本文選題：T細胞　切入點：表位預測　出處：《東北師范大學》2013年碩士論文

【摘要】：T淋巴細胞(T Lymphocyte)簡稱T細胞。在免疫應(yīng)答過程中，T細胞不能像抗體一樣直接識別完整的天然抗原，而是需要借助T細胞表面的T細胞抗原受體(T-cell Receptor,TCR)識別抗原蛋白中一段具有特殊功能的肽段，這段肽段稱為T細胞表位(Epitope)，或抗原決定簇(Antigenic Determinant)。主要組織相容性復合體(Major HistocompabilityComplex, MHC)根據(jù)其編碼及其參與抗原提呈途徑的不同可分為MHC I類分子以及MHC II類分子。分別參與內(nèi)源性以及外源性抗原提呈途徑。定位與MHC分子相結(jié)合的T細胞表位不僅有助于了解自身免疫性疾病、過敏反應(yīng)、傳染性疾病以及腫瘤等疾病的免疫應(yīng)答機理，而且對于計算機輔助設(shè)計人工疫苗以及免疫干預治療等都有非常重要的意義。目前T細胞表位的定位方法都需要消耗大量人力物力資源，，并且對設(shè)備的要求也很高。 隨著已知表位數(shù)據(jù)的大量增加，計算機作為一種有效的輔助實驗手段漸漸被研究者應(yīng)用于生物實驗當中。通過計算機方法預測得到候選表位可以通過后續(xù)的生物實驗加以驗證。恰當?shù)厥褂眠@種計算機與生物實驗相結(jié)合的方式既可以保證結(jié)果的準確性，又可以節(jié)約成本、提高工作效率，從而滿足現(xiàn)代社會的生產(chǎn)需求。 目前比較成熟的MHC分子親和肽預測方法主要有結(jié)合基序法(Binding Motif)、定量矩陣法(Quantitative Binding Matrix)以及機器學習法(Machine Learning)。本文在機器學習方法的基礎(chǔ)上提出一種聚類后PCA方法用以約減參與MHC分子親和肽預測的氨基酸理化性質(zhì)，此方法針對不同MHC分子篩選不同的氨基酸理化性質(zhì)，使其更具有針對性，以提高機器學習算法的性能。 試驗證實，本文的算法在準確率以及敏感性方面都有不同程度的提高，因此本文在T細胞表位預測理論研究以及實際應(yīng)用方面都有推動作用。
[Abstract]:T-lymphocyte T Lymphocyte.in the course of immune response, T cells cannot recognize intact natural antigens as directly as antibodies. Instead, we need to use T-cell receptor (T-cell receptor) on the surface of T cells to recognize a peptide that has a special function in an antigen protein. The peptide segment is called T cell epitope Epitopeus, or antigenic determinant. Major histocompatibility complex Major Histocompactability Complexes (MHCs) can be divided into MHC class I and MHC II according to their coding and the way in which they participate in antigen presentation. Localization of T cell epitopes combined with MHC molecules is not only helpful for understanding autoimmune diseases. The immune response mechanism of allergic reactions, infectious diseases and tumours, It is very important for computer-aided design of artificial vaccine and immune intervention therapy. At present, the method of T cell epitope localization needs a lot of manpower and material resources, and the requirement of equipment is also very high. With the large increase in known epitope data, As an effective auxiliary experimental method, computer has been gradually applied in biological experiments. The candidate epitopes predicted by computer method can be verified by subsequent biological experiments. The combination of computer and biological experiments can guarantee the accuracy of the results. It can also save cost and improve work efficiency to meet the production needs of modern society. At present, mature MHC molecular affinity peptide prediction methods mainly include binding motif method, quantitative Binding matrix method and machine learning method. In this paper, a post-cluster PCA method is proposed based on machine learning method. The physicochemical properties of amino acids predicted by MHC molecular affinity peptide were reduced. In order to improve the performance of machine learning algorithm, different amino acid physicochemical properties were screened by this method for different MHC molecules. The experimental results show that the proposed algorithm can improve the accuracy and sensitivity of T cell epitopes in different degrees, so this paper can promote the theoretical research and practical application of T cell epitope prediction.
【學位授予單位】：東北師范大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：R392;TP311.13

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