基于噬菌體組合肽庫篩選的B細胞表位預(yù)測研究
發(fā)布時間：2018-07-17 14:58
【摘要】：在體液免疫過程中,B細胞表面受體(BCR)會識別外源抗原蛋白,并產(chǎn)生與抗原蛋白特異性結(jié)合的抗體,同時一部分B細胞會被激活分化成為記憶B細胞,并在下次病原體侵入體內(nèi)時產(chǎn)生更加迅速的免疫應(yīng)答�？乖砻姹籅細胞表面受體識別并與抗體特異性結(jié)合的區(qū)域稱為B細胞表位。 定位抗原表面B細胞表位對于設(shè)計人工疫苗、免疫干預(yù)治療以及高通量的抗體制備而言都具有重要意義。目前,定位B細胞表位最可靠的方法是通過抗原-抗體復(fù)合體晶體衍射實驗以及核磁共振的方法獲得復(fù)合體的空間結(jié)構(gòu)。然而這兩種實驗的方法都需要很高的成本以及大量的人力并且對于設(shè)備的要求也很高。隨著一些輔助的實驗手段的發(fā)展以及已知表位數(shù)據(jù)的增加,人們開始考慮使用計算機進行表位預(yù)測。通過預(yù)測的方法獲得的候選表位可以通過后續(xù)的生物實驗進行驗證。使用這種實驗和計算機相結(jié)合的方法既可以保證結(jié)果的準確又可以節(jié)約成本,提高工作效率。 基于噬菌體組合肽庫篩選的B細胞表位預(yù)測是實驗方法和計算方法相結(jié)合的一種B細胞表位預(yù)測方法。方法首先通過噬菌體組合肽庫篩選實驗獲取與抗體親和度較高的模擬表位序列,然后利用這些模擬表位序列在抗原表面搜索與之相匹配的氨基酸預(yù)測候選表位。近年來,隨著噬菌體組合肽庫篩選獲得的模擬表位序列數(shù)據(jù)和抗原-抗體復(fù)合體三維結(jié)構(gòu)數(shù)據(jù)的不斷增長,許多基于噬菌體組合肽庫篩選的B細胞表位預(yù)測方法被提出,并在幾個測試例上運行都得到了較好的預(yù)測結(jié)果。然而到目前為止,在基于噬菌體組合肽庫篩選的B細胞表位預(yù)測方面還沒有一個通用的標準測試集,同時對算法間性能的分析比較也沒有一個完全的評價體系。 本文的研究工作主要包括構(gòu)建基于噬菌體組合肽庫篩選的B細胞表位預(yù)測標準測試集、建立算法間性能的評價體系、提出更加敏感的基于抗原結(jié)構(gòu)信息和噬菌體組合肽庫篩選的B細胞表位預(yù)測新方法。 首先,本文在對現(xiàn)有的基于噬菌體組合肽庫篩選的B細胞表位預(yù)測方法研究基礎(chǔ)上,整合了MimoDB、PDB、CED和IEDB 4個數(shù)據(jù)庫中的相關(guān)信息,構(gòu)建了一個通用的標準測試集。使用標準測試集及其代表測試集對Mapitope、PepSurf、Pepitope、Pep-3D-Search和EpiSearch 5個公開發(fā)表的基于噬菌體組合肽庫篩選的B細胞表位預(yù)測方法進行了測試,這5個算法或提供源碼或提供免費的網(wǎng)絡(luò)服務(wù)。文章通過標準測試集及其代表數(shù)據(jù)集,并使用敏感性、特異性、準確率和馬氏相關(guān)系數(shù)4個評價參數(shù)為基于噬菌體組合肽庫篩選的B細胞表位預(yù)測方法建立了一個全面的評價體系,并對5個算法的性能進行了綜合的評價分析。 在綜合評價分析基礎(chǔ)上,本文提出了一種更加敏感的基于抗原結(jié)構(gòu)信息和噬菌體組合肽庫篩選的B細胞表位預(yù)測方法。算法首先根據(jù)結(jié)構(gòu)特征并使用支持向量機對抗原氨基酸進行分類,實現(xiàn)對抗原的預(yù)處理;然后在現(xiàn)有表位預(yù)測算法的基礎(chǔ)上引入劃分的思想,通過將抗原表面氨基酸劃分成若干交疊的patch區(qū)域進行表位預(yù)測。在為每一個patch構(gòu)建無向圖的過程中,算法首次嘗試使用可變的距離閾值來定義無向圖中頂點的連接。此外,本文第一次采用完備的搜索方法保證了搜索的路徑最優(yōu)。 最后,通過與其它5個算法的測試結(jié)果進行比較驗證,本文提出的算法的敏感度有很明顯的提高。本研究不僅對B細胞表位預(yù)測方法的理論研究具有重要意義,同時也將推動其向?qū)嵱梅较虬l(fā)展。
[Abstract]:During the humoral immune process, the B cell surface receptor (BCR) recognizes the exogenous antigen protein and produces the antibody specific binding with the antigen protein. At the same time, a part of the B cells will be activated and differentiated into memory B cells and produce a more rapid immune response when the next pathogen invades the body. The antigen surface is identified by the surface receptor of the B cell and The region specifically binding to antibodies is called B cell epitope.
B cell epitopes on the surface of the antigen surface are of great significance for the design of artificial vaccines, immune intervention and high throughput antibody preparation. At present, the most reliable method for locating the epitopes of B cells is to obtain the spatial structure of the complex by means of the diffraction experiment of antigen antibody complex crystal and the square method of nuclear magnetic resonance. However, these two kinds of methods have been used to obtain the space structure of the complex. Experimental methods require a high cost, a large number of manpower and a high demand for equipment. With the development of some auxiliary experimental methods and the increase of known table data, people begin to consider using computers for epitope prediction. The candidate tables obtained by the prediction method can be achieved through subsequent biological reality. Verify that the combination of experiment and computer can ensure the accuracy of the result, save cost and improve work efficiency.
The prediction of B cell epitopes based on phage combination peptide library is a prediction method of B cell epitopes combined with the combination of experimental and computational methods. First, a simulated epitope sequence with high affinity to antibodies is obtained by the screening experiment of phage combination peptide library, and then the epitope sequences are used to search the surface of the antigens on the surface of the antigen. In recent years, a number of B cell epitope prediction methods based on the screening of phage combination peptide library have been proposed, which have been better used in several test cases. But so far, there is not a universal standard test set in the prediction of B cell epitopes based on the phage combination peptide library, and there is no complete evaluation system for the analysis and comparison of the performance between the algorithms.
The research work of this paper mainly includes the construction of the B cell epitope prediction standard test set based on the screening of phage combination peptide library, the establishment of the evaluation system for the performance of the algorithm, and a more sensitive new method for predicting B cell epitopes based on the antigen structure information and the screening of the phage combination peptide library.
First, on the basis of the existing B cell epitope prediction method based on phage combination peptide library, this paper integrates the related information in 4 databases of MimoDB, PDB, CED and IEDB, and constructs a general standard test set. The standard test set and its representative test set are used for Mapitope, PepSurf, Pepitope, Pep-3D-Search, and EpiS. Earch 5 published B cell epitope prediction methods based on phage combination peptide library screening were tested, these 5 algorithms or source code or free network services. The article passes standard test set and its representative data set, and uses 4 evaluation parameters based on sensitivity, specificity, accuracy and Markov correlation coefficient. A comprehensive evaluation system for predicting the B cell epitopes screened by the combined peptide library was established, and the performance of the 5 algorithms was comprehensively evaluated and analyzed.
On the basis of comprehensive evaluation analysis, this paper proposes a more sensitive B cell epitope prediction method based on antigen structure information and phage combination peptide library screening. Firstly, the algorithm is used to classify antigen amino acids based on structural features and use support vector machines to achieve antigen preprocessing; then, the existing epitope prediction algorithm is used. Based on the idea of division, the epitope is predicted by dividing the antigen surface amino acid into a number of overlapping patch regions. In the process of constructing an undirected graph for each patch, the algorithm first attempts to use a variable distance threshold to define the connection of the vertex in the undirected graph. In addition, this paper first uses a complete search method. The path of search is proved to be optimal.
Finally, by comparing with the test results of the other 5 algorithms, the sensitivity of the proposed algorithm is obviously improved. This study is not only of great significance to the theoretical research of the B cell epitope prediction method, but also will promote its practical direction.
【學位授予單位】：東北師范大學
【學位級別】：博士
【學位授予年份】：2011
【分類號】：R392
本文編號：2130013
[Abstract]:During the humoral immune process, the B cell surface receptor (BCR) recognizes the exogenous antigen protein and produces the antibody specific binding with the antigen protein. At the same time, a part of the B cells will be activated and differentiated into memory B cells and produce a more rapid immune response when the next pathogen invades the body. The antigen surface is identified by the surface receptor of the B cell and The region specifically binding to antibodies is called B cell epitope.
B cell epitopes on the surface of the antigen surface are of great significance for the design of artificial vaccines, immune intervention and high throughput antibody preparation. At present, the most reliable method for locating the epitopes of B cells is to obtain the spatial structure of the complex by means of the diffraction experiment of antigen antibody complex crystal and the square method of nuclear magnetic resonance. However, these two kinds of methods have been used to obtain the space structure of the complex. Experimental methods require a high cost, a large number of manpower and a high demand for equipment. With the development of some auxiliary experimental methods and the increase of known table data, people begin to consider using computers for epitope prediction. The candidate tables obtained by the prediction method can be achieved through subsequent biological reality. Verify that the combination of experiment and computer can ensure the accuracy of the result, save cost and improve work efficiency.
The prediction of B cell epitopes based on phage combination peptide library is a prediction method of B cell epitopes combined with the combination of experimental and computational methods. First, a simulated epitope sequence with high affinity to antibodies is obtained by the screening experiment of phage combination peptide library, and then the epitope sequences are used to search the surface of the antigens on the surface of the antigen. In recent years, a number of B cell epitope prediction methods based on the screening of phage combination peptide library have been proposed, which have been better used in several test cases. But so far, there is not a universal standard test set in the prediction of B cell epitopes based on the phage combination peptide library, and there is no complete evaluation system for the analysis and comparison of the performance between the algorithms.
The research work of this paper mainly includes the construction of the B cell epitope prediction standard test set based on the screening of phage combination peptide library, the establishment of the evaluation system for the performance of the algorithm, and a more sensitive new method for predicting B cell epitopes based on the antigen structure information and the screening of the phage combination peptide library.
First, on the basis of the existing B cell epitope prediction method based on phage combination peptide library, this paper integrates the related information in 4 databases of MimoDB, PDB, CED and IEDB, and constructs a general standard test set. The standard test set and its representative test set are used for Mapitope, PepSurf, Pepitope, Pep-3D-Search, and EpiS. Earch 5 published B cell epitope prediction methods based on phage combination peptide library screening were tested, these 5 algorithms or source code or free network services. The article passes standard test set and its representative data set, and uses 4 evaluation parameters based on sensitivity, specificity, accuracy and Markov correlation coefficient. A comprehensive evaluation system for predicting the B cell epitopes screened by the combined peptide library was established, and the performance of the 5 algorithms was comprehensively evaluated and analyzed.
On the basis of comprehensive evaluation analysis, this paper proposes a more sensitive B cell epitope prediction method based on antigen structure information and phage combination peptide library screening. Firstly, the algorithm is used to classify antigen amino acids based on structural features and use support vector machines to achieve antigen preprocessing; then, the existing epitope prediction algorithm is used. Based on the idea of division, the epitope is predicted by dividing the antigen surface amino acid into a number of overlapping patch regions. In the process of constructing an undirected graph for each patch, the algorithm first attempts to use a variable distance threshold to define the connection of the vertex in the undirected graph. In addition, this paper first uses a complete search method. The path of search is proved to be optimal.
Finally, by comparing with the test results of the other 5 algorithms, the sensitivity of the proposed algorithm is obviously improved. This study is not only of great significance to the theoretical research of the B cell epitope prediction method, but also will promote its practical direction.
【學位授予單位】：東北師范大學
【學位級別】：博士
【學位授予年份】：2011
【分類號】：R392
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