阿茲海默症，又名老人失智症，為現今最嚴重的疾病其中之一。到目前為止，仍無法明確的得知發病原因。儘管如此，在罹患阿茲海默症患者的大腦中，卻有兩個顯著的病理特徵，為澱粉樣蛋白(β-amyloid, Aβ)的聚集以及神經纖維糾結(Neurofibrillary tangles, 簡稱NFTs)，科學家們指出，若能夠減輕或抑制這兩個病癥的生成，也許能夠減輕阿茲海默症的病情，甚至於治癒阿茲海默症。因此在本研究當中，我們的目標是利用幾個生物化學的研究工具(分子嵌合、分子動力學模擬等等)來找出具有潛力成為小分子藥物的候選化合物，來減低NTFs的生成。根據研究指出，NFTs的生成主要是因為Tau蛋白的過度磷酸化。目前已知GSK3α與GSK3β激酶磷酸化Tau蛋白，後者對於Tau 蛋白的過度磷酸化問題已廣被人研究，而GSK3α對Tau蛋白過度磷酸化的課題，在近期才逐漸被重視，一些研究顯示GSK3α的影響比GSK3β來的明顯。在本研究當中，我們利用酵素激酶實驗與分子嵌合軟體，來找尋新的且有潛力的小分子GSK3抑制劑，由於GSK3α 尚未廣泛被研究，因此我們將目標先放至GSK3β 上。 在GSK3β的酵素激酶實驗上，我們測試了一些有潛力的化合物，這些化合物是由畢業的學姊於其論文中所推薦。在那些化合物當中，我們針對8個化合物進行了IC50曲線的測試，其中有四個化合物有明確的測出IC50值，其餘四個則是在高濃度的抑制率仍低於50%，因此無法得知IC50值。在已知的IC50值的化合物當中，以化合物編號VBNTNU063測出來的值最佳，其值為61nM；其餘三個標號為VBNTNU062, VBNTNU074和 VBNTNU075測出來的IC50值依序為64 uM, 65 uM, 和4.0x10-1 uM.。這些都被認為是好的藥物設計的先導化合物，其中T6已經推薦給細胞學家與動物學家來進行更進一步的測試。除了GSK3β的潛力抑制劑之外，我們對於找尋GSK3α的潛力抑制劑也有濃厚的興趣。由於GSK3α至今仍未有結晶構型，因此我們藉由同源模擬(Homology modeling) 與分子動態模擬(Molecular dynamics)來產生GSK3α之構型，以進行分子嵌合虛擬篩選計算，大量篩選(virtual screening)的計算程序是根據先前對GSk3β激酶研究的結果：Chemscore 的評分函數加上兩條氫鍵的設定。目前已經篩選了約150萬個小分子化合物，並推薦有潛力的GSK3α 抑制劑，於之後進行酵素激酶的實驗。

Alzheimer’s disease (AD) is one of the most serious diseases in modern times. So far, exact cause of this disease is unknown. Nevertheless, two cellular symptoms were observed in the patients’ brain cells. One is the beta-amyloid aggregation and the other is neurofibrillary tangles, abbreviated as NFTs. It was proposed that alleviating these symptoms will alleviate AD or even cure. In the present study, we aim to use several bio-molecular tools to identify potential, small molecule drug candidates, which may alleviate NFTs. It is known that the NFTs were caused by over-phosphorylation of the tau protein. GSK3α and GSK3β kinases are two known upstream kinases which phosphorylate tau protein. The latter has been important target protein against AD over the years and the significance of GSK3α kinase is gradually recognized recently. In the present study, we aimed to use experimental kinase assay and docking computation to identify new, potential, small molecule inhibitors of GSK3 kinases. We first focused on GSK3β kinase. For GSK3β kinase, we measured inhibition concentration at 50% (IC50) for 8 compounds which were selected from top-scored compounds obtained in docking computation carried out by my lab-mate. The measurement gave IC50 of 61 nM for one compound, coded VBNTNU063, and was recommended for cell and animal tests. The compounds, VBNTNU062, VBNTNU074, and VBNTNU075, have their IC50 values in the range of uM, serving as good lead compounds for designing new derivative compounds in the future along with VBNTNU063. The remaining 4 compounds are of low inhibition. In addition to GSK3β kinase, identifying new small molecule inhibitors against GSK3α kinase, which has no crystal structure available, was of interest. Homology modeling plus molecular dynamics simulations with simulated annealing and replicate exchange methods were carried out to generate GSK3α structures. With the structure in hand, virtual screening over 1.5 million compounds were carried out with the Chemscore scoring function plus two hydrogen bonds constraints. The protocol was identified by a benchmark study of GSK3 kinase which have crystal structure available and abundant affinity data. The top-scored compounds obtained from docking of GSK3α kinase are useful in identifying new GSK3α inhibitors experimentally in the future.

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