研究生: |
李旻晉 Min-Chin Lee |
---|---|
論文名稱: |
以pleconaril之抑制機制為基礎透過虛擬高速藥物篩選尋找抑制腸病毒71 型之先導藥物 Finding Leads for Anti -Enterovirus 71 Agent s by Virtual High - Throughput Screening Based on Pleconaril |
指導教授: |
林志侯
Thy-Hou Lin |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
生命科學暨醫學院 - 分子醫學研究所 Institute of Molecular Medicine |
論文出版年: | 2004 |
畢業學年度: | 92 |
語文別: | 中文 |
論文頁數: | 72 |
中文關鍵詞: | 虛擬高速藥物篩選 、腸病毒 、分子對接 、電腦輔助藥物設計 、同源模擬法 、先導藥物 |
外文關鍵詞: | pleconaril, enterovirus 71, virtual screening, computer-assisted drug design, dock, x-score, homology modeling, capsid vp1 |
相關次數: | 點閱:3 下載:0 |
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腸病毒曾爆發過四次大流行,分別在保加利亞、匈牙利、馬來
西亞和台灣,1998年在台灣發生的腸病毒感染共有129,106名通報
病例,造成78位兒童死亡,405位兒童得到嚴重的合併症,至今還
有病例昏迷或癱瘓在醫院,無法出院的,其中分離出的病毒以腸病
毒71型為主,但至今仍然沒有針對腸病毒71型開發出有效的治療方
式。
隨著計算機科學的進步,以電腦模擬配合組合化學庫以及高通
量篩選來加速藥物的開發已成為趨勢,在本實驗中我們用虛擬藥物
篩選的方式來找出腸病毒71型的先導藥物。
本實驗先用insightII軟體中的homology模組以同源模擬法建
立腸病毒71型VP1蛋白的三度空間立體結構,再用DOCK分子對接
軟體和X-Score評分程式配合,以pleconaril的抑制機制為基礎,分
為兩部分訓練出高速篩選和精確篩選。在精確篩選中又分為相關性
篩選和結合位置篩選,結合以上幾種篩選方式搜尋Maybridge資料
庫,以尋找具有潛力的腸病毒71型先導藥物。實驗結果從
Maybridge資料庫中的五萬九千一百二十八個化合物中篩選出有潛
力的化合物,再結合其他方式判斷,得到八十三個有可能成為腸病
毒71行先導藥物的化合物。接下來應進一步進行活性測試,確認化
合物的實際生物活性。在確認生物活性後,可依不同篩選方式得到
之化合物中含有有活性的化合物比例來進一步確認這些先導藥物的
作用機制。
Enterovirus had caused 4 epidemic outbreaks, these
outbreaks happened in Bulgaria, Hungary, Malaysia and
Taiwan. In the 1998 epidemic outbreak of enterovirus in
Taiwan, 129,106 cases repor ted, 78 patient s died, 405
patient s with severe disease. Most cases in this outbreak are
caused by enterovirus 71. To da te, there is no effective
antiviral agent for t reatment of enterovirus 71. Here we t ried
to find leads for anti - enterovirus 71 agent s by virtual
screening.
First, a virtual 3D s t ructure of capsid protein VP1 of
enterovirus 71 is built by insightII homology module. Then
DOCK and X-Score are combined to build two screening
mo del,high - speed screening model and precise screening
mo del, based on the mechanism of pleconaril. The precise
screening model is fur ther seperated in two ways, corelation
screening and binding site screening. We searched Maybridge
database with these mo dels. After screening the 59,128
compunds in Maybridge da tabase, we picked out 83 potential
compounds. These compounds will be activity tes ted. From
the ra tio of active compounds in each screening model, we
may conjecture the mechanism of these leads.
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