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手性对映体分子的物理化学性质基本相同,其旋光性差异直接影响生物体内生理和药理的活性。本文以Builder和Discover-3模块为基础,在InsightII平台上模建并优化,采用Dock刚性、柔性对接及GOLD柔性对接的方法,模拟了β-环糊精识别手性对映体的能力。通过利用DMS程序计算环糊精分子溶剂的可接触表面,用SPHGEN程序计算,产生球集,形成环糊精分子活性位点的负像;在环糊精分子上打格点,计算格点能;采用格点的打分函数打分,分子对接,保存分数最高的对接结果等步骤;表明手性识别的预测率从高到低依次是GOLD对接,Dock柔性对接,Dock刚性生对接。由于刚性对接过程对映体分子保持刚性,导致手性识别效果最差,故分子柔性的作用不可忽视;GOLD对接在模拟环糊精对对映体手性识别方面要优于Dock对接模拟。因此,用GOLD对接建立了β-环糊精对对映体的手性识别模型,结合能越大,相互作用越强,形成的包合物越稳定。分析模型的结合能组分,以分子识别过程中的氢键作用为模型,则其手性识别预测结果正确率为45.5%;如以分子间氢键作用和范德华作用为预测模型,则其预测正确率为81.8%。表明手性识别的驱动力不仅有氢键的作用,也存在范德华力的作用。
Chiral enantiomers of the physical and chemical properties of the same, the optical difference directly affects the biological activity of the body physiologically and pharmacologically. Based on the Builder and Discover-3 modules, this paper builds and optimizes on the Insight II platform and simulates the ability of β-cyclodextrin to recognize chiral enantiomers by Dock rigid, flexible docking and GOLD flexible docking. By using the DMS program to calculate the accessible surface of the cyclodextrin molecule solvent and calculating with the SPHGEN program, a ball set is generated to form a negative image of the active site of the cyclodextrin molecule. The lattice point on the cyclodextrin molecule is calculated to calculate the lattice energy ; Using grid points scoring function scoring, molecular docking, the preservation of the highest score docking results and other steps; indicating that the chiral recognition rate of high-to-low GOLD Docking Docking Dock flexible docking Dock rigid docking. Due to the rigidity of the enantiomers in the rigid docking process, which results in the worst chiral recognition effect, the role of molecular flexibility can not be ignored. GOLD docking is better than dock docking in simulating cyclodextrin enantioselective chiral recognition. Therefore, the chiral recognition model of β-cyclodextrin enantiomers was established by GOLD docking. The larger the binding energy is, the stronger the interaction is and the more stable the inclusion complex is formed. The binding energy components of the model were analyzed. The accuracy of the chiral recognition prediction was 45.5% based on the hydrogen bonding in the molecular recognition process. For intermolecular hydrogen bonding and van der Waals interactions, the predictive model The correct rate was 81.8%. It indicates that the driving force of chiral recognition is not only the hydrogen bond but also van der Waals forces.