本文關鍵詞：基于RCF算法的表位研究及一株鼠疫中和抗體表位鑒定　出處：《中國人民解放軍軍事醫(yī)學科學院》2017年博士論文　論文類型：學位論文

  更多相關文章： 分子對接 抗體分子建模 表位預測與鑒定

【摘要】：抗體是在抗原和免疫系統(tǒng)的相互作用下,由B淋巴細胞轉(zhuǎn)化的漿細胞產(chǎn)生的能與相應抗原發(fā)生特異性結合的免疫球蛋白。當抗體與這些抗原結合時,抗體上CDR區(qū)與抗原上的某個區(qū)域,即抗原決定簇(antigen determinant)結合。這里的抗原決定簇就是該抗體結合的表位。而結合表位的抗體CDR區(qū)上的氨基酸殘基構成抗體上的配位。表位分為線性表位和構象表位。線性表位由一段連續(xù)的氨基酸位點組成;而構象表位通常由一些在抗原一級序列上離散,但是在空間結構中相互靠近的位點共同構成。所以,抗原分子結構的變化可能會顯著影響抗體結合構象表位,但是對于線性表位影響并不大。表位是抗體最為重要的性質(zhì)之一,也是研究者最想獲得的抗體信息。通過抗體的表位,可以獲得抗體發(fā)揮保護作用的機制,研究病原體的致病機制,并能夠以表位為基礎,反向研究激發(fā)保護性抗體的疫苗。目前主流的研究抗體表位的方法是通過實驗來鑒定。這些實驗方法有些對實驗條件與設備要求高,有些方法工作量大,有些方法則成功率低。隨著計算機性能不斷增強以及模擬計算的方法不斷成熟,出現(xiàn)了一些應用在生物領域的分子模擬方法,能夠通過模擬對生物大分子進行研究。這些方法的主要特點是對實驗條件和設備要求低,大量的計算由計算機完成,并能為實驗設計提供明確指導,為實驗現(xiàn)象提供合理的解釋,越來越被研究人員所重視。本研究的主要目的是建立一種通過計算機建模,分子對接等分析方法,預測抗體表位,指導實驗進行驗證,快速簡便的對抗體表位進行鑒定的方法。這種方法的特點是僅需要抗體序列與抗原的晶體結構,就能對抗體表位進行預測,不僅需要的時間短,而且也沒有任何實驗要求,門檻較低。當然,對于預測的結果,需要通過實驗進行驗證才能確定。但是,計算機的預測結果給了我們一個目標去設計實驗,有了這個目標,通過簡單的突變實驗就能驗證,大大降低了表位鑒定的難度。在本文中,我們設計的表位鑒定方法具體步驟為:(1)通過Discovery Studio軟件,使用抗體的一級氨基酸序列建立模型,獲得抗體的分子結構;(2)抗原的晶體結構大多已經(jīng)通過X射線晶體衍射方法獲得,因而在PDB數(shù)據(jù)庫中下載相應的抗原晶體結構;(3)使用Discovery Studio軟件的ZDock,對抗體分子結構和抗原分子結構進行分子對接;(4)使用Residues Contact Frequency(RCF)算法對ZDock分子對接結果進行分析,預測抗原抗體相互作用的關鍵氨基酸;(5)設計實驗驗證預測結果。我們在Docking Benchmark 5數(shù)據(jù)庫中選取了22對抗原抗體作為測試集,對以上預測方法的有效性進行驗證。首先,我們驗證了Discovery Studio軟件對抗體結構的預測。對這22個抗體進行分子建模,將模型與真實的分子結構進行對比。我們發(fā)現(xiàn)抗體建模的準確程度很高。然后,我們驗證RCF算法對ZDock結果的預測分析。RCF是一種對ZDock結果進行統(tǒng)計分析,預測蛋白-蛋白相互作用關鍵氨基酸位點的方法。我們使用Perl語言在DS軟件中的Workscript窗口中實現(xiàn)了RCF算法。我們根據(jù)抗體結構的特殊性,對RCF算法設計了三種優(yōu)化:1.僅考慮抗體CDR區(qū)原子進行RCF分析;2.使用抗原抗體分子的夾角對ZDock預測的pose(復合物構象)進行篩選;3.依據(jù)抗原抗體分子的夾角為每個pose添加權重函數(shù)-cos。我們分析了RCF算法及三種優(yōu)化在測試集中的22對抗原抗體表位配位預測中的表現(xiàn),結果顯示RCF算法及三種優(yōu)化均能夠一定程度上對相互作用的關鍵氨基酸位點進行預測,并且三種優(yōu)化的RCF算法預測結果均好于未優(yōu)化的RCF算法,但是三種優(yōu)化之間區(qū)分并不明顯。于是我們選擇第一種優(yōu)化的RCF算法進行后續(xù)預測。在驗證了抗體分子建模和RCF優(yōu)化算法預測分析的有效性后,我們將這一方法應用在具體的抗體表位分析上。F2H5是一株鼠疫桿菌F1蛋白的抗體,是前期實驗室通過雜交瘤技術獲得的一株具有完全保護效果的鼠源抗體。實驗室前期完成了F2H5抗體的人源化。我們首先通過實驗確認人源化的F2H5抗體與F1蛋白在Western Blot和ELISA中均能相互結合。我們使用了主流的鑒定表位的實驗方法——合成F1蛋白重疊肽庫的方法,進行F2H5抗體的表位鑒定。但是出乎意料的是,所有的多肽都不與F2H5抗體結合。所以,我們采取了模擬計算的方法對抗體的表位進行預測,并進行實驗驗證。首先,我們使用DS軟件對F2H5抗體進行建模,從PDB數(shù)據(jù)庫中下載了F1蛋白的晶體結構,選取了其中分辨率較高的五個結構,分別與F2H5的結構進行分子對接,獲得了五個ZDockResults.dsv的結果文件。RCF優(yōu)化算法對這五個結果進行分析。預測結果顯示F1蛋白上F96和E105這兩個氨基酸位點可能是F2H5的關鍵氨基酸�；谶@一結果,我們對F1蛋白進行丙氨酸掃描,設計了F1蛋白95-111位氨基酸突變?yōu)楸彼岬膯吸c突變體。實驗結果顯示,F1-G104A、F1-E105A、F1-N106A三個F1蛋白突變體在ELISA與Western Blot中均不能與F2H5結合,同時F1-K101A、F1-N103A兩個突變體與F2H5抗體結合的能力明顯降低。我們通過RCF優(yōu)化算法成功鑒定了F2H5抗體的表位。完成了對F2H5抗體表位的鑒定后,我們使用G104E105N106這個表位篩選ZDock對接后產(chǎn)生的pose,選定了篩選后的pose中ZRank打分最高者作為F2H5-F1的相互作用的分子模型。對這個選定的pose,我們在DS軟件中使用分子動力學進行了進一步的優(yōu)化。根據(jù)優(yōu)化后的pose,我們計算了基于這個構象,F1上95-111位點丙氨酸突變后對復合物穩(wěn)定性的影響,其結果與丙氨酸掃描的結果吻合。我們進一步分析了抗體上的重要氨基酸位點。在RCF優(yōu)化算法中,預測出Y170和Y214非常關鍵。與此同時,我們又使用了一個分析抗體上氨基酸適合程度的算法——Amino Acid Interface Fitness(AIF),對F2H5抗體與F1的相互作用進行了分析,也發(fā)現(xiàn)了這兩個位點是最為關鍵的位點,并且酪氨酸是這兩個位點上最適合的氨基酸。基于之前確定的復合物結構,我們計算了抗體重鏈上CDR2和CDR3上氨基酸飽和突變后的突變能,選取了能量變化明顯的20株突變體進行實驗驗證。通過實驗發(fā)現(xiàn),20株突變體中,預測親和力減弱的11株突變抗體均不結合F1或親和力下降明顯,預測準確率為100%;預測親和力增加的9株突變抗體中,5株能夠與F1結合。在這5株能夠與F1結合的突變抗體中,2株F2H5上CDRH3區(qū)218位單點突變抗體,F2H5-D218R和F2H5-D218Y的EC50較F2H5表現(xiàn)出了顯著下降,說明獲得了親和力增強的突變體。我們建立了一個基于ZDock分子對接,RCF優(yōu)化算法的計算機預測抗體表位的方法。通過這個方法,我們對一株鼠疫F1蛋白的抗體F2H5的表位進行了預測,并成功通過實驗驗證了該預測表位。又通過計算機分析預測了抗原抗體復合物結構以及一些能夠引起抗體親和力顯著變化的抗體突變株,也通過實驗獲得了驗證。我們的結果說明,將計算機輔助的方法應用于抗體表位研究中,是有效并且很有意義的。
[Abstract]:In the interaction of antigen antibody and immune system, transformed by B lymphocytes plasma cells which can occur specific immunoglobulin binding with corresponding antigen. When combined with these antibodies and antigen, antibody and antigen on the CDR area of a region, namely the antigenic determinant (antigen determinant). Here is the antigenic determinant antibody binding epitopes. Combined with amino acid residues of CDR antibody epitope on the composition of antibody coordination. The epitope is divided into linear epitopes and conformational epitopes. The linear epitope consists of a continuous amino acid; and conformational epitopes usually consists of some in a sequence of discrete antigen, but close to each other in the spatial structure of the site together. So, change the molecular structure of the antigen may affect the antibody binding epitope, but for linear epitopes did not affect the epitope is. One of the most important properties of antibodies, antibody information but also of the most want to get. The antibody epitope mechanism can obtain antibody may play a protective role in the pathogenic mechanism of pathogens, and to epitope based reverse stimulation on protective antibody vaccine. The current mainstream method of antibody table who is identified by experiments. The experimental methods of some experimental conditions and equipment requirements, some methods of workload, some methods are low success rate. With the method of computer simulation and enhances the performance of the continuously mature, the simulation method of molecular applications in the field of biology, can through the simulation study biological macromolecules. The main features of these methods is the experimental conditions and equipment requirement is low, a large number of calculation by computer, and can provide clear guidance for experimental design, for real Experimental results provide a reasonable explanation, becomes more and more important. The main purpose of this study is to establish a through computer modeling, molecular docking analysis, prediction of epitopes, direct experimental verification, simple and rapid method for identification of antibody epitopes. The characteristic of this method is the only crystal structure need antibody and antigen sequence, will be able to predict epitopes, requires not only a short time, and there is no experimental requirements, low threshold. Of course, for the forecast results, through the experiment can be determined. However, the computer prediction results gave us a goal to design a experiment. This goal, through the mutation experiment can verify the simple, greatly reduces the difficulty of epitope identification. In this paper, we design the epitope identification method comprises the following steps: (1) by Discovery Stu Dio software to establish the model of primary amino acid sequence using antibody, molecular structure of the antibody; (2) the crystal structure of antigen have mostly through X ray diffraction method, the crystal structure of the corresponding antigen download in the PDB database; (3) using the Discovery Studio software ZDock, molecular docking of antibody molecules the molecular structure and antigenic structure; (4) using Residues Contact Frequency (RCF) algorithm for ZDock molecular docking results, prediction of key amino acid antigen antibody interaction; (5) the design of experiments. The prediction results we selected 22 antigen antibody in Docking Benchmark 5 database as test set to verify the effectiveness of above forecasting methods. First, we verified the predicted Discovery Studio software on the antibody structure. Molecular modeling of these 22 antibodies, the model with the real molecules The structure were compared. We found that the degree of accuracy is very high. Then the antibody modeling, we verify the prediction and analysis of.RCF RCF algorithm on ZDock result is a statistical analysis of the results of ZDock, prediction of protein protein interactions of key amino acids. We use the Perl language in DS software in the Workscript window to realize RCF algorithm. We according to the special antibody structure, three kinds of optimization algorithms of RCF design: 1. only consider the atomic antibody CDR region RCF analysis; 2. using antigen antibody molecules of pose angle ZDock prediction (complex conformation) screening; antigen antibody molecules according to an angle of 3. for each pose adding weight function -cos. we analyzed the RCF algorithm and three kinds of optimization in the test set 22 antigen antibody epitope ligand in the prediction of performance, results show that the RCF algorithm and the three optimization can to a certain extent The key amino acid sites of interaction prediction, and RCF algorithm to predict the three optimization results are better than the RCF algorithm is not optimized, but between the three kinds of optimization distinction is not obvious. So we choose the first kind of optimized RCF algorithm. In the subsequent prediction of antibody was verified Zi Jianmo analysis and prediction of RCF optimization algorithm then, we apply this method in the specific epitope analysis on.F2H5 antibody of Yersinia pestis F1 protein was obtained by the laboratory, is a strain of hybridoma technology has completely protective effect of murine anti F2H5 antibody. The previous complete human source. We first confirmed through experiments the humanized F2H5 antibody and F1 protein were combined in Western Blot and ELISA. We use the experimental synthesis of F1 protein overlapping peptide library identification method of the mainstream of the square table Method, epitopes were identified with the F2H5 antibody. But surprisingly, all of the peptides were not with F2H5 antibody binding. Therefore, we take the calculation of antibody epitopes were predicted and verified by the experiment. First, we use DS software to model the F2H5 antibody, the crystal structure of F1 protein the download from the PDB database, selects five high resolution structures, respectively. Molecular docking and F2H5 structure, obtained five results of ZDockResults.dsv file.RCF optimization algorithm to analyze these five results. The prediction results showed that F1 protein on F96 and E105 of the two amino acid sites may be the key amino acid F2H5. Based on this result, we performed an alanine scan of F1 protein, F1 protein design 95-111 amino acid mutations for single point mutants of alanine. Experimental results show that F1-G104A, F1-E105A, F1-N1 06A three F1 protein mutants were not with F2H5 in ELISA and Western Blot combination, and F1-K101A, F1-N103A two and F2H5 mutant antibody binding was significantly reduced by the RCF algorithm. We successfully identified F2H5 antibody epitope. The completion of the F2H5 antibody epitope identification, we use the pose G104E105N106 this epitope screening ZDock docking generated after the molecular model was selected after screening by pose ZRank in the highest scoring as F2H5-F1 interaction. The selected pose, we use molecular dynamics in DS software is optimized further. Based on the optimized pose, we calculated based on the conformation F1, on the site of 95-111 alanine mutation on stability of the complex, the results with alanine scanning results. We analyzed the important amino acid sites on the antibody in RCF optimization. In the algorithm, Y170 and Y214 prediction is very important. At the same time, we also use the Amino Acid Interface Fitness algorithm is an analysis of the antibody level for amino acid (AIF), the interaction of F2H5 antibody and F1 were analyzed, but also found that the two sites are the most important sites, and tyrosine this is the most suitable amino acids on the two loci. The composite structure based on the determined before, we calculated the antibody heavy chain CDR2 and CDR3 amino acid mutations after saturation mutagenesis, selects the energy change of 20 mutants significantly in experiments. Experimental results show that the 20 mutants, weakened affinity prediction the 11 mutant antibodies were not combined with F1 or affinity decreased significantly, the prediction accuracy is 100%; the 9 predicted increased affinity mutant antibody, 5 strains can bind with F1. In these 5 strains mutation could be combined with F1 Antibody, 2 strains of F2H5 CDRH3 218 single point mutations F2H5-D218R and F2H5-D218Y antibody, EC50 F2H5 showed significantly decreased, indicating the enhanced affinity of the mutant. We set up a ZDock based molecular docking prediction method of antibody epitope RCF optimization algorithm of computer. By this method, we the plague F1 antibody strain F2H5 protein epitopes were predicted, and through experimental verification of the predicted epitopes. And through the computer to analyze and predict the structure of antigen antibody complexes and some can cause significant changes in antibody antibody affinity of the mutant strain, was also verified by experiments. Our results show that the computer aided method applied to the antibody epitope study, is effective and meaningful.

【學位授予單位】：中國人民解放軍軍事醫(yī)學科學院
【學位級別】：博士
【學位授予年份】：2017
【分類號】：R392

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