K103N mutant (12). In thiswork, we revealed the mechanism of drug resistance for DATAs onmutant HIVRT with molecular dynamics (MD) simulation and three-dimensional quantitative structure–activity relationship (3D-QSAR)models. These models can be used to make quantitative predictionof their bioactivities for lead compounds before resorting to in vitroand in vivo experimentation.

Experimental Protocols

Data setsStructures and bioactivities (IC50) of diaryltriazine inhibitors (the con-centration in lM for 50% inhibition of MT-4 cells against the cyto-pathic effect of HIV-1) are extracted from the literature (9) andlisted in Tables 1–3. DATA inhibitors are consisted of three rings(A: the diaryl ring, B: the triazine ring, and C: the benzonitrile ring).The compounds marked with '*' belong to test set. The others con-stitute training set.

Molecular dockingThe complexes of HIVRT and ligand were extracted from the ProteinDatabank [WT: 1S6Q (8), L100I mutant: 1S1V (13), K103N mutant:1IKX (14), Y181C: 1JLA (15), and Y188L mutant: 1BQN (16)]. Three-dimensional structure modeling of inhibitors was performed usingthe SYBYL

a program package. AUTODOCK 3.0 package was used toautomated dock the whole ligands in the Tables 1–3 and receptors.

63

Chem Biol Drug Des 2011; 77: 63–74

Research Article

ª 2010 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2010.01049.x

The development and the principle of AutoDock 3.0 have beendescribed elsewhere (17,18). During the docking process, a series ofthe docking parameters were chosen. The number of generations,energy evaluations, and docking runs were set to 370 000,1 500 000, and 50, respectively.

Molecular dynamics simulationMolecular dynamics simulations and energy minimizations were per-formed using the AMBER8.0 simulation package (19) and the ff02force field (20) with the TIP3P water model (21). Hydrogen atomswere added using the LEAP module of AMBER8.0. Antechamber (22)was used to handle the force field of ligands. AM1-bcc charge wasassigned to the ligands. Cl ions were placed around the complex tomaintain the system's neutrality. The SHAKE algorithm (23) wasused to constrain bonds involving hydrogen atoms. The complexwas solvated in a truncated octahedron box of water, with theshortest distance between any protein atom and the edge of the

box as approximately 10 �. Particle Mesh Ewald (PME) (24) wasemployed to calculate long-range electrostatic interactions. Then,the complex was minimized with the PMEMD module of AMBER8.0.This minimization consisted of 1000 steps with the steepest des-cent method. The total energy versus minimization step was shownin Figure S1. The total energy has small fluctuation at the end of1000 steps and the systems became dynamics equlibration. Then,the systems were heated to 298 K and equilibrated at this temper-ature for 40 ps, then MD trajectory was recorded. At 298 K, 2.0 nseach was simulated for the ten complexes. The time step used inall calculations was 2.0 fs. The coordinates were saved every2.0 ps for the purpose of subsequent analysis. A total of 20 ns tra-jectories were collected for ten complex systems, taking about9830 CPU hours on the in-house Xeon (1.86 GHz) cluster.

CoMFA modelsThe CoMFA method was approved by Cramer et al. (25). After con-sistently aligning the molecules within a lattice, a probe sp3 carbonatom with +1 net charge was employed to calculate the steric andelectrostatic interactions between the probe atom and the mole-cule. Then, the obtained steric and electrostatic fields were scaled

Table 1: Substituent and IC50 activities of diaryltriazine inhibitors(subset I)

N N

N NH9

X

CNNH2

R

A B C

No. R X Wt L100I K103N Y181C Y188L

6 2,6-diCl CH2 2.201 0.400 1.398 0.699 0.50011A H CH2 0.545 )0.907 )0.928 ND ND11B 2-Cl CH2 0.886 )0.423 0.398 )0.410 )0.86711C 2,4-diCl CH2 1.854 )0.322 0.562 0.102 )0.36511D* 2,3,6-triCl CH2 2.523 0.381 1.201 0.544 0.57711E 2,4,6- triCl CH2 2.854 0.656 1.796 1.208 0.79311F 2,6-diMe CH2 2.921 )0.164 1.377 0.648 0.97911G 2,5-diMe CH2 2.509 )0.768 0.564 )0.196 )0.01311H 3.5-diMe CH2 1.041 )1.892 )0.706 )0.804 ND11I* 2,3,5,6-tetraMe CH2 2.337 )0.826 1.071 )0.076 0.43511J* 2,4,6-triMe CH2 3.097 1.086 2.523 1.824 1.34721A 2,4,6-triMe NH 3.000 2.432 2.699 1.959 0.90721B 2,4,6-triMe S 2.538 1.194 2.222 1.721 1.44421C 2,4,6-triMe O 3.000 1.125 2.699 1.886 0.74021D 2,6-diMe O 2.509 0.070 1.770 0.478 0.48521E 2,6-diMeO O 1.509 ND 0.279 )0.193 ND21F 2,6-diCl O 2.509 0.416 1.086 0.554 )0.66021G 2,4,6- triCl O 2.620 1.060 1.921 1.208 0.29921H 2,4,6-triBr O 2.268 0.883 1.770 1.215 0.20821I 2,4,6-triF O 1.292 0.666 1.060 )0.562 )0.95221J* 2,6-diCl-4-F O 2.137 0.151 1.187 0.587 )0.00921K* 2-Cl,4-Br,6-Me O 2.678 1.229 2.222 1.456 0.98721L 2,6-diBr-4-Me O 2.699 1.721 2.301 1.770 0.38221M 2,6-diMe-4-Br O 2.824 0.876 2.097 1.377 1.25221N 2,6-diMe-4-Cl O 2.456 0.767 1.959 1.260 0.91721O* 2,6-diMe-4-I O 1.523 0.690 2.000 1.167 1.12521P 2,6-diMe-4-NO2 O 2.699 0.481 2.097 1.244 0.91021Q 2,6-diMe-4-NH2 O )0.894 ND ND ND ND

The value is pIC50 in nM.ND, not determined.

Table 2: Substituent and IC50 activities of diaryltriazine inhibitors(subset II)

A

Cl

Cl

N

NHN9

NCN

Y

B C

No. Y Wt L100I K103N Y181C Y188L

14A NHMe 2.155 )0.064 0.863 0.357 )0.00914B* NMe2 0.780 )1.508 )0.671 )1.127 ND14C NHnPropyl 0.071 ND ND ND ND14D N-Morpholino 0.712 ND ND ND ND14E* NHCH2 CH2N(Me)2 2.301 )0.100 1.678 0.421 0.52714F NHOH 2.620 0.971 1.886 1.420 1.58514G NHOMe 2.208 )0.140 1.086 0.202 0.09514H NHOEt 1.609 ND ND ND ND14I NHOPropyl 1.000 ND ND ND ND14J NMeOMe 1.066 ND ND ND ND14K N3 1.854 ND 0.650 )0.057 ND14L NHNH2 0.215 )0.766 )0.241 )0.766 )0.86514M SMe 1.745 ND 0.474 )0.371 ND14N SH 1.027 ND 0.141 )0.207 ND14O* OMe 1.921 )0.061 0.636 )0.161 )0.19014P OH 0.796 ND 0.217 )0.326 ND14Q SOMe )0.190 ND )1.190 )1.836 ND14R* OCH2CH2N(Me) 2 2.260 )0.033 1.432 )0.143 0.50214S NHCONHiPr 0.421 )1.545 )0.878 )1.641 ND14T NHCOMe 1.796 )2.000 1.398 )2.000 0.80114U* N=CN(Me) 2 2.602 0.200 1.796 0.500 0.60014V NHCO2Et 1.921 )0.215 0.261 )0.121 )0.23814W NH(C=NH)Me 2.569 0.214 1.398 0.678 0.44614X F )0.721 ND )1.721 )2.000 ND

ND, not determined.

Li et al.

64 Chem Biol Drug Des 2011; 77: 63–74

by the CoMFA-STD method in SYBYLa with default energy of30 kcal ⁄ mol. Coulomb potential was used to model electrostaticinteractions. Lennard–Jones potential was used to calculate stericinteractions. The regression analysis was carried out using the par-tial least-squares (PLS) (26) method. The final model was developedwith the optimum number of components to yield the highest qcv

2.The set of inhibitors was initially divided into two groups in theapproximate ratio 3:1. The selection of the test set and training setwas performed such that low, moderate, and high activity com-pounds are present in roughly equal proportions in both sets.

CoMSIA modelsThe CoMSIA similarity indices method were proposed by Klebeet al. (27,28), with the same lattice box in CoMFA model. Five phys-icochemical properties such as steric, electrostatic, hydrophobic,hydrogen bond donor, and hydrogen bond acceptor fields were eval-uated. Gaussian-type potential function was employed to calculatethese fields, which led to much smoother sampling of the fieldsaround the molecules than CoMFA. A default value of 0.3 was usedas attenuation factor.

Data analysisHydrophobic contact and hydrogen bond assignment were handledwith IN-HOUSE software (29–36). Hydrophobic interaction is definedthat the distance between the center mass of hydrophobic residueand the ring centers of A, B, and C for the ligand is no longer than6.5 �.

Results

Ten complexes of the compounds 20A (the most active one) and14L (the less active one) with wild type and four mutants of HIVRTwere simulated. To investigate the mechanism of drug resistancefor these mutants, we should compare if the bioactivity of the com-pound for mutant has significant change relative to wild type. The

root-mean-squared deviation (RMSD) relative to the initial structurewas calculated. An example of simulation time versus Ca RMSD ofwt-HIVRT is shown in supplement file (Figure S2). The RMSD varia-tion of HIVRT model was about 1.5 � for 20A and 14L, respectively.The results show that both complexes became dynamics equilibra-tion after 1.0 ns of simulation.

Hydrogen bond networkThe principal hydrogen bonding interactions for ten complexes of20A and 14L were identified with Ligplot (37) and their populationswere listed in Table 4.

Key residue of Lys101Table 4 illustrates that the populations of hydrogen bond betweenligands and Lys101 for all types of HIVRT are rather high. Further-more, these hydrogen bonds have similar hydrogen bond donor andacceptor atoms, especially between N9 of 20A and oxygen ofLys101 (shown in Figure S3). These backbone hydrogen bonds arealways between ligand and main chain of Lys101. The alignmentbetween 20A and 14L for wt-HIVRT is shown in Figure 1 (20A andits complex in green; 14L and its complex in magenta). It is foundthat 20A and 14L have similar interactions on Lys101 within thehydrophobic pocket. The average distance between oxygen atom ofLys101 and N9 of ligand is about 2.9 �. In summary, Lys101 is a

Table 3: Substituent and IC50 activities of diaryltriazine inhibitors(subset III)

N N

N NH9

X

CN

H

R

A B C

No. R X Wt L100I K103N Y181C Y188L

20A 2,4,6-triMe NH 3.523 1.886 2.523 2.097 1.39820B 2,6-diBr-4-Me NH 3.301 2.523 2.523 2.523 1.10220C 2,6-diMe-4-Br NH 3.222 1.102 2.222 1.796 1.20120D* 2,6-diBr-4-CN NH 3.000 0.600 2.097 1.301 1.30120E 2,6-diMe-4-Br O 3.222 1.699 2.523 1.699 1.20120F* 2,6-diBr-4-Me O 2.886 1.796 2.222 1.398 0.40020G 2,6-diMe-4-CN O 2.398 0.000 1.699 0.300 0.60020H* 2,4,6-triMe S 2.699 1.495 2.523 1.886 1.699

Table 4: Hydrogen bond network for 20A and 14L

Complex ⁄ residue Lys 101 His 235 Tyr 318 Glu 690

20A-WT0.0003a

N9-Ob

1c– – –

20A-L100I0.013

– – – –

20A-K103N0.003

N9-O1

– – –

20A-Y181C0.008

N9-O0.988

– N1-OH0.715-

–

20A-Y188L0.040

N9-O1N15-O0.985

– – –

14L-WT0.610

N9-O1N15-O0.783

N-O0.832

– N2-OE10.990

14L-L100I5.83

– – – –

14L-K103N1.74

N9-O0.998

N-O0.846

– N2-OE10.255

14L-Y181C5.83

N9-O0.999N15-O0.954

N-O0.145

– N2-OE20.765

14L-Y188L7.32

N9-O1N15-O0.963

– – N2-OE20.796

aIC50.bCoupled atoms of hydrogen bond: ligand atom – residue atom.cPopulation of hydrogen bond.

Drug Resistance of HIVRT Inhibitor

Chem Biol Drug Des 2011; 77: 63–74 65

very important residue to form hydrogen bond with the HIVRT inhib-itor. This is consistent with other reports (38–43). However, thishydrogen bond between the backbone of Lys101 and N9 of liganddisappears in L100I mutant. This suggests that the mutation on100th residue leads to the change of orientation for the ligand.

Key residue of Lys103It was found that Lys103 is another key residue between wt-HIVRTand 20A during this simulation. At this site, K103N is a popularmutant with its novel mechanism of drug resistance. The NNRTIbinding with ligand is hampered because of the existence of ahydrogen bond between the side chains of Asn103 and Tyr188under ligand-free (Figure S4) (44–46). Therefore, the 'gatekeeper' ofhydrogen bond will limit the entry of inhibitor. Fatima Rodriguez-Barrios and Federico Gago reported that etravirine can break thishydrogen bond because of the cyano substituent competing for thehydrogen bond (45,46). Because the new hydrogen bond is estab-lished between cyano substituent and Asn103, etravirine can over-come the resistance of K103N mutant. In this study, weinvestigated whether the diaryltriazine analog can form a similarinteraction. The distance between oxygen atom of Asn103 andnitrogen atom in cyano of 20A is shown in Figure 2. This figure

shows that there is a propensity to form a valid hydrogen bondbetween Asn103 and 20A.

Other residues of Tyr318 (47), His235, and Glu690 (41) are also playconsiderable roles in hydrogen bond network. These backbone(Tyr318 ⁄ ligand) and sidechain (His235 ⁄ ligand and Glu690 ⁄ ligand)hydrogen bonds can help to stabilize the whole system. In summary,DATA analogs can overcome these mutations mostly because of thepresence of Lys101.

Hydrophobic interaction networkHydrophobic network is another principal interaction between DATAinhibitors and HIVRT. It can stabilize the ligand within the hydropho-bic pocket of the enzyme in vivo. The population of hydrophobicinteraction is listed in Table 5. This table suggests that 20A and14L have similar interaction models with the highly conservativeresidues, for example, Leu100, Val106, Val179, Tyr181, Tyr188,Leu234, and Tyr318 as key components to maintain the hydrophobicinteraction (41,48). Among these residues, we just focus on threespecific residues and their mutants of L100I, Y181C, and Y188L.

Key residue of Leu100It was found that Leu100 has hydrophobic interactions with threerings (A, B, and C) of the ligand for wt-HIVRT. L100I mutant stillhas strong hydrophobic interactions with rings A and B. However,there are no hydrophobic interactions between ring C and Ile100comparing with wt. This suggests that ring C moves away fromIle100. Figure 3A illustrated the alignment between wt-HIVRT andL100I mutant for 20A. This figure suggests that the orientation of20A for L100I is different from wt. The mutant of Ile100 pushes the

Figure 1: Hydrogen bond distances of 20A and 14L with wt-HIVRT.

Figure 2: Distance between Asn103 and 20A in wt and K103Nmutant.

Table 5: Hydrophobic interaction network for 20A and 14L

Complex ⁄ Residue 100 181 188 Sum_Ring Total

20A-wt 0.0003 A 1 0.992 0.992 2.984B 1 1C 1 1 4.984

20A-L100I 0.013 A 0.999 0.895 1.894B 1 1C 2.894

20A-Y181C 0.008 AB 1 0.0115 1.012C 1 1 2 3.011

20A-Y188L 0.040 A 1 0.951 1 2.951B 1 1C 0.613 0.613 4.564

14L-wt 0.610 A 1 0.8945 1 2.894B 1 1C 0.991 0.991 4.885

14L-L100I 5.83 A 0.943 0.793 0.245 1.981B 0.999 0.069 0.804 1.872C 0.066 0.066 3.919

14L-Y181C 5.83 A 1 0.988 1.988B 1 1C 0.997 0.997 3.985

14L-Y188L 7.32 A 0.320 0.063 1 1.383B 1 1C 0.649 0.649 3.031

Li et al.


ring C of ligands away from the backbone of HIVRT. This canexplain that the bioactivity of 20A has a 43-fold decreases forL100I mutant.

Figure 3B suggests that the orientation of 14L within the pokect ofL100I is also different from wt. This leads to discard the backbonehydrogen bond between Lys101 and N9 of 14L, and the hydrophobicinteraction between ring C and Ile100. This can explain that thebioactivity of 14L has a 10-fold decreases for L100I mutant. This isin agreement with the previous work that the binding mode of asimilar inhibitor is significantly changed by the L100I mutation (6).

Key residue of Tyr181Tyr181 is another popular mutant residue. Because Cys is not ahydrophobic residue, Y181C mutant will discard whole hydrophobicinteraction between Tyr181 and ligand. This will directly introducedrug resistance. The population of hydrophobic contact decreases to3.011 for Y181C from 4.984 for wt. These remaining interactionssuggest that 20A still has part inhibitor activity. This is consistentwith the experiment. For 14L, the results are similar to those of 20A.

Key residue of Tyr188Many studies (15,16,48,49) have demonstrated that Tyr181 andTyr188 play key roles in hydrophobic interaction with NNRTIs andthese two mutations result in high-level resistance to nevirapineand efavirenz (44). The two aromatic amino acids are quite flexible.In ligand-free HIVRT, the aryl-ring extends into the center of thepocket. The aryl-ring can adjust to establish p-p stack with thearyl-ring of inhibitor upon the ligand binding. Instead of p-p stackwith ring A of the ligand, Leu188 just contacts with ring A with themodel of point to plane. Although the populations of hydrophobicinteraction are similar between wt and Y188L, Y188L mutant

decreases the power of hydrophobic interaction for 20A. Thealignment between wt and Y188L for 20A is shown in Figure 4A.This figure suggests that the orientation of 20A is slightly changedtoward Leu188. The similar results are found for 14L (Figure 4B).However, the bioactivity of 20A and 14L for Y188L is the lowestamong all these mutants. T188L mutant increases the volume ofthe binding cavity. Large cavity has high possibility to change theorientation of inhibitor. For example, the orientation of 14L is signif-icant change, which weakens other interactions. This explains thatthe bioactivity of 14L-Y188L is the less active one.

The superposition of 20A and 14L within wt-HIVRT hydrophobicpocket is also illustrated in Figure 1. The orientations of three ringsbetween 20A and 14L are very similar and the structures are mostlyaligned well. This demonstrates that these compounds have similarinteraction mechanism with wt-HIVRT. The results of mutants aresimilar to those of wt. Therefore, we could construct common 3D-QSAR models for these wt and mutant HIVRT inhibitors.

3D-QSAR modelsTwo methods, CoMFA and CoMSIA, were used to construct 3D-QSAR models for HIVRT inhibitors. The alignment diagram of thecompounds for the training and test sets is shown in Figure 5.Because the experimental activities of 14C, 14D, 14H, 14I, 14J, and21Q were not determined, these compounds were excluded from3D-QSAR models.

Then, the models of WT and four mutants were constructed. Thecoefficients and parameters for these models are given in Tables 6and S1–S4. In each table, CoMFA and the best three models ofCoMSIA are listed. The correlations between experimental (EA) andpredicted activities (PA) are shown in supplement file (Tables S5and S6).

A B

Figure 3: 20A and 14L withinthe binding pocket of both wt andL100I mutant.

A B

Figure 4: 20A and 14L in thebinding pocket of both wt andY188L mutant.



3D-QSAR model of wt-HIVRTIn Table 3, CoMFA model has the highest q2 and r 2, which indicatestrong prediction ability. The best of CoMSIA model is the combina-tion of SEH. The contribution of steric, electrostatic, and hydropho-bic field is 0.162, 0.516, and 0.321, respectively. The contribution ofhydrophobic field is rather significant. This is consistent with theresult of MD that hydrophobic interaction is principal for DATAsbinding. Surprisingly, the contribution of the steric field in CoMFAmodel seems to be divided into steric and hydrophobic fields inCoMSIA model. In other models, we also find the introduction ofhydrophobic field will mainly decrease the contribution of stericfield. Anyhow, hydrophobic and steric fields might have similar con-tributions to the bioactivity. The contribution of hydrogen bonddonor field is 12.2%. This is also in accord with the result of hydro-gen bond network in MD simulation.

Contour plots are used to describe the effect of force fields on thebioactivity for different substituents. Figure 6 shows the contour

plot of CoMFA model for wt-HIVRT with the structure 20A. Themeanings of the different color areas are given in the legend. Atthe 5¢ site of triazine ring, small and positive groups are favorableto the bioactivity. This is consistent with the fact that 20-series(–H) have much higher bioactivity than 14-series (–NR2). Among the14-series compounds, the activity also meets this rule. It couldexplain that the activities have the sequence: 14A(NHMe) >14B(NMe2) > 14C(NHnPropyl), 14G(NHOMe) > 14H(NHOEt) >14I(NHOPropyl) for small group, and 14O(OMe) > 14P(OH) > 14X(F)for positive groups. Green-colored regions near the substituent ofbenzene ring indicate that bulky groups could increase activity. Theactivity data suggest that 2,4,6-tri- substituent at this site is ratherpopular. For example, the entire 20-series possesses 2,4,6-triMe.The order of bioactivity is same to that of bulk for the substituent:11E(2,4,6-triCl) > 11C(2,4-diCl) > 11B(2-Cl) > 11A(H), 21C(2,4,6-triMe)> 21D(2,6-diMe), 11J(2,4,6-triMe) > 11E(2,4,6-triCl), 21C(2,4,6-triMe)> 21G(2,4,6-triCl) > 21H(2,4,6-triBr), 21M(2,6-diMe-4-Br) > 21O(2,6-diMe-4-I) > 21N(2,6-diMe-4-Cl). Red-colored regions near the substi-tuent of benzene ring indicate that negative charge groups arefavorable to activity. Actually, p-electron at this position is helpfulfor hydrophobic contact. For the linkage atom between benzene andtriazine ring, negative charge atom is also demonstrated to be afavorable effect: 20A(NH)>20H(S), 21A(NH)>21C(O)>21B(S).

The contour plots of CoMSIA_SEH model with the structure 20A areillustrated in Figure 7. The circumstances of steric and electrostaticfields are similar to those of CoMFA model. The orange-coloredregions covering benzene ring strongly suggest that hydrophobicgroups are favorable to activity. It is no doubt that the benzene ring ofDATAs is one of the most important factors for potent bioactivity.

3D-QSAR model of L100I mutantThe PLS analysis of L100I is similar to that of wt. In CoMFA model,the contribution of steric and electrostatic fields is about half-half.

Figure 5: Alignment of training and test compounds.

Table 6: PLS Statistics of CoMFA and CoMSIA Models for wt-HIVRT

PLS statistics

CoMFA CoMSIA

SE SE SEH SEHD

Q2 0.828 0.767 0.797 0.785R2 0.933 0.910 0.914 0.896SEE 0.249 0.288 0.283 0.311F 233.813 169.393 177.157 143.544PLS components 3 3 3 3Field contribution

Steric 0.484 0.288 0.162 0.145Electrostatic 0.516 0.712 0.516 0.457Hydrophobic – – 0.321 0.276H-Donor – – – 0.122H-Acceptor – – – –

S, steric field; E, electrostatic field; H, hydrophobic field; D, hydrogen bonddonor field; PLS, partial least-squares.

Figure 6: Contour plot of CoMFA model for wt-HIVRT. Greencontours indicate regions where bulky groups increase activity,whereas yellow contours indicate regions where bulky groupsdecrease activity. Blue contours indicate regions where positivegroups increase activity, whereas red contours indicate regionswhere negative charge increases activity.

Li et al.


The best CoMSIA model is also the combination of SEH. The bestCoMSIA model suggests that electrostatic and hydrophobic fieldsare rather important.

Comparison with wt-HIVRT, the regions of green-, yellow-, andorange-colored become small (The contour mapof L100I mutantmodel is shown in Figure S5). To enhance the bioactivity or weakenthe drug resistance for L100I mutant, small and hydrophobic groupsat the 5¢ site of triazine ring, –Me other than halogen at the posi-tion 4¢ of benzene ring are favorable to activity.

3D-QSAR model of K103N mutantIt was found that the contribution of hydrogen bond donor fielddecreases from 12.2% of wt-HIVRT to 8.1% of K103N mutant inthe K103N CoMSIA model. This suggests that the hydrogen bonddonor force field becomes weak which is probably induced byK103N mutation. The hydrogen bond donor and acceptor fields areshown in Figure S6 for wt, and Figure S7 for K103N.

Figure S6 illustrated that cyan-colored regions near the atom of N9suggest hydrogen bond donor atom to be favorable for activity. Thisis agreement with the result of a hydrogen bond between N9 of20A and oxygen of Lys101 (wt) from MD simulation. Furthermore, ablue-colored region near nitrogen of benzonitrile indicates a hydro-gen bond acceptor favorable area. This is also consistent with theresult of MD simulation. For K103N mutant (shown in Figure S7),the red-colored region encircling the molecule indicates hydrogenbond acceptor unfavorable area around the molecule. This is neverfound in other mutants of HIVRT. Therefore, this mutation directlyaffects not only K103 but also the hydrogen bond around the inhibitor.

3D-QSAR model of Y181C mutantThe PLS analysis of Y181C mutant shows that the best CoMSIAmodel is the combination of SEHD that is different from the

previous one of SEH. MD simulation suggests that Y181C mutantentirely discards hydrophobic interaction. Therefore, the CoMSIAmodel of Y181C would have a poor q2 (0.581) for SEH model com-paring with other models of HIVRT.

The contour plots of CoMSIA_SEH model with structure 20A illus-trated in Figure S8. The hydrophobic contour plots have morewhite-colored and less orange-colored than those of wt-HIVRT. Ingeneral, the compounds with 2,4,6-triMe or 4-halogen at benzenering have high bioactivity. However, the bioactivity of these com-pounds almost decreases 30–50-fold for Y181C mutant, such as20C-F, 11J, 21A-C, and 21K-M. Notably, the bioactivity of the com-pounds 14K-P even decreases over 100-fold.

3D-QSAR model of Y188L mutantThe PLS statistics of CoMFA and CoMISA are summarized inTable S4. The contribution of hydrophobic field is almost 1 ⁄ 3 of thewhole force field, and the best CoMSIA model is the combinationof SEH. Y188L mutation did have a positive effect on the hydropho-bic contact. In the Figure S9, it is obvious that the orange-coloredregions are more bulky and concentrative than those of Y181Cmutant. It is consistent with the result of MD simulation that thehydrophobic interactions for Y188L are stronger than those ofY181L. The compounds with 2, 4, 6-triMe at benzene ring are stillthe most active ones, such as 20C-F, 11J, 21A-C, and 21K-M. Br atposition 4¢ is also better than Cl and I, for example,21M>21O>21N. This demonstrates that the substituent of benzenering is the most important group for hydrophobic interaction andconsistent with the result from wt-HIVRT.

Evaluation of CoMFA and CoMSIA modelsWe examine now the correlation models obtained with SYBYL 6.9between experimental and predicted activities (EA and PA) for fiveCoMFA and CoMSIA models. The correlations between EA and PAare shown in Figure 8. The correlation coefficient r2 of test setbetween EA and PA is 0.903, 0.988, 0.977, 0.983, and 0.971, withstandard error (SEE) equal to 0.195, 0.143, 0.129, 0.108, and0.089, respectively, for wild type, L100I, K103N, Y181C, andY188L CoMFA model. The good predictions for the test set con-firm the significant predicted ability of CoMFA model. Further-more, the correlation coefficient r2 of test set between EA andPA is 0.886, 0.967, 0.949, 0.819, and 0.901 with standard error(SEE) equal to 0.228, 0.222, 0.204, 0.323, and 0.178, respectively,for wild type, L100I, K103N, Y181C, and Y188L CoMSIA model.This indicates that each CoMSIA model also has good predictionability.

Bioactivity prediction of inhibitors excludingfrom training and test setThe prediction activity for five CoMFA models is listed in Table 7.For wild type, the difference between EA and PA of 14D, 14H, 14I,and 14J is very small, except 14C and 21Q. This suggests that 3D-QSAR model of wt-HIVRT has good prediction ability. For themutant model of L100I, the prediction suggests that each compoundmight have significant drug resistance. For the mutant model of

Figure 7: Contour plot of CoMSIA_SEH model for wt-HIVRT.White contours indicate regions where hydrophobicity is unfavor-able to activity, whereas orange for hydrophobicity favorable toactivity.



K103N, the drug resistance is relative lower than other mutants.This is same to the rule of drug resistance for 14L. For the mutantsof Y181C and Y188L, the prediction bioactivity also suggests that

each compound has drug desistance. This is consistent with MDsimulations that 14-series compounds form fewer hydrogen bondand hydrophobic contact than those of 20-series.

Figure 8: Correlations betweenexperimental and predicted activi-ties of 3D-quantitative structure–activity relationship.

Li et al.


Discussion

Molecular dynamics simulation versus 3D-QSARMolecular dynamics simulation suggests that there is the hydrogenbond between atom N9 of 20A and oxygen of Lys101 for wt andmutant HIVRT. Furthermore, 3D-QSAR model indicates that this siteis a potent hydrogen donor favorable area. Molecular dynamicsresults are in good consistent with those of 3D-QSAR. At the sametime, both two methods can explain that hydrophobic interactionsof DATAs are mostly owing to the presence of bulk & muti-substitu-ent on the benzene ring. The drug resistant mechanisms of Y181Cand Y188L mutants can be testified by these two methods. In sum-mary, the results of MD can be confirmed by those of 3D-QSAR.The key residues around 20A are shown in Figure 9. This figureillustrates the hydrogen bonding and hydrophobic interactions of20A.

Drug resistant mechanism of DATAs inhibitorsIn this research, we explore the drug resistant mechanism of DATAsfor wild type, L100I, K103N, Y181C, and Y188L HIVRT. The resultsfrom MD and 3D-QSAR have not only demonstrated the drug resis-tant mechanism of the slight change for the binding model but alsoexplored the influence of force field. L100I mutation brings a signifi-cant decrease in hydrophobic interaction with ring C of the ligand.Furthermore, this mutation actually weakens the fields of steric andhydrophobic. K103N mutation leads a huge decline of the strengthfor hydrogen bond force field. Y181C mutation lets the inhibitors

absolutely lose the hydrophobic interaction, while Y188L mutantattenuates the hydrophobic contacts with ring A for 14L. At thesame time, Y188L introduces the large cavity of binding pocket.This will induce that the inhibitor loses most of other contacts inthe hydrophobic pocket.

3D-QSAR and the modification blueprint

• The bulk & muti-substituent on the benzene ring is necessary.The 20-series compounds have a potent activity and good antimu-tant ability. In 11- and 21-series, the compounds with 2,4,6-triMeare the most active ones. We have demonstrated the substituent-benzene ring is the most important site with strong hydrophobicinteractions. To increase hydrophobic interactions, it might be favor-able to replace benzene ring with isostere or naphthaline ring

• The substituent of 5¢ site at triazine ring should be a smallgroup. In these series, H is demonstrated to be the best one. –NH2, –OH could be added to gain the potential electrostatic andhydrogen donor fields. –OMe and –NHMe are also suitable substit-uents based on the 3D-QSAR results.

• The linker of benzene and triazine ring is another certain site. Oand NH are the best groups and both have the ability to formhydrogen bond.

• Some residues with strong hydrophobic interaction are foundaround benzonitrile ring, such as Pro225, Phe227, Leu228, Trp229.Some alkyls on this ring can enhance the contacts with these resi-dues. Note also, the cyano group should be retained for its poten-tial hydrogen bond acceptor site.

• DATAs are a mature family of NNRTIs at the backbone. Twowing-like aryl rings are able to extend and embed into the pocket.It might be favor to add appropriate groups on the wings for morebinding sites.

Conclusion

Molecular dynamics simulation was used to study the drug resis-tance of L100I, K103N, Y181C and Y188L HIVRT. The results sug-gest that the active compound has quite stable hydrogen bonds.Molecular dynamics results also illustrate the robust interactionmodel which can explain durg resistance because of the mutationof key hydrophobic residues. The alignment between 20A and 14Lwithin HIVRT-binding pocket of wt and mutants suggests that theseanalog compounds have similar binding mode with HIVRT.

Then CoMFA and CoMSIA methods were used to construct quanti-tative structure–activity models for wild type and mutants. Thecross-validated q 2 values are larger than 0.55 for ten CoMFA andCoMSIA models, and the non-cross-validated r 2 values are largerthan 0.91. The correlation coefficient r 2 for test set is larger than0.81 for all models. The results show that these models possessgood prediction ability for wt and mutants HIVRT. Furthermore,

Figure 9: The key residues of wt-HIVRT around 20A.

Table 7: The prediction activity of five CoMFA models

No. Wt L100I K103N Y181C Y188L

14C 0.740 )1.553 0.595 )1.529 )0.42114D 0.711 )2.470 0.201 )0.245 )0.29514H 1.612 )1.754 0.244 1.239 0.39414I 1.010 )0.478 0.506 0.333 )0.25614J 1.053 )0.927 0.262 )0.073 )0.31921Q 0.501 )0.191 )0.666 0.169 )0.532



3D-QSAR model can also explain the different drug resistances forHIVRT mutants and is in good consistent with those of MD simulation.

Acknowledgments

This work is supported by the Innovation Activities Project ofShanghai Undergraduate Student (Grants No.IAP1046), by the Instru-mental Analysis Center of Shanghai Jiaotong University, by the Nat-ural Science Foundation of Shanghai (Grants No. 10ZR1414500),Sponsored by Shanghai Pujiang Program (10PJD010 and10PJ1406800), in part by grants from Ministry of Science andTechnology China (2011CB910204), and by Medical EngineeringCross Fund of Shanghai Jiaotong University (YG2010MS67).

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Note

aSt. Louis, MO: TRIPOS, Assoc., Inc.

Supporting Information

Additional Supporting Information may be found in the online ver-sion of this article:

Figure S1. Total energy versus minimization step for L100I-14L.



Figure S2. Simulation time versus root-mean-squared deviationof the backbones.

Figure S3. Hydrogen bond between 20A and wt-HIVRT.

Figure S4. Hydrogen bond between Asn103 and Tyr188 in ligand-free K103N mutant.

Figure S5. Contour plot of CoMSIA_SEH model of 20A for L100Imutant.

Figure S6. Contour plot of CoMSIA_DA model for wt.

Figure S7. Contour plot of CoMSIA_DA model for K103N.

Figure S8. Contour plot of CoMSIA_SEH model of Y181C.

Figure S9. Contour plot of CoMSIA_SEH model for Y188L.

Table S1. PLS Statistics of CoMFA and CoMSIA Models forL100I.

Table S2. PLS Statistics of CoMFA and CoMSIA Models ofK103N.

Table S3. PLS Statistics of CoMFA and CoMSIA Models forY181C.

Table S4. PLS Statistics of CoMFA and CoMSIA Models forY188L.

Table S5. pIC50 values of experimental and predicted activitiesby CoMFA models.

Table S6. pIC50 values of experimental and predicted activitiesby CoMSIA models.

Please note: Wiley-Blackwell is not responsible for the content orfunctionality of any supporting materials supplied by the authors.Any queries (other than missing material) should be directed to thecorresponding author for the article.

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