符海洋,燕志慧,林潤方,刁愛坡,肖冬光
(1. 天津科技大學(xué)生物工程學(xué)院,天津 300457;2. 南開大學(xué)藥學(xué)院,天津 300071)
趨化因子受體CXCR4非肽類抑制劑藥效團(tuán)的設(shè)計(jì)
符海洋1,燕志慧1,林潤方2,刁愛坡1,肖冬光1
(1. 天津科技大學(xué)生物工程學(xué)院,天津 300457;2. 南開大學(xué)藥學(xué)院,天津 300071)
趨化因子受體CXCR4 是HIV進(jìn)入宿主細(xì)胞的輔助受體之一.為了研究其抗HIV抑制劑的構(gòu)效關(guān)系,6個(gè)結(jié)構(gòu)多樣性和擁有高活性的抑制劑被作為訓(xùn)練集來構(gòu)建藥效團(tuán)模型,然后對由活性抑制劑和非活性抑制劑構(gòu)成的測試集進(jìn)行篩選,通過應(yīng)用受試者工作特征(receiver operator characteristic,ROC)曲線和計(jì)算富集因子(enrichment factor,EF)的方法評估每個(gè)模型的預(yù)測能力與合理性.最終得到一個(gè)具有較高的ROC曲線下面積(area under curve,AUC)與富集因子的藥效團(tuán)模型,可用于指導(dǎo)新穎CXCR4抑制劑的設(shè)計(jì)及相關(guān)抗艾滋病藥物的研發(fā).
藥效團(tuán);CXCR4受體;抑制劑;ROC曲線;富集因子
趨化因子受體CXCR4是基質(zhì)細(xì)胞衍生因子1 (stromal cell-derived factor-1,SDF-1)的特異受體,后者是骨髓基質(zhì)細(xì)胞產(chǎn)生的CXC趨化蛋白,系統(tǒng)命名CXCL12(CXC chemokine ligand-12).CXCR4受體與CXCL12蛋白協(xié)同表達(dá)于多種組織中,如腦、心臟、腎、免疫系統(tǒng),在它們的運(yùn)轉(zhuǎn)與發(fā)育過程中起著相當(dāng)重要的作用[1],并參與體內(nèi)多種生理機(jī)制,如HIV-1病毒感染、造血和腫瘤遷移[2–3].CXCR4是由352個(gè)氨基酸組成的類視紫紅質(zhì)亞家族G蛋白偶聯(lián)受體(GPCR),具有7次跨膜結(jié)構(gòu)[4].當(dāng)HIV-1病毒入侵細(xì)胞過程時(shí),病毒刺突中的gp120蛋白與細(xì)胞膜表面的CD4復(fù)合物作用.在CD4的輔助下,gp120蛋白與CXCR4相互作用,使HIV-1病毒侵入細(xì)胞.因此,通過阻斷或影響CXCR4受體與配體結(jié)合,可以阻礙HIV-1病毒對正常組織的侵染.以CXCR4為靶標(biāo)的抗HIV抑制劑不斷被報(bào)道[5–7],包括雙環(huán)拉胺類抑制劑(AMD3100)、非拉胺類抑制劑(AMD070)和肽類抑制劑(T22).其中大多數(shù)抑制劑在體外有較好的抑制活性,但由于肽類抑制劑相對分子質(zhì)量大,不利于分子的穿膜和吸收,以至于口服活性低.因此,AMD類抑制劑已成為目前最有前景的CXCR4抑制劑.藥物之所以能夠發(fā)揮藥理作用是因?yàn)樗幬锓肿幽軌蛞蕴囟ǖ娜S結(jié)構(gòu)與體內(nèi)受體發(fā)生結(jié)合作用,在三維空間中,藥物分子特定的構(gòu)象是呈現(xiàn)藥物活性的必要前提.在計(jì)算機(jī)輔助藥物設(shè)計(jì)中,藥效團(tuán)模型方法可以最大限度地利用已知的針對某一特定受體的活性分子三維結(jié)構(gòu)信息來篩選并發(fā)現(xiàn)新的活性化合物.目前,報(bào)道基于AMD類抑制劑的藥效團(tuán)模型很少.為了研究其構(gòu)效關(guān)系,本文選擇了具有可靠類藥性三維結(jié)構(gòu)特征的6個(gè)AMD類抑制劑構(gòu)建藥效團(tuán)模型,然后通過計(jì)算ROC曲線下面積(area under curve,AUC)和富集因子(enrichment factor,EF)對藥效團(tuán)模型的數(shù)據(jù)庫篩選能力進(jìn)行評價(jià)并選出最優(yōu)的模型.
1.1 材料
224個(gè)CXCR4抑制劑被用來構(gòu)建和驗(yàn)證藥效團(tuán)模型[8–22],其中6個(gè)抑制活性最佳的分子作為訓(xùn)練集以構(gòu)建藥效團(tuán)模型[8–12].抑制活性使用pIC50值表征,保證了所構(gòu)建模型可以表征能夠較好地與CXCR4受體結(jié)合并產(chǎn)生抑制作用的化合物之物理化學(xué)特征,分子的結(jié)構(gòu)與活性值如圖1所示.在Schr?dinger的軟件中[23],構(gòu)建的6個(gè)分子在Ligprep模塊中用OPLS 2005力場對其結(jié)構(gòu)進(jìn)行優(yōu)化,最后使用MCMM(monte carlo multiple minimum)方法對經(jīng)過優(yōu)化的分子進(jìn)行構(gòu)象搜索,設(shè)定最大分子位置偏差為0.05nm以除去冗余構(gòu)象.
圖1 用于構(gòu)建藥效團(tuán)的抑制劑化學(xué)結(jié)構(gòu)及其抑制活性(pIC50)Fig. 1 Inhibitor structures used for pharmacophore generation and their inhibitory activities(pIC50)
1.2 模型的構(gòu)建
使用Schr?dinger軟件中的Phase模塊構(gòu)建藥效團(tuán)模型步驟如下[23]:(1)導(dǎo)入經(jīng)過前處理的分子集,(2)選擇并編輯活性位點(diǎn),(3)尋找分子間通用的藥效團(tuán)結(jié)構(gòu),(4)對得到的藥效團(tuán)模型假設(shè)進(jìn)行打分.當(dāng)給出1個(gè)與同一特定受體作用的分子集合后,Phase采用K-point藥效團(tuán)分析法對每一個(gè)活性的分子進(jìn)行分析,統(tǒng)計(jì)每個(gè)分子的藥效團(tuán)位點(diǎn),包括氫鍵受體位點(diǎn)、氫鍵供體位點(diǎn)、疏水區(qū)域和芳香性環(huán).建立模型后,Phase會對每個(gè)模型進(jìn)行打分,在對藥效團(tuán)模型打分過程中,設(shè)定假設(shè)模型的RMSD上限值為0.11nm,以提高打分效率.
1.3 測試集的生成與驗(yàn)證
為驗(yàn)證模型的準(zhǔn)確性與有效性,構(gòu)建了一個(gè)測試分子集.分子集由剩余的218個(gè)CXCR4抑制劑[8–22]和由DecoyFinder軟件搜索出的5,261個(gè)Decoy組成.Decoy是一些針對特定靶標(biāo)無活性的分子,如不可能與靶標(biāo)發(fā)生作用的分子,它們通常用來計(jì)算配體富集因子(ligand enrichment factor)以驗(yàn)證針對特定靶點(diǎn)虛擬篩選流程的準(zhǔn)確性.在Decoy Finder軟件中導(dǎo)入218個(gè)抑制劑,選擇搜索目標(biāo)為Zinc數(shù)據(jù)庫.DecoyFinder可以在數(shù)據(jù)庫中和給定的活性分子集中分別計(jì)算MACCS指紋特征和五點(diǎn)物理性質(zhì)描述符(physical descriptors),通過比對活性分子和數(shù)據(jù)庫中潛在的Decoy間兩個(gè)特征的差別,搜索出針對CXCR4受體的Decoy分子[24].
使用Phase模塊中的Find Matches to Hypothesis對上面獲得的分子三維結(jié)構(gòu)數(shù)據(jù)庫進(jìn)行基于藥效團(tuán)模型的搜索.對于單個(gè)化合物,搜索過程中都要對該化合物的每個(gè)三維構(gòu)象進(jìn)行比對,最小滿足位點(diǎn)個(gè)數(shù)設(shè)置為5個(gè),最大允許位點(diǎn)偏離距離設(shè)置為0.2nm.搜索完成后,分子庫中符合藥效團(tuán)假設(shè)的分子被篩選出來,并計(jì)算出這些分子的擬合度(fitness).然后通過計(jì)算EF、AUC值驗(yàn)證每一個(gè)藥效團(tuán)模型預(yù)測能力.
EF是評價(jià)篩選流程優(yōu)劣的一個(gè)重要指標(biāo),它是指數(shù)據(jù)集中活性分子的命中率與初始活性分子濃度的比值,這個(gè)指標(biāo)用于評價(jià)篩選流程中命中有效活性分子的陽性率.富集因子計(jì)算公式[25]為EF=m/n.其中:m=NActive Fit/NTotal Fit,NActive Fit指篩選結(jié)果中活性分子的個(gè)數(shù),NTotalFit指篩選所得分子總數(shù);n=NAll Active Comp/NAll Comp,NAll Active Comp指被篩選分子中活性分子個(gè)數(shù),為218個(gè),NAllComp指被篩選分子總個(gè)數(shù),為5,479個(gè).
對所得到的數(shù)據(jù)表進(jìn)行ROC曲線分析.ROC曲線表征的是敏感度(sensitivity)和特異性(specificity)之間的關(guān)系[26–27].評價(jià)篩選流程的好壞,則會使用到AUC值,即ROC曲線下面積.當(dāng)被評價(jià)的篩選流程能夠篩選出同等濃度的非活性Decoy分子時(shí),篩選出越多的活性分子就代表該流程對活性分子的敏感度更高.AUC值是以具體數(shù)值表示ROC曲線下面積,取值范圍為[0–1]:“0”表示篩選結(jié)果包含全部的Decoy分子,不包含活性分子;“1”表示篩選結(jié)果包含全部活性分子,不包含Decoy分子.在曲線的制作過程中,將全部活性分子的化合物名稱改為“1”,全部Decoy分子的化合物名稱改為“0”,設(shè)置化合物名稱變量為檢驗(yàn)變量,F(xiàn)itness值為狀態(tài)變量.選擇其中富集因子較大,AUC值較高的模型作為CXCR4受體抑制劑的藥效團(tuán)模型.
將與藥效團(tuán)模型擬合度較高的活性分子使用Schr?dinger軟件的Glide模塊[23]與CXCR4受體結(jié)構(gòu)(PDB 3ODU)[4]進(jìn)行分子對接,分析配體與受體間作用模式、藥效團(tuán)特征與蛋白結(jié)構(gòu)特征間的聯(lián)系與吻合程度,進(jìn)而驗(yàn)證所獲得的藥效團(tuán)模型的合理性.
在Schr?dinger軟件包中[23],使用Phase模塊構(gòu)建CXCR4抑制劑的藥效團(tuán)模型,得到9個(gè)五點(diǎn)藥效團(tuán)假設(shè)模型.模型由氫鍵供體(A)、疏水中心(H)、正電中心(P)和芳香環(huán)(R)中的3個(gè)元素或4個(gè)元素組成.9個(gè)藥效團(tuán)模型的Survival值范圍為2.538~2.803,Selectivity值范圍為2.332~2.501,均具有較好的選擇性和代表性.為了考察所得藥效團(tuán)模型的數(shù)據(jù)庫篩選能力,用以上所得9個(gè)藥效團(tuán)模型為提問結(jié)構(gòu),搜索由5,479個(gè)化合物構(gòu)成的測試集(218個(gè)CXCR4受體抑制劑和5,261個(gè)Decoy分子,其中不含構(gòu)建藥效團(tuán)模型所用的6個(gè)抑制劑化合物),并用AUC值和EF值評價(jià)9個(gè)藥效團(tuán)模型的合理性與篩選效率,結(jié)果見表1.
運(yùn)用藥效團(tuán)進(jìn)行虛擬篩選的目的是希望具有抑制活性的化合物分子出現(xiàn)在篩選結(jié)果中,而對CXCR4受體無抑制作用的Decoy分子則盡可能不被篩選出來.在統(tǒng)計(jì)結(jié)果上表征為ROC曲線下面積越大越好,EF值更高,借此可以選擇藥效團(tuán)模型中最優(yōu)的一個(gè),以用于指導(dǎo)和篩選抗HIV活性的CXCR4抑制劑.由表1可知,AHPRR.34和AHPRR.38模型雖然能得到更多的活性分子,但是這兩個(gè)模型的假陽性都比較高,不適合進(jìn)行數(shù)據(jù)庫的篩選.剩余的7個(gè)模型對于活性分子的識別能力幾乎都一樣,不同的是對非活性分子的識別能力.其中與化合物2擬合最好的模型HPRRR.114(圖2)能從5,261個(gè)Decoy分子中得到更少的非活性分子(95個(gè)),EF值為15.95,AUC值為0.913(ROC曲線如圖3所示),表明HPRRR.114模型的篩選結(jié)果中含有更大比例的活性分子,且活性預(yù)測的偏差較小,證明該模型具有較好的活性預(yù)測能力.因此,選擇該模型用以進(jìn)一步研究小分子抑制劑與CXCR4受體的構(gòu)效關(guān)系.
表1 藥效團(tuán)模型篩庫能力參數(shù)Tab. 1Performance of pharmacophore models in library screening
圖2 藥效團(tuán)模型HPRRR.114與化合物2的疊合圖Fig. 2 Compound 2 mapped into model HPRRR.114
圖3 HPRRR.114對測試集篩選的ROC曲線圖Fig. 3 The ROC curve of HPRRR.114 in screening tests
將化合物2以其與藥效團(tuán)擬合度最高的構(gòu)象對接到受體蛋白中,分析受體與配體的相互作用(對接結(jié)果如圖4(a)所示.圖中白色配體為化合物2,化合物2上黑色為氮原子).由圖4(a)可以看到化合物2的吡啶環(huán)與受體口袋中TRP.94的芳香稠環(huán)及HIS.113的咪唑環(huán)發(fā)生了π-π相互作用,與芳香環(huán)(R)特征相符合;配體中心處帶有正電荷的氮原子距離受體的ASP.97較近.Wu等[4]的研究表明,ASP.97的側(cè)鏈可以與配體帶正電荷的部分產(chǎn)生鹽橋,表明在這一位置存在正電荷藥效團(tuán)特征(P)對于生物分子的活性發(fā)揮與結(jié)構(gòu)穩(wěn)定有很重要的作用.與此同時(shí),對受體的親水/疏水趨向面進(jìn)行分析(結(jié)果如圖4(b)所示,亮白色代表疏水趨向,黑色代表親水趨向),發(fā)現(xiàn)化合物2吡啶基團(tuán)上的甲基正好深入了活性口袋中的疏水部分,從而與周圍疏水性氨基酸殘基發(fā)生范德華力作用,加強(qiáng)了對接產(chǎn)物的穩(wěn)定性,而在之前的藥效團(tuán)構(gòu)建結(jié)果中,疏水特征位點(diǎn)(H)恰好擬合于該甲基位點(diǎn)上.由此可見,對接結(jié)果顯示的配體與受體間相互作用的模式與藥效團(tuán)特征比較吻合.
圖4 化合物2與受體蛋白的作用模式以及受體的親水/疏水趨向面圖Fig. 4 The interaction between compound 2 and the receptor,and the hydrophilicity/hydrophobicity surface
本次研究利用Schr?dinger軟件構(gòu)建CXCR4抑制劑的藥效團(tuán)模型,得到一個(gè)含有5個(gè)藥效團(tuán)特征的模型,包括1個(gè)疏水中心(H)、1個(gè)正電中心(P)、3個(gè)芳香環(huán)(R).疏水中心匹配在化合物的3–甲基吡啶的甲基上;3個(gè)芳香性環(huán)分別匹配在了吡啶環(huán)上和苯并咪唑的2個(gè)稠環(huán)上;正電中心匹配在了化合物中心的陽性氨基上,并且各個(gè)位點(diǎn)之間的距離與位置均要滿足一定的空間限制(藥效團(tuán)模型結(jié)構(gòu)見圖2).綜合比對得到的藥效團(tuán)模型和CXCR4蛋白結(jié)構(gòu)模型發(fā)現(xiàn),CXCR4受體抑制劑與受體活性位點(diǎn)之間的相互作用形式主要包含氫鍵相互作用、π-π相互作用和疏水相互作用,并且各個(gè)基團(tuán)之間滿足一定的空間限制,從而產(chǎn)生抑制CXCR4受體的活性作用.
本文應(yīng)用6個(gè)結(jié)構(gòu)多樣性和擁有高活性的抗HIV的抑制劑來構(gòu)建藥效團(tuán)模型,然后對測試集進(jìn)行篩選,通過應(yīng)用ROC曲線和計(jì)算EF值的方法評估每個(gè)模型的預(yù)測能力與合理性.最終得到1個(gè)最優(yōu)的模型HPRRR.114,為進(jìn)一步指導(dǎo)和篩選潛在的抗HIV藥物分子提供了理論依據(jù).
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責(zé)任編輯:郎婧
Pharmacophoric Design for Non-peptide Inhibitors of Chemokine Acceptor CXCR4
FU Haiyang1,YAN Zhihui1,LIN Runfang2,DIAO Aipo1,XIAO Dongguang1
(1. College of Biotechnology,Tianjin University of Science & Technology,Tianjin 300457,China;
2. College of Pharmacy,Nankai University,Tianjin 300071,China)
CXCR4 is one of several chemokine receptors that HIV can use to infect host cells. To study the structure-activity relationship between chemokine receptor CXCR4 and its inhibitors,six inhibitors with structural diversity and high-activity were used to develop pharmacophore models,and then a molecular library consisting of active and decoy ligands was applied to evaluate the predicting capability and rationality of every model by receiver operator characteristic(ROC)curve and enrichment factor(EF)value. The result should that a pharmacophore model with higher area under ROC curve and EF value was obtained,which can be used to guide the design of novel CXCR4 inhibitors and the development of the related anti-HIV drugs.
pharmacophore;CXCR4 receptor;inhibitor;ROC curve;EF value
O642.3
A
1672-6510(2014)02-0006-05
10.13364/j.issn.1672-6510.2014.02.002
2013–11–15;
2013–12–23
國家高技術(shù)研究發(fā)展計(jì)劃“863計(jì)劃”資助項(xiàng)目(2012AA023408)
符海洋(1989—),男,河南內(nèi)鄉(xiāng)人,碩士研究生;通信作者:刁愛坡,教授,diaoaipo@tust.edu.cn.