在本研究中，我們運用計算生物學方法，將線上資料庫裡的基因序列「轉譯」為抗體對抗A型流感病毒的功能性試驗與動物實驗之數據。
我們以流感病毒作為目標，試著去預測抗體中和病毒的能力，並希望能藉此加速抗體藥物的開發。我們使用同源建模的方式，從基因序列預測病毒與抗體的蛋白質結構，並使用ZDOCK來將預測得知的蛋白質結構進行對接。最後將對接完的結果進一步優化，預測出兩者之間的結合能量。
我們將此結果與其他傑出期刊的論文做比較，得到一些不錯的成果，足以證明我們的方法是有用的且可行的新方法來輔助抗體藥物的開發。

In this research, we use our computational approach to "translate" genomic sequences from online databases into functional assay and in-vivo experiments of antibodies against influenza virus.
We take influenza virus as our target, trying to predict the efficacy of neutralization antibodies, hoping to accelerate the process of antibody drug discovery. By using homology modeling, we can predict the 3D structure of antibody and virus protein from genomic sequences. After protein structure prediction for both antibody and virus, we use ZDOCK to perform docking between these two proteins. Finally, we calculate the binding energy score with further refinement, and correlate our computational outcome with experimental results from some outstanding journals.
The results we get are pretty well and suffice to show that our approach is useful. All the work we have done is providing an innovative way for researchers to develop antibody drug for clinic.

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