药物靶点研究

讨论
• 为什么拿宫颈癌和正常的纤维母细胞进行 比较？
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Figure- 2 Cancer-perturbed protein-protein interactions in the apoptosis network. (A) 'Gain-of-function' network, showing 140 nodes and 157 edges. (B) 'Loss-of-function' network, showing 126 nodes and 162 edges. Colors of nodes represent Gene Ontology annotations. Supplementary Tables 2 and 3 list proteins with detailed Gene Ontology annotations, with ranking according to the degree of perturbation.
药物靶点研究
学 号ຫໍສະໝຸດ Baidu09810068 研 究 生：王兴旺 导 师：张武教授 谢江副教授
介绍
1 药物靶点概念 • 药物靶点是指药物在体内的作用结合位点，包括基因位点、受体、酶 、离子通道、核酸等生物大分子。 • 现代新药研究与开发的关键首先是寻找、确定和制备药物筛选靶—分 子药靶。选择确定新颖的有效药靶是新药开发的首要任务。迄今已发 现作为治疗药物靶点的总数约500个，其中受体尤其是G-蛋白偶联的 受体（GPCR）靶点占绝大多数，另还有酶、抗菌、抗病毒、抗寄生 虫药的作用靶点。合理化药物设计（rational drug design）可以依据 生命科学研究中所揭示的包括酶、受体、离子通道、核酸等潜在的药 物作用靶位，或其内源性配体以及天然底物的化学结构特征来设计药 物分子，以发现选择性作用于靶点的新药。
Table 2: Truth table of all 16 events in the sample space of comparisons of individual interaction of protein i and protein j between HeLa and normal cells
Figure 3 Flow chart for identification of potential drug targets in the cancer-perturbed network using microarray （微阵列芯片）data.
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Figure 1 Global protein-protein interactions of apoptosis in cancerous and normal cells. (A) Apoptotic（细胞凋亡） protein-protein interaction network in HeLa cells, showing 183 nodes and 552 edges. (B) Apoptotic protein-protein interaction network in normal primary lung fibroblasts, showing 175 nodes and 547 edges. Each interaction was calculated twice and only interactions with two '1' scores after AIC evaluation was considered 'true' interactions (see Supplementary Table 1 for detailed information). All protein-protein interaction networks in this study were constructed with Osprey version 1.2.0.
讨论
• 药物靶点开发新药的原理是什么？
3 药物靶点发现方法
1、从有效单体化合物着手发现药物靶点 2、以正常组织与病理组织基因表达差异发现靶点 3、通过定量分析和比较研究在正常和疾病状态下蛋 白质表达谱的改变发现靶点 4、以蛋白质相互作用为基础发现药物靶标 5、应用RNA干扰技术特异的抑制细胞中不同基因 的表达，通过细胞的表型变化发现靶标
2 经济效益分析
（1）利用HMG CoA还原酶作为药物靶标开发了一系列他 汀类降脂药物，仅2000年，该类药物的销售额达120亿美 元，并以每年15 %~20 %的速度增长。 （2）Novartis公司利用慢性粒细胞性白血病(CML)相关蛋白 Bcr-Abl为靶标，在短时间内开发出有效治疗CML的新药 —高活性Bcr-Abl激酶抑制剂STI571 (Gleevac)。因此，生 物医药公司纷纷投入大量人力和财力，寻找治疗重要疾病 的新型药物靶点。随着生命科学的迅速发展，对于疾病发 生机制了解的逐渐深入，各种新的研究技术不断涌现，也 同时出现了许多新的靶标发现技术。
Figure 4 Dynamic（动态） protein-protein interactions in caspase formation. (A) Protein-protein interactions within the hub caspases of cancer cells during 0–8 hours after induction of apoptosis. Distinct interactions at different times are marked with bold lines.(B) During 4–30 hours after induction of apoptosis in cancer cells. (C) During 0–8 hours after induction of apoptosis in normal cells. (D) During 4–36 hours after induction of apoptosis in normal cells (see Supplementary Tables 5 and 6).
Figure 5 Graphical representation of individual protein interactions and cooperative protein interactions. Our dynamic（动态） protein interaction equation includes three individual or binary protein-protein interactions to the target protein x [t] (protein x1 [t], x2 [t], and x3 [t]) and one cooperative interaction between protein x1 [t] and x2 [t] to the target protein x [t]. a denotes the influence of a target protein at one time point to the target protein at the next time point; bi denotes individual or binary interaction of protein i with target protein x [t]; and cij denotes the cooperative interaction ability of protein i and protein j on the target protein x.
构建癌症扰动蛋白质相互作用网络发 现凋亡细胞药物靶点
Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets
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摘要
背景：癌症是由于基因异常情况引发的，比如致癌基因突变 或肿瘤抑制基因，他们改变了下游的信号转换路径和蛋白质 相互作用。通过对致癌蛋白和正常细胞蛋白的相互作用比较 可以阐明致癌作用机制。
研究结果和结论
Results: We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets （两个人类双杂交 数据集）and four online databases. Next, we applied a nonlinear stochastic model, （非线性 随机模型）maximum likelihood parameter estimation, （极大似然参数估计） and Akaike Information Criteria (AIC) to eliminate false-positive（假正值） protein-protein interactions in our initial protein interaction networks by use of microarray（微阵列芯片） data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells（宫颈癌细胞） and in normal primary Lung fibroblasts（纤维母细胞） provided insight into the mechanism of apoptosis and allowed identification of Potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison Of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion: Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets（分子靶点） for development of Anticancer drugs.
Background: Cancer is caused by genetic abnormalities, such as mutations of oncogenes or Tumor suppressor genes, which alter downstream signal transduction pathways and proteinprotein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis.