本文關(guān)鍵詞：基于信息融合和計算智能的構(gòu)象性B細胞表位預測方法研究　出處：《東北師范大學》2016年博士論文　論文類型：學位論文

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【摘要】：抗原能夠使機體產(chǎn)生免疫應答,能夠使免疫系統(tǒng)產(chǎn)生抗體物質(zhì)與之反應。在抗原與抗體發(fā)生特異性反應時,抗原分子中決定抗原特異性的特殊化學基團被稱為抗原表位�？乖砦慌c抗體結(jié)合,引起機體自身的免疫應答。在抗原表位中,可以被B細胞抗原受體或抗體特異性識別的片區(qū)被稱為B細胞表位。B細胞表位對于機體構(gòu)建獲得性免疫具有重要作用,對抗體疫苗的制備,疾病的預防與治療起到重大的指導作用。因此近些年來對于B細胞表位的預測成為了一個重要的研究方向。本文從模擬表位和抗原抗體結(jié)合特異性、模擬表位與抗原3D結(jié)構(gòu)、表位屬性信息等幾個方向,采用信息融合和計算智能的方法,對構(gòu)象性B細胞表位預測算法進行研究,主要成果如下:(1)提出了一個新的基于模擬表位和抗原抗體結(jié)合特異性的構(gòu)象性B細胞表位預測算法。算法在基于模擬表位序列信息的B細胞表位預測方法的基礎(chǔ)上,引入抗體對位的信息。在進行預測時不僅考慮抗原本身的信息,而且通過抗原和抗體結(jié)合的特異性信息,將抗體與抗原的結(jié)合特征引入構(gòu)象性B細胞表位的預測。與單純考慮模擬表位信息的幾種方法在相同的測試集下比較,本文新提出的預測方法具有更好的預測性能。(2)提出了一個新的基于模擬表位和抗原3D結(jié)構(gòu)信息的構(gòu)象性B細胞表位預測算法。在進行預測時,首先根據(jù)抗原的3D結(jié)構(gòu)信息進行計算,計算抗原的表面氨基酸殘基信息(這些表面氨基酸殘基更有可能成為表位);然后結(jié)合模擬表位的信息進行預測,通過兩者的結(jié)合,使得抗原的表面氨基酸殘基不再是氨基酸殘基的簡單疊加,而是將表面氨基酸殘基按照模擬表位劃分為具有統(tǒng)計意義的氨基酸殘基區(qū)域;最后,為了獲得更多的預測表位,對這些區(qū)域進行合理的組合,將合理組合后形成的區(qū)域中的氨基酸殘基作為方法的預測結(jié)果。新提出的方法表明:算法的靈敏度、精度、正確率都有了比較明顯的提升。(3)提出了一個新的基于表位屬性特征信息融合的構(gòu)象性B細胞表位預測算法。在進行預測時,根據(jù)表位體現(xiàn)出來的相關(guān)屬性特征強度信息,使用計算智能的相關(guān)算法對預測的氨基酸殘基進行分類,從而預測出表位信息及非表位信息。從最終的預測結(jié)果分析,該方法的預測性能比較穩(wěn)定,同時也說明表位的屬性信息對表位的預測算法具有一定的貢獻度。但是同時存在的問題是如何發(fā)掘有效的構(gòu)象性B細胞表位屬性特征信息,來提高預測的準確性。(4)基于以上的算法,實現(xiàn)相關(guān)的在線預測平臺,提供在線預測服務。根據(jù)不同的輸入條件,利用不同的預測方法,實現(xiàn)構(gòu)象性B細胞表位的預測。使用者可以根據(jù)不同的情況,利用預測平臺來獲得更準確的預測結(jié)果。預測平臺實現(xiàn)了構(gòu)象性B細胞表位預測理論研究的實際應用。
[Abstract]:Antigens can make the body immune response, can make the immune system to produce antibody substances to react with it, when the antigen and antibody specific reaction. The specific chemical groups in antigen molecules that determine antigen specificity are called antigen epitopes. The binding of antigen epitopes to antibodies causes the body's own immune response. B cell epitopes are known as B cell epitopes and B cell epitopes, which can be specifically recognized by B cell antigen receptors or antibodies, play an important role in the construction of acquired immune system, and also play an important role in the preparation of antibody vaccines. The prevention and treatment of diseases play an important role in guiding. So the prediction of B cell epitopes has become an important research direction in recent years. In this paper, mimic epitopes and antigen-antibody binding specificity. In order to simulate the 3D structure of epitopes and antigens and the information of epitope attributes, the prediction algorithm of conformational B-cell epitopes is studied by using information fusion and computational intelligence. The main results are as follows:. A new conformational B cell epitope prediction algorithm based on analogue epitope and antigen-antibody binding specificity is proposed. The algorithm is based on the analogue epitope sequence information. In the prediction, not only the information of the antigen itself is considered, but also the specific information of the binding of the antigen and the antibody is taken into account. The binding characteristics of antibodies and antigens were introduced into the prediction of conformational B cell epitopes, which were compared with several methods with only simulated epitope information under the same test set. In this paper, a new prediction method with better prediction performance is proposed. A new conformational B cell epitope prediction algorithm based on simulated epitopes and antigenic 3D structure information is proposed. Firstly, the surface amino acid residues of antigens are calculated according to 3D structure information of antigens (these amino acid residues are more likely to become epitopes; Then combined with the information of analog epitopes to predict, through the combination of the two, the surface amino acid residues of antigen is no longer a simple superposition of amino acid residues. The surface amino acid residues were divided into statistically significant amino acid residues according to the simulated epitopes. Finally, in order to obtain more prediction epitopes, these regions are reasonably combined. The amino acid residues in the region formed by reasonable combination are taken as the prediction results of the method. The new method shows the sensitivity and accuracy of the algorithm. A new conformational B-cell epitope prediction algorithm based on epitope attribute feature information fusion is proposed. The predicted amino acid residues are classified by using the correlation algorithm of computational intelligence according to the information of the characteristic strength of the related attributes reflected by the epitopes. Thus, the epitope information and non-epitope information are predicted. The prediction performance of this method is stable from the analysis of the final prediction results. It also shows that the attribute information of epitope has a certain contribution to the epitope prediction algorithm, but the problem is how to explore the effective conformational B-cell epitope attribute information. To improve the accuracy of prediction. (4) based on the above algorithm, the related online prediction platform is implemented to provide online prediction services. According to different input conditions, different prediction methods are used. To realize the prediction of conformational B cell epitopes. The prediction platform is used to obtain more accurate prediction results, and the practical application of the conformational B cell epitope prediction theory is realized.
【學位授予單位】：東北師范大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：R392

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