桔梗悬浮细胞对莪二酮的生物转化研究

目的:利用植物悬浮细胞体系对莪二酮进行结构改造研究.方法:采用生物转化技术和天然药物化学手段,分离转化产物单体,并利用波谱学手段对转化产物进行结构鉴定,并利用MTT法对转化产物的抗肿瘤活性进行了评价.结果:分离并鉴定了5个转化产物,分别为1β,10α-环氧基莪二酮(2),3α-羟基-莪二酮(3),3β-羟基-莪二酮(4),1α,10β-环氧基-11-羟基莪二酮(5)和2β-羟基-莪二酮(6).结论:桔梗悬浮细胞对于莪二酮具有良好的转化能力,可以利用其作为植物反应器对莪二酮进行结构改造,以获得水溶性更好或活性更佳的衍生物.     

伞形酮产生菌的构建

【背景】伞形酮为香豆素类化合物,具有较广泛的药用价值,也是重要的工业制品原料。传统的植物提取成本较高,应开发更有效的获取技术。【目的】利用微生物为宿主异源合成伞形酮。【方法】对不同植物来源香豆素类化合物合成基因进行整合;以酿酒酵母为宿主,通过对宿主酪氨酸代谢通路的改造构建表达宿主,进一步将表达基因转化到改造后的宿主中。【结果】获得产伞形酮的酵母菌株,产量为(67.39±4.87)μg/mL。【结论】通过整合生物合成基因可获得香豆素类天然产物表达菌株。     

红根草内酯的化学结构

前文曾报道从红根草(Salvia prionitis Hance)中分得新二萜醌类化合物去氢丹参新酮,红根草对醌,3-羰基红根草对醌及新天然产物红根草邻醌等成分。在进一步研究红根草的活性成分时,又分得一新的二萜醌类化合物——红根草内酯(Ⅰ)。红根草内酯为浅黄色棱形结晶,熔点205—206℃,分子式 C_(15)H_(14)O_3(~1H RMS M~+242.0938)。红外光谱示 v-内酯(1750cm~(-1)),芳香环(1505,1485 cm~(-1))以及酚羟基     

洪湖生态环境的化学结构

本文探讨了化学元素在洪湖水、植物、沉积物中的分布,在此基础上分析了水生植物对元素的吸收特征及元素在生态系统中的贮存和迁移规律。指出湖水中Ｃａ￣（２＋）和是主要的阳离子和阴离子,湖水酸容纳能力在１．２×１０￣（－３）－２．０×１０￣（－３）ｍｏｌ／ｌＨ￣＋之间,水化学的稳定性受到碳酸盐地球化学平衡过程的控制;湖中水生植物具有富集Ｃ、Ｎ、Ｋ、Ｃａ和Ｃｄ的性质,并导致这些元素在沉积物表层集累;系统中Ｃ多存在于植物体中,Ｎ、Ｐ、Ｋ、Ｃａ和Ｍｇ多存在于沉积物中,沉积物分室是营养元素主要的贮存库。     

魔芋甘露聚糖化学结构的研究

研究了陕西花魔芋块茎中分离所得的魔芋甘露聚糖的化学结构与分子组成。经葡聚糖凝胶Ｇ－７５柱层析为一组均一性多糖,气相色谱检测由甘露糖,葡萄糖组成,其克分子比Ｍａｎ;Ｇｌｕ＝１．７８：１,平均分子量为１１×１０＾－５。     

拐芹倍半萜没药烷吉酮的结构修饰及其对H~+/K~+-ATP酶的抑制作用

三峡区域药用植物拐芹的根中富含倍半萜类抗溃疡活性成分没药烷吉酮,为可开发和利用的中草药资源。本文对该化合物进行了提取分离、结构修饰和初步的构效关系研究,从拐芹根茎中提取并分离了没药烷吉酮,通过选择性还原、缩合和加成反应制备了四个没药烷吉酮氨基甲酰腙衍生物。用核磁共振波谱、质谱、红外和元素分析等方法确证了其结构,并测试了其体外对H~+/K~+-ATP酶的抑制活性和细胞毒活性。没药烷吉酮还原衍生物2及4-氯苯基取代的氨基甲酰腙衍生物4d较阳性对照药物奥美拉唑具有更好的体外抗溃疡活性(IC50<24μmol/L)。本文探明了没药烷吉酮衍生物的结构对体外H~+/K~+-ATP酶抑制活性的影响,为消化性溃疡的治疗提供了新型倍半萜类候选药物。     

拐芹倍半萜没药烷吉酮的结构修饰及其对H~+/K~+-ATP酶的抑制作用

三峡区域药用植物拐芹的根中富含倍半萜类抗溃疡活性成分没药烷吉酮,为可开发和利用的中草药资源。本文对该化合物进行了提取分离、结构修饰和初步的构效关系研究,从拐芹根茎中提取并分离了没药烷吉酮,通过选择性还原、缩合和加成反应制备了四个没药烷吉酮氨基甲酰腙衍生物。用核磁共振波谱、质谱、红外和元素分析等方法确证了其结构,并测试了其体外对H~+/K~+-ATP酶的抑制活性和细胞毒活性。没药烷吉酮还原衍生物2及4-氯苯基取代的氨基甲酰腙衍生物4d较阳性对照药物奥美拉唑具有更好的体外抗溃疡活性(IC5024μmol/L)。本文探明了没药烷吉酮衍生物的结构对体外H~+/K~+-ATP酶抑制活性的影响,为消化性溃疡的治疗提供了新型倍半萜类候选药物。     

徐长卿中一种新葡聚糖化学结构的研究

从常用中药徐长卿中获得一分子量为 1.5× 10 4 的多糖CPB 1。比旋光度 [α]D= 15 1.4°(0 .96 ,H2 O)。单糖组成分析表明仅含葡萄糖。甲基化分析、部分酸水解、乙酰解、IR及NMR数据表明CPB 1的主链由α D 1,4连接的葡萄糖残基组成 ,其侧链由 1,4和 1,6连接的葡萄糖残基构成。每五个葡萄糖残基组成的重复单元中含有一个分枝 ,位于主链葡萄糖残基的O 6位上     

细菌荚膜多糖的化学结构

荚膜是一些细菌所具有的表层结构,与多种疾病有着密切联系。细菌荚膜多糖不仅结构复杂,而且在免疫活性方面发挥着重要的作用。同一种细菌根据其荚膜多糖的抗原性不同可分为不同的血清型,不同血清型细菌荚膜多糖的化学结构也存在差异。以荚膜多糖为基础的疫苗正在积极研究开发当中,对不同致病细菌荚膜多糖具体化学结构的掌握是疫苗得到许可的必备条件之一。本文对致病细菌荚膜多糖的化学结构进行了归纳和总结,以期为荚膜多糖的化学结构研究和疫苗开发提供参考。     

The chemical structure of pemptoporphyrin

1. Pemptoporphyrin dimethyl ester was further purified on an alumina column and then crystallized from chloroform and methanol with melting point 215-218 degrees . The absorption spectra in the visible and ultraviolet with molar extinction coefficients are presented. 2. The nuclear-magnetic-resonance spectrum of pemptoporphyrin dimethyl ester in deuterochloroform solution is reported, together with those of several other porphyrins. 3. One vinyl and one hydryl group were found at positions 2 and 4 (or 4 and 2) in the molecule. The structure of pemptoporphyrin is 2(4)-hydryl-1,3,5,8-tetramethyl-4(2)-vinyldeuteroporphyrin IX. 4. Reduction of pemptoporphyrin dimethyl ester with resorcinol gave deuteroporphyrin IX dimethyl ester.     

Statistical decoding of potent pools based on chemical structure

Pooling experiments are used as a cost-effective approach for screening chemical compounds as part of the drug discovery process in pharmaceutical companies. When a biologically potent pool is found, the goal is to decode the pool, i.e., to determine which of the individual compounds are potent. We propose augmenting the data on pooled testing with information on the chemical structure of compounds in order to complete the decoding process. This proposal is based on the well-known relationship between biological potency of a compound and its chemical structure. Application to real data from a drug discovery process at GlaxoSmithKline reveals a 100% increase in hit rate, namely, the number of potent compounds identified divided by the number of tests required.     

Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data

MOTIVATION: Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability. RESULTS: We describe a novel method for predicting protein-chemical interaction using SVM. We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions. AVAILABILITY: Available on request from the authors.     

Quantum chemical studies of protein structure

Quantum chemical methods now permit the prediction of many spectroscopic observables in proteins and related model systems, in addition to electrostatic properties, which are found to be in excellent accord with those determined from experiment. I discuss the developments over the past decade in these areas, including predictions of nuclear magnetic resonance chemical shifts, chemical shielding tensors, scalar couplings and hyperfine (contact) shifts, the isomer shifts and quadrupole splittings in M?ssbauer spectroscopy, molecular energies and conformations, as well as a range of electrostatic properties, such as charge densities, the curvatures, Laplacians and Hessians of the charge density, electrostatic potentials, electric field gradients and electrostatic field effects. The availability of structure/spectroscopic correlations from quantum chemistry provides a basis for using numerous spectroscopic observables in determining aspects of protein structure, in determining electrostatic properties which are not readily accessible from experiment, as well as giving additional confidence in the use of these techniques to investigate questions about chemical bonding and chemical reactions.