WO2020047962A1 - 区分物质雄激素与抗雄激素效应模型的构建和应用 - Google Patents

区分物质雄激素与抗雄激素效应模型的构建和应用 Download PDF

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WO2020047962A1
WO2020047962A1 PCT/CN2018/111676 CN2018111676W WO2020047962A1 WO 2020047962 A1 WO2020047962 A1 WO 2020047962A1 CN 2018111676 W CN2018111676 W CN 2018111676W WO 2020047962 A1 WO2020047962 A1 WO 2020047962A1
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androgen
distinguishing
ligand
substance
model
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刘红玲
石来昊
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Nanjing University
Nanjing Tech University
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Nanjing Tech University
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    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/20—Protein or domain folding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/30—Drug targeting using structural data; Docking or binding prediction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
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    • G16C20/60—In silico combinatorial chemistry
    • G16C20/64—Screening of libraries

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  • the invention belongs to the technical field of testing or analysis by measuring chemical or physical properties, and particularly relates to a model for distinguishing substance androgen and anti-androgenic effect, a construction method and application thereof.
  • endocrine disruptors refer to environmental substances that can affect the homeostasis, reproduction and development of organisms by interfering with the synthesis, secretion, transport, metabolism, binding and removal of endogenous hormones.
  • Environmental androgens Substances are an important class of pollutants. Among them, substances that can mimic natural androgens to activate androgen receptors are called pseudoandrogens, and substances that inhibit natural androgens are called antiandrogens.
  • In vitro experiments include cell proliferation experiments, reporter gene experiments, competitive binding experiments, and yeast two-hybrid experiments.
  • In vivo experiments mainly use animal feeding and weight-loss experiments after excision of hormone-dependent tissues (such as prostate, seminal vesicles, uterus, etc.) from rodents (such as mice), and in vivo biomarker experiments. Both in vitro and in vivo experiments have their own advantages and disadvantages. In vivo experiments have been reasonably developed and applied to various high-throughput test screening methods to cope with the huge number of potentially harmful compounds, as well as the huge overhead and moral hazard of animal in vivo testing.
  • computational toxicology refers to the method of developing mathematical or computer models to better understand or predict the interference effects of compounds by integrating data from different sources such as in vivo, in vitro experiments, and computer simulations.
  • Traditional quantitative structure-activity relationship (QSAR) has been widely used and achieved good results.
  • the existing molecular docking only focuses on the docking of specific conformations.
  • the molecular dynamics method is also a conformational change that runs for a limited time, and only focuses on a class of structurally similar compounds, and is relatively single in the recognition of action patterns.
  • Wang Xiaoxiang and others applied molecular dynamics to screen nuclear receptor-mediated endocrine disruptors, but this method only judges the fluctuation of the root-mean-square deviation of the receptor after docking, and the prediction accuracy is low.
  • the purpose of the present invention is to provide a pseudomimetic / anti-androgenic activity recognition for a variety of compounds with different structures based on molecular dynamics analysis of interactions and energy changes between ligands and receptors, and allosteric effects of receptors. method.
  • the present invention provides a model for distinguishing substance androgen from anti-androgenic effect, a construction method and application thereof.
  • the invention is realized in this way, a model for distinguishing substance androgen from antiandrogenic effect, the model for distinguishing substance androgen from antiandrogenic effect adopts SYBYL's Surflex-Dock program to dock the ligand into AR.
  • Another object of the present invention is to provide a method for constructing a model for distinguishing substance androgen from an antiandrogenic effect.
  • the method for constructing a model for distinguishing substance androgen from an antiandrogenic effect includes the following steps:
  • Step 1 Pretreat the protein, add hydrogen atoms, and add charge before docking. Use Automatic mode to search for binding pockets during docking.
  • the threshold is 0.5, the expansion coefficient is 0, and the default value is 17;
  • step two 20 conformations are generated when each ligand is docked with the receptor.
  • the highest scoring structure is used as the most likely biologically active conformation, and the conformation is used as the initial conformation of the MD simulation.
  • Another object of the present invention is to provide a method for differentiating material androgen and antiandrogenic effect by using the model for distinguishing material androgen and antiandrogenic effect, and a method for distinguishing material androgen and antiandrogenic effect. It includes the following steps:
  • Step 1 Use the Sketch Module in SYBYL7.3 to construct the structure of the ligand molecule and the positive control, minimize the energy of the ligand molecule, optimize it using the Powell method, give the Gasteiger-Huckel charge, and use the Tripos standard molecular force field Energy optimization; the energy convergence criterion for the energy optimization of the Tripos standard molecular force field is The maximum number of iterations is 1000.
  • Step 2 Introduce the androgen receptor sequence into the Swiss-Model server, and use the androgen receptor structure in the excited state as a template to establish a homologous model to establish the activation conformation of human AR;
  • Step 3 Use SYBYL's Surflex-Dock program to dock the ligand into the AR, pre-treat the protein before docking, add hydrogen atoms, and give charge.
  • Each ligand generates 20 conformations when docked with the receptor to score The highest structure is used as the most likely biologically active conformation, and the conformation is used as the initial conformation of the MD simulation;
  • Step 4 The MD simulation uses the CHARMM27 force field.
  • a TIP3P spherical water molecular layer is added around the composite system.
  • the composite system is 1.5 nm away from the solvent edge.
  • Chloride ions are added to neutralize the charge in the system.
  • the composite system uses the steepest descent method.
  • Energy optimization Energy optimization will increase the temperature from 0K to 300K to balance the system within 40ps, and maintain 300K equilibrium for 1ns under one atmosphere. After that, molecular dynamics simulation was performed, and the simulation was performed for 30 ns, in which the step size was 2 fs, which was saved every 2 ps.
  • the invention adopts a method of constructing a molecular dynamics model.
  • Molecular dynamics simulation is a research method for simulating the movement process of a molecular system, the structure and properties of a computing system, describing the movement trajectory of a compound in space, and then simulating the microscopic behavior of molecules.
  • the re-docking of the molecular docking system and the exploration of the actual conformation of the receptors have avoided problems in the experimental method.
  • Molecular and dynamic methods were used to simulate the interaction of the androgen receptor H12 chain with the ligand-binding domain and change in distance to identify the pseudo / anti-androgen receptor properties of organics.
  • FIG. 1 is a flowchart of a method for constructing a model for discriminating substance androgen and antiandrogenic effect according to an embodiment of the present invention.
  • FIG. 2 is a flowchart of a method for distinguishing a substance androgen from an antiandrogenic effect according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram for evaluating the stability of a system for distinguishing between an androgen receptor antagonist and an agonist according to an embodiment of the present invention.
  • FIG. 4 is a residue form diagram of the interaction between androgen receptor antagonist and agonist ligand DHT, HFT, TBB, TBCO, TBPH, TBBPA, and BDE155 provided by an embodiment of the present invention.
  • Fig. 5 is a schematic diagram showing the distances between the DHT-binding receptors H874 and W741 and the R871 and H12 helix before the simulation of the androgen receptor antagonist and agonist discrimination method provided by the embodiment of the present invention.
  • FIG. 6 is a schematic diagram showing the distances between the DHT-receptor H874 and W741 and the R871 and H12 helices after the simulation of the androgen receptor antagonist and agonist discrimination method provided by the embodiment of the present invention.
  • FIG. 7 is a schematic diagram showing a distance between an R871 and an H12 helix of an HFT-binding receptor, an androgen receptor antagonist and an agonist discrimination method, according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a binding energy of an androgen receptor antagonist and an agonist discrimination method according to an embodiment of the present invention.
  • the present invention establishes a model for distinguishing substance androgenic effects from anti-androgenic effects through molecular dynamics simulation of the binding process between androgen receptor and a series of ligands.
  • the model for distinguishing between androgen and anti-androgenic effects uses SYBYL's Surflex-Dock program to dock the ligand into the AR.
  • a method for constructing a model for distinguishing substance androgen from an antiandrogenic effect includes the following steps:
  • S101 Pretreat the proteins before adding them, add hydrogen atoms, and add electric charge. Use Automatic mode to search for binding pockets during docking.
  • the threshold (threshold) is 0.5 and the expansion coefficient (bloat) is 0. Both are the default value 17;
  • Each ligand generates 20 conformations when it is docked with the receptor.
  • the highest scoring structure is the most likely biologically active conformation, and the conformation is used as the initial conformation of the MD simulation.
  • the method for distinguishing substance androgenic and antiandrogenic effects provided by an embodiment of the present invention includes the following steps:
  • S201 Use the SketchModule in SYBYL7.3 to construct the structure of the ligand molecule and the positive control. Minimize the energy of the ligand molecule, optimize it using Powell method, give the Gasteiger-Huckel charge, and use the Tripos standard molecular force field for energy optimization;
  • the human AR receptor sequence is from Uniprot.
  • the existing AR structures are all activated conformations, lacking AR inhibitory structures, so the homologous modeling is used to establish the activated conformation of human AR;
  • S203 Use SYBYL's Surflex-Dock program to dock the ligand into AR, pre-treat the protein before docking, add hydrogen atoms, and give charge. Each ligand generates 20 conformations when docked with the receptor, giving the highest score As the most likely biologically active conformation, and the conformation as the initial conformation of the MD simulation;
  • the ligand molecules provided in the embodiments of the present invention are TBB, TBCO, TBPH, TBBPA, and BDE155.
  • TBB, TBCO, TBPH, and TBBPA have androgen receptor antagonistic properties in previous reports.
  • the energy convergence criterion for the energy optimization of the Tripos standard molecular force field provided by the embodiment of the present invention is The maximum number of iterations is 1000.
  • the activation conformation of human-derived AR provided by the embodiment of the present invention is to use the AR-LBD combined with 5 ⁇ -DHT as a template on the Swiss-model platform to construct the activation conformation of human-derived AR.
  • the automatic mode search combined pocket used during docking has a threshold value of 0.5 and an expansion coefficient of 0, which are all default values.
  • the steepest descent method used in the embodiment of the present invention for energy optimization is to increase the temperature from 0K to 300K to balance the system within 40ps, and maintain 300K equilibrium for 1ns under one atmospheric pressure.
  • the free energy generated by binding was calculated by the mmpbsa method and a trajectory file generated after ligand binding to the receptor 22 .
  • the formula for mmpbsa to calculate free energy is as follows:
  • ⁇ G binding G complex- (G protein + G ligand );
  • the ⁇ G complex in the above formula is the total free energy of the protein-ligand complex, and Gprotein and Gligand are the total energy of the protein and ligand separated in the solvent, respectively.
  • Each G x can be calculated by the following formula:
  • the EMM in the above formula is the average molecular mechanical potential energy in a vacuum
  • TS represents the contribution of entropy
  • T and S represent the temperature and entropy, respectively
  • Gsolvation is the free energy of solvation.
  • the EMM includes bond energy, electrostatic interaction, and van der Waals' interaction, as shown in the following formula:
  • E MM E bonded + E electrostatic + E vdW ;
  • the solvation free energy consists of two parts: polar and non-polar solvation free energy, as shown in the following formula:
  • G solvation G polar + G non-polar ;
  • Gnon-polar is calculated based on the solvent contactable surface area (SASA) model.
  • the RMSD of the antagonist's ligand cannot reach equilibrium before 15 ns, or the RMSD is higher than 0.1 nm after equilibrium.
  • the agonist can reach equilibrium quickly after binding, and the RMSD is less than 0.1nm after equilibrium.
  • Resistance to antiandrogen drugs such as flutamide and enzalutamide is caused by mutations in the residue sequence of the AR receptor. Further research on AR mutations has shown that L701H, W741L, H874Y, T877A, and M895T mutations will cause changes in the hydrogen bonding network between these amino acid residues, resulting in resistance to antagonists such as flutamide. From this observation, the changes in the interaction between these amino acid residues after the androgen receptor binds these ligands.
  • the hydrophilic group of W741 faces H874 under the action of the ligand, and the distance between the two is reduced from the original 3.9A to 2.2A, which simultaneously drives the residue R203 around H874 to make it
  • the distance between the H12 helix and the H12 helix was reduced to 2.2A, and the hydrogen bond between the H11 helix and the H12 helix was maintained.
  • the M895 and LBD regions on the H12 helix are close to each other.
  • the binding energy of the amino acid on the H12 helix is significantly smaller than that with the agonist. This shows that the binding strength of H12 and LBD decreases after binding to the antagonist, and H12 tends to be unstable compared to the agonist-bound AR receptor.

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Abstract

一种区分物质雄激素与抗雄激素效应的模型及构建方法和应用，区分物质雄激素与抗雄激素效应的模型采用SYBYL的Surflex-Dock程序将配体对接到AR内。通过双氢睾酮（DHT）与羟基氟他胺（HFT）与AR的结合模式建立，并进一步用于新型溴代阻燃剂，发现在模拟结束后，拮抗剂结合的H12螺旋与其他结构之间的相符作用明显减弱，使得H12螺旋的稳定性下降，而激动剂则能维持H12的稳定性；通过这种方法可以对物质的激动以及拮抗进行初步的筛分。

Description

区分物质雄激素与抗雄激素效应模型的构建和应用 技术领域
本发明属于借助于测定化学或物理性质来测试或分析技术领域，尤其涉及一种区分物质雄激素与抗雄激素效应的模型及构建方法和应用。
背景技术
目前，业内常用的现有技术是这样的：化学物质的内分泌干扰效应是其综合毒性的重要组成部分。根据美国EPA的定义，内分泌干扰物是指能通过干扰内源性激素的合成、分泌、转运、代谢、结合和去除过程来影响生物的稳态、生殖和发育过程的环境物质，其中环境雄激素类物质是一类重要的污染物，其中能模拟天然雄激素激活雄激素受体的物质称为拟雄激素，而抑制天然雄激素物质的称为抗雄激素。近年来研究发现，很多野生动物，如鱼类、鸟类等，会出现不同程度的生殖紊乱、性器官畸形和雄性雌性化等症状。之前这种现象的机理研究主要集中在环境雌激素上。野外调查发现雌酮，雌二醇，雌马酚等雌激素无法解释辽东湾野生梭鱼雌雄同体的高发生率。随着研究的推进，辽东湾中具有抗雄激素效应的p，p’-DDE以及雌马酚可能是其原因。现有的筛选方法主要有实验法和计算法。实验法可分为体外法和体内法。体外实验包括细胞增殖实验，报告基因实验，竞争结合实验，酵母双杂交实验。体内实验主要使用动物喂养和啮齿类动物(如鼠等)激素依赖组织(如前列腺、精囊、子宫等)割除后增重实验，体内生物标志实验等。体外试验与体内实验各有优缺点，体内实验被合理开发应用到各种高通量测试筛选方法中，以应对数量巨大的潜在危害化合物，以及动物体内测试的巨大开销和道德风险。但是体外实验无法完全模拟动物体内复杂的分布，吸收，代谢，排出过程以及其它因素的影响，因此无法得到体内测试般准确的结果，且这两个方法都耗时费力，无法应付数量日益增长的化学品。因此，亟需发展化学品内分泌干扰效应筛选的计算毒理学。 计算毒理学方法指通过综合体内、体外实验和计算机模拟等不同来源的数据，开发数学或计算机模型，以更好理解或预测化合物干扰效应的方法。传统的定量构效关系(QSAR)已被大量应用，并取得较好的效果。但是现实中的化合物结构相似，却有的具有活性有的没有活性，这使得单纯依赖结构来预测效应的传统QSAR方法无法预测活性的有无。而分子对接与分子动力学是模拟配体与受体结合成稳定复合体时两者的相互作用。通过两者相互作用这种方法来识别潜在的内分泌干扰物。
但是现有的分子对接只关注特定构象的对接情况。分子动力学方法也是运行有限时间的构象变化，且只关注于一类结构类似的化合物，在作用模式识别上也相对单一。例如王小享等将分子动力学应用于筛选核受体介导的内分泌干扰物，但是该方法仅对受体对接后的均方根偏差的波动情况进行判断，预测精度低。
综上所述，现有技术存在的问题是：
(1)现有的体内体外筛选方法无法应付数量日益庞大的化学品。
(2)现有的传统QSAR无法区分活性，而分子对接与分子动力学往往只关注化合物的某个特定构象和有限时间内的构象变化。
解决上述技术问题的难度和意义：
针对每一种环境有机污染物的拟/抗雄激素效应的生物实验筛查方法需要消耗大量时间和财力。目前已有的虚拟筛选方法存在无法预测活性有无(QSAR)或者只能预测一类物质的活性有无的问题。本发明的目的是基于分子动力学分析配体与受体之间的相互作用与能量变化，以及受体的变构效应来提供一种对于多种不同结构的化合物的拟/抗雄活性识别的方法。
发明内容
针对现有技术存在的问题，本发明提供了一种区分物质雄激素与抗雄激素效应的模型及构建方法和应用。
本发明是这样实现的，一种区分物质雄激素与抗雄激素效应的模型，所述区分物质雄激素与抗雄激素效应的模型采用SYBYL的Surflex-Dock程序将配体对接到AR内。
本发明的另一目的在于提供一种所述区分物质雄激素与抗雄激素效应的模型的构建方法，所述区分物质雄激素与抗雄激素效应的模型的构建方法包括以下步骤：
步骤一，对接前对蛋白质进行预处理，添加氢原子，赋予电荷，对接时使用Automatic模式搜索结合口袋，阈值为0.5，膨胀系数为0，均为默认值17；
步骤二，每个配体与受体对接时产生20个构像，以打分最高的结构作为最有可能的生物活性构象，并以构像作为MD模拟的初始构象。
本发明的另一目的在于提供一种应用所述区分物质雄激素与抗雄激素效应的模型的区分物质雄激素与抗雄激素效应的方法，所述区分物质雄激素与抗雄激素效应的方法包括以下步骤：
步骤一，使用SYBYL7.3中的Sketch Module构建配体分子以及阳性对照的结构，对配体分子进行能量最小化，采用Powell方法进行优化，赋予Gasteiger-Huckel电荷，并使用Tripos标准分子力场，能量优化；Tripos标准分子力场进行能量优化的能量收敛标准为
Figure PCTCN2018111676-appb-000001
最大迭代次数为1000次。
步骤二，将雄激素受体序列导入Swiss-Model服务器，以激动状态的雄激素受体结构为模版同源模建，建立人源AR的激活构象；
步骤三，采用SYBYL的Surflex-Dock程序将配体对接到AR内，对接前对蛋白质进行预处理，添加氢原子，赋予电荷，每个配体与受体对接时产生20个构像，以打分最高的结构作为最有可能的生物活性构象，并将构像作为MD模拟的初始构象；
步骤四，MD模拟采用CHARMM27力场，在复合体系周围加上TIP3P球形水分子层，复合物体系距离溶剂边缘为1.5nm，加入氯离子以中和体系中的电 荷，复合体系使用最速下降法进行能量优化，能量优化在40ps内将温度从0K上升至300K对体系进行平衡，在一个大气压下保持300K平衡1ns。之后进行分子动力学模拟，模拟进行30ns，其中步长2fs，每2ps保存一次。
综上所述，本发明的优点及积极效果为：
通过双氢睾酮(DHT)与羟基氟他胺(HFT)与AR的结合模式建立，并进一步用于新型溴代阻燃剂，发现在模拟结束后，拮抗剂结合的H12螺旋与其他结构之间的相符作用明显减弱，使得H12螺旋的稳定性下降，并导致H12螺旋与配体结合域的距离轻微增加，而激动剂则能维持H12的稳定性；通过这种方法可以对多种结构不相似的物质的激动以及拮抗进行初步的筛分。
本发明采用建构分子动力学模型的方法，分子动力学模拟是模拟分子体系的运动过程、计算系统的结构和性质，描述化合物于空间中的运动轨迹进而模拟分子微观行为的研究方法，可用于对分子对接体系的再度对接以及探索受体实际构象，避免了实验法中出现的问题。通过分子动力学方法模拟雄激素受体H12链与配体结合域的相互作用以及距离的变化来识别有机物的拟/抗雄激素受体性质。
附图说明
图1是本发明实施例提供的区分物质雄激素与抗雄激素效应的模型的构建方法流程图。
图2是本发明实施例提供的区分物质雄激素与抗雄激素效应的方法流程图。
图3是本发明实施例提供的雄激素受体拮抗剂与激动剂区分的体系稳定性评估示意图。
图4是本发明实施例提供的雄激素受体拮抗剂与激动剂区分方法的配体DHT，HFT，TBB，TBCO，TBPH，TBBPA以及BDE155与AR相互作用的残基形式图。
图5是本发明实施例提供的雄激素受体拮抗剂与激动剂区分方法的模拟前 结合DHT的受体H874与W741以及R871与H12螺旋之间的距离示意图。
图6是本发明实施例提供的雄激素受体拮抗剂与激动剂区分方法的模拟后结合DHT的受体H874与W741以及R871与H12螺旋之间的距离示意图。
图7是本发明实施例提供的雄激素受体拮抗剂与激动剂区分方法的结合HFT的受体，R871与H12螺旋之间的距离示意图。
图8是本发明实施例提供的雄激素受体拮抗剂与激动剂区分方法的结合能示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
环境中广泛存在环境类雄激素物质与抗雄激素物质，物质在环境中存在浓度微量，却能严重干扰环境中生物的内分泌功能，产生雌雄同体等现象。面对日益增多的潜在雄激素受体干扰物质，迫切需要发展一种快速筛选方法。本发明通过分子动力学模拟雄激素受体与一系列配体的结合过程来建立区分物质雄激素效应与抗雄激素效应的模型。
下面结合附图对本发明的应用原理作详细的描述。
本发明实施例提供的区分物质雄激素与抗雄激素效应的模型采用SYBYL的Surflex-Dock程序将配体对接到AR内。
如图1所示，本发明实施例提供的区分物质雄激素与抗雄激素效应的模型的构建方法包括以下步骤：
S101：对接前对蛋白质进行预处理，添加氢原子，赋予电荷，对接时使用Automatic模式搜索结合口袋，阈值(threshold)为0.5，膨胀系数(bloat)为0，均为默认值17；
S102：每个配体与受体对接时产生20个构像，以打分最高的结构作为最有 可能的生物活性构象，并以将该构像作为MD模拟的初始构象。
如图2所示，本发明实施例提供的区分物质雄激素与抗雄激素效应的方法包括以下步骤：
S201：使用SYBYL7.3中的SketchModule构建配体分子以及阳性对照的结构，对配体分子进行能量最小化，采用Powell方法进行优化，赋予Gasteiger-Huckel电荷，并使用Tripos标准分子力场，进行能量优化；
S202：人源AR受体序列来自Uniprot。现有解析出来AR结构均是激活构象，缺乏AR的抑制结构，因此通过同源模建，建立人源AR的激活构象；
S203：采用SYBYL的Surflex-Dock程序将配体对接到AR内，对接前对蛋白质进行预处理，添加氢原子，赋予电荷，每个配体与受体对接时产生20个构像，以打分最高的结构作为最有可能的生物活性构象，并将该构像作为MD模拟的初始构象；
S204：MD模拟通过Gromacs5.1.2软件包进行，采用CHARMM27力场，在复合体系周围加上TIP3P球形水分子层，复合物体系距离溶剂边缘为1.5nm，加入氯离子以中和体系中的电荷，复合体系使用最速下降法进行能量优化，进行分子动力学模拟，模拟进行30ns，其中步长2fs，每2ps保存一次。
本发明实施例提供的配体分子为TBB，TBCO，TBPH，TBBPA，BDE155，其中，TBB，TBCO，TBPH，TBBPA在之前的文献报道中具有雄激素受体拮抗性质。
本发明实施例提供的Tripos标准分子力场进行能量优化的能量收敛标准为
Figure PCTCN2018111676-appb-000002
最大迭代次数为1000次。
本发明实施例提供的人源AR的激活构象是在Swiss-model平台上使用结合了5α-DHT的AR-LBD作为模板构建人源AR激活构象的结构。
本发明实施例提供的对接时使用的Automatic模式搜索结合口袋，阈值为0.5，膨胀系数为0，均为默认值。
本发明实施例提供的复合体系使用的最速下降法进行能量优化即在40ps内 将温度从0K上升至300K对体系进行平衡，在一个大气压下保持300K平衡1ns。
下面结合附图对本发明的应用效果做详细的描述。
如图3-图8所示：
1、MMPBSA能量计算
为了定量配体与受体之间的相互作用，计算了配体与受体之间的结合能。通过mmpbsa方法和配体与受体结合后产生轨迹文件来计算结合产生的自由能 22。mmpbsa计算自由能的公式如下：
ΔG binding＝G complex-(G protein+G ligand)；
上式中的ΔG complex是蛋白质-配体复合物总自由能，Gprotein和G ligand分别是溶剂中分离的蛋白质和配体的总能量。每个G x可以通过下式来计算：
G x＝E MM-TS+G solvation；
上式中的EMM是真空中平均分子力学势能，TS表示熵的贡献，其中T和S分别表示温度和熵，G solvation是溶剂化的自由能。其中EMM包括键能，静电相互作用，范德华作用三块，如下式所示：
E MM＝E bonded+E electrostatic+E vdW；
溶剂化自由能由两部分组成：极性与非极性溶剂化自由能，如下式所示：
G solvation＝G polar+G non-polar；
其中Gnon-polar基于溶剂可接触表面积(SASA)模型计算。
2、结果
体系稳定性评估
模拟结束后，提取模拟前后的构象进行叠合比较，发现各个受体在模拟30ns后构象并未产生明显的变化。之后对所有体系的H12链以及配体分子的RMSD进行分析。可以看到所有体系均在30ns前达到平衡状态。所有体系在平衡后，H12链的的变动大小都接近0.1nm，但是BDE-155的RMSD变化接近于0.15，明显高于其他的配体，但是这并不能做出较好的区分。而由配体的RMSD可以看出，除了TBCO外，拮抗剂的配体的RMSD在15ns前无法达到平衡，或者在 平衡后RMSD高于0.1nm。而激动剂在结合后均可较快的达到平衡，且平衡后RMSD小于0.1nm。
配体受体相互作用残基
从各个配体与AR相互作用的残基来看，与这几种物质相互作用的残基的保守性较差，无法通过识别与配体相互作用残基的形式来区分拮抗剂与激动剂。
H12链氢键状态
在氟他胺以及恩扎鲁胺等抗雄激素药物出现耐药性是因为AR受体的残基序列出现突变造成的。进一步对于AR突变的研究得了L701H，W741L，H874Y，T877A以及M895T突变会造成这些氨基酸残基之间氢键网络的变化，从而导致氟他胺等拮抗类药物的抗药性。由此观察雄激素受体结合这几个配体之后，这些氨基酸残基之间相互作用的变化情况。
在模拟的前后，构象变化并不显著。在初始状态下，受体H12链的C端与H11链形成两个氢键。
在于DHT等激动剂结合后，在配体的作用下，W741的亲水基团朝向H874，两者的距离由原来的3.9A缩小到2.2A，这同时带动H874周围的残基R203，使其与H12螺旋之间的距离缩小，距离缩小到2.2A，维持了H11螺旋与H12螺旋之间的氢键。而与初始构象相比，H12螺旋上的M895与LBD区域相互接近。
但是在拮抗剂(HFT为例)结合的受体模拟30ns后，恰恰相反，W741的疏水苯环部分朝向H874，或者W741远离H874，这使得H874远离W741，H874周围的残基R871远离H12链，距离增加到5.8A。H11螺旋与H12螺旋之间的氢键消失。这说明拮抗剂结合后导致H12的结构出现更加不稳定的现象。同时与初始构象相比，H12螺旋上的M895与LBD区域相互远离。
从结合能可以看出，结合拮抗剂后，H12螺旋上的氨基酸的结合能明显小于和激动剂结合是的结合能。这说明结合拮抗剂后H12与LBD之间作用牢固程度下降，相比于激动剂结合的AR受体，H12趋于不稳定的状态。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发 明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (6)

  1. 一种区分物质雄激素与抗雄激素效应的模型，其特征在于，所述区分物质雄激素与抗雄激素效应的模型采用SYBYL的Surflex-Dock程序将配体对接到AR内。
  2. 一种如权利要求1所述区分物质雄激素与抗雄激素效应的模型的构建方法，其特征在于，所述区分物质雄激素与抗雄激素效应的模型的构建方法包括以下步骤：
    步骤一，对接前对蛋白质进行预处理，添加氢原子，赋予电荷，对接时使用Automatic模式搜索结合口袋，阈值为0.5，膨胀系数为0，均为默认值；
    步骤二，每个配体与受体对接时产生20个构像，以打分最高的结构作为最有可能的生物活性构象，并以该构像作为MD模拟的初始构象。
  3. 一种应用权利要求1所述区分物质雄激素与抗雄激素效应的模型的区分物质雄激素与抗雄激素效应的方法，其特征在于，所述区分物质雄激素与抗雄激素效应的方法包括以下步骤：
    步骤一，使用SYBYL7.3中的Sketch Module构建配体分子以及阳性对照的结构，对配体分子进行能量最小化，采用Powell方法进行优化，赋予Gasteiger-Huckel电荷，并使用Tripos标准分子力场，能量优化；
    步骤二，通过同源模建，建立人源AR的激活构象；
    步骤三，采用SYBYL的Surflex-Dock程序将配体对接到AR内，对接前对蛋白质进行预处理，添加氢原子，赋予电荷，每个配体与受体对接时产生20个构像，以打分最高的结构作为最有可能的生物活性构象，并将构像作为MD模拟的初始构象；
    步骤四，MD模拟采用CHARMM27力场，在复合体系周围加上TIP3P球形水分子层，复合物体系距离溶剂边缘为1.5nm，加入氯离子以中和体系中的电荷，复合体系使用最速下降法进行能量优化，进行分子动力学模拟，模拟进行30ns，其中步长2fs，每2ps保存一次。
  4. 如权利要求3所述的区分物质雄激素与抗雄激素效应的方法，其特征在 于，Tripos标准分子力场进行能量优化的能量收敛标准为
    Figure PCTCN2018111676-appb-100001
    最大迭代次数为1000次。
  5. 如权利要求3所述的区分物质雄激素与抗雄激素效应的方法，其特征在于，能量优化在40ps内将温度从0K上升至300K对体系进行平衡，在一个大气压下保持300K平衡1ns。
  6. 如权利要求3所述的区分物质雄激素与抗雄激素效应的方法，其特征在于，模拟结果区分方式为以下步骤：利用mmpbsa计算结合能，并观察H12螺旋上氨基酸与配体结合域之间的距离。
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