本文關(guān)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(xué)科學(xué)院》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 分子對接 抗體分子建模 表位預(yù)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(xì)胞轉(zhuǎn)化的漿細(xì)胞產(chǎn)生的能與相應(yīng)抗原發(fā)生特異性結(jié)合的免疫球蛋白。當(dāng)抗體與這些抗原結(jié)合時(shí),抗體上CDR區(qū)與抗原上的某個(gè)區(qū)域,即抗原決定簇(antigen determinant)結(jié)合。這里的抗原決定簇就是該抗體結(jié)合的表位。而結(jié)合表位的抗體CDR區(qū)上的氨基酸殘基構(gòu)成抗體上的配位。表位分為線性表位和構(gòu)象表位。線性表位由一段連續(xù)的氨基酸位點(diǎn)組成;而構(gòu)象表位通常由一些在抗原一級序列上離散,但是在空間結(jié)構(gòu)中相互靠近的位點(diǎn)共同構(gòu)成。所以,抗原分子結(jié)構(gòu)的變化可能會顯著影響抗體結(jié)合構(gòu)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(hù)作用的機(jī)制,研究病原體的致病機(jī)制,并能夠以表位為基礎(chǔ),反向研究激發(fā)保護(hù)性抗體的疫苗。目前主流的研究抗體表位的方法是通過實(shí)驗(yàn)來鑒定。這些實(shí)驗(yàn)方法有些對實(shí)驗(yàn)條件與設(shè)備要求高,有些方法工作量大,有些方法則成功率低。隨著計(jì)算機(jī)性能不斷增強(qiáng)以及模擬計(jì)算的方法不斷成熟,出現(xiàn)了一些應(yīng)用在生物領(lǐng)域的分子模擬方法,能夠通過模擬對生物大分子進(jìn)行研究。這些方法的主要特點(diǎn)是對實(shí)驗(yàn)條件和設(shè)備要求低,大量的計(jì)算由計(jì)算機(jī)完成,并能為實(shí)驗(yàn)設(shè)計(jì)提供明確指導(dǎo),為實(shí)驗(yàn)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(jì)算機(jī)建模,分子對接等分析方法,預(yù)測抗體表位,指導(dǎo)實(shí)驗(yàn)進(jìn)行驗(yàn)證,快速簡便的對抗體表位進(jìn)行鑒定的方法。這種方法的特點(diǎn)是僅需要抗體序列與抗原的晶體結(jié)構(gòu),就能對抗體表位進(jìn)行預(yù)測,不僅需要的時(shí)間短,而且也沒有任何實(shí)驗(yàn)要求,門檻較低。當(dāng)然,對于預(yù)測的結(jié)果,需要通過實(shí)驗(yàn)進(jìn)行驗(yàn)證才能確定。但是,計(jì)算機(jī)的預(yù)測結(jié)果給了我們一個(gè)目標(biāo)去設(shè)計(jì)實(shí)驗(yàn),有了這個(gè)目標(biāo),通過簡單的突變實(shí)驗(yàn)就能驗(yàn)證,大大降低了表位鑒定的難度。在本文中,我們設(shè)計(jì)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(jié)構(gòu);(2)抗原的晶體結(jié)構(gòu)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(yīng)的抗原晶體結(jié)構(gòu);(3)使用Discovery Studio軟件的ZDock,對抗體分子結(jié)構(gòu)和抗原分子結(jié)構(gòu)進(jìn)行分子對接;(4)使用Residues Contact Frequency(RCF)算法對ZDock分子對接結(jié)果進(jìn)行分析,預(yù)測抗原抗體相互作用的關(guān)鍵氨基酸;(5)設(shè)計(jì)實(shí)驗(yàn)驗(yàn)證預(yù)測結(jié)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(yù)測方法的有效性進(jìn)行驗(yàn)證。首先,我們驗(yàn)證了Discovery Studio軟件對抗體結(jié)構(gòu)的預(yù)測。對這22個(gè)抗體進(jìn)行分子建模,將模型與真實(shí)的分子結(jié)構(gòu)進(jìn)行對比。我們發(fā)現(xiàn)抗體建模的準(zhǔn)確程度很高。然后,我們驗(yàn)證RCF算法對ZDock結(jié)果的預(yù)測分析。RCF是一種對ZDock結(jié)果進(jìn)行統(tǒng)計(jì)分析,預(yù)測蛋白-蛋白相互作用關(guān)鍵氨基酸位點(diǎn)的方法。我們使用Perl語言在DS軟件中的Workscript窗口中實(shí)現(xiàn)了RCF算法。我們根據(jù)抗體結(jié)構(gòu)的特殊性,對RCF算法設(shè)計(jì)了三種優(yōu)化:1.僅考慮抗體CDR區(qū)原子進(jìn)行RCF分析;2.使用抗原抗體分子的夾角對ZDock預(yù)測的pose(復(fù)合物構(gòu)象)進(jìn)行篩選;3.依據(jù)抗原抗體分子的夾角為每個(gè)pose添加權(quán)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(yù)測中的表現(xiàn),結(jié)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(guān)鍵氨基酸位點(diǎn)進(jìn)行預(yù)測,并且三種優(yōu)化的RCF算法預(yù)測結(jié)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(jìn)行后續(xù)預(yù)測。在驗(yàn)證了抗體分子建模和RCF優(yōu)化算法預(yù)測分析的有效性后,我們將這一方法應(yīng)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(shí)驗(yàn)室通過雜交瘤技術(shù)獲得的一株具有完全保護(hù)效果的鼠源抗體。實(shí)驗(yàn)室前期完成了F2H5抗體的人源化。我們首先通過實(shí)驗(yàn)確認(rèn)人源化的F2H5抗體與F1蛋白在Western Blot和ELISA中均能相互結(jié)合。我們使用了主流的鑒定表位的實(shí)驗(yàn)方法——合成F1蛋白重疊肽庫的方法,進(jìn)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(jié)合。所以,我們采取了模擬計(jì)算的方法對抗體的表位進(jìn)行預(yù)測,并進(jìn)行實(shí)驗(yàn)驗(yàn)證。首先,我們使用DS軟件對F2H5抗體進(jìn)行建模,從PDB數(shù)據(jù)庫中下載了F1蛋白的晶體結(jié)構(gòu),選取了其中分辨率較高的五個(gè)結(jié)構(gòu),分別與F2H5的結(jié)構(gòu)進(jìn)行分子對接,獲得了五個(gè)ZDockResults.dsv的結(jié)果文件。RCF優(yōu)化算法對這五個(gè)結(jié)果進(jìn)行分析。預(yù)測結(jié)果顯示F1蛋白上F96和E105這兩個(gè)氨基酸位點(diǎn)可能是F2H5的關(guān)鍵氨基酸。基于這一結(jié)果,我們對F1蛋白進(jìn)行丙氨酸掃描,設(shè)計(jì)了F1蛋白95-111位氨基酸突變?yōu)楸彼岬膯吸c(diǎn)突變體。實(shí)驗(yàn)結(jié)果顯示,F1-G104A、F1-E105A、F1-N106A三個(gè)F1蛋白突變體在ELISA與Western Blot中均不能與F2H5結(jié)合,同時(shí)F1-K101A、F1-N103A兩個(gè)突變體與F2H5抗體結(jié)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(gè)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(gè)選定的pose,我們在DS軟件中使用分子動力學(xué)進(jìn)行了進(jìn)一步的優(yōu)化。根據(jù)優(yōu)化后的pose,我們計(jì)算了基于這個(gè)構(gòu)象,F1上95-111位點(diǎn)丙氨酸突變后對復(fù)合物穩(wěn)定性的影響,其結(jié)果與丙氨酸掃描的結(jié)果吻合。我們進(jìn)一步分析了抗體上的重要氨基酸位點(diǎn)。在RCF優(yōu)化算法中,預(yù)測出Y170和Y214非常關(guān)鍵。與此同時(shí),我們又使用了一個(gè)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(jìn)行了分析,也發(fā)現(xiàn)了這兩個(gè)位點(diǎn)是最為關(guān)鍵的位點(diǎn),并且酪氨酸是這兩個(gè)位點(diǎn)上最適合的氨基酸�；谥按_定的復(fù)合物結(jié)構(gòu),我們計(jì)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(jìn)行實(shí)驗(yàn)驗(yàn)證。通過實(shí)驗(yàn)發(fā)現(xiàn),20株突變體中,預(yù)測親和力減弱的11株突變抗體均不結(jié)合F1或親和力下降明顯,預(yù)測準(zhǔn)確率為100%;預(yù)測親和力增加的9株突變抗體中,5株能夠與F1結(jié)合。在這5株能夠與F1結(jié)合的突變抗體中,2株F2H5上CDRH3區(qū)218位單點(diǎn)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(qiáng)的突變體。我們建立了一個(gè)基于ZDock分子對接,RCF優(yōu)化算法的計(jì)算機(jī)預(yù)測抗體表位的方法。通過這個(gè)方法,我們對一株鼠疫F1蛋白的抗體F2H5的表位進(jìn)行了預(yù)測,并成功通過實(shí)驗(yàn)驗(yàn)證了該預(yù)測表位。又通過計(jì)算機(jī)分析預(yù)測了抗原抗體復(fù)合物結(jié)構(gòu)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(shí)驗(yàn)獲得了驗(yàn)證。我們的結(jié)果說明,將計(jì)算機(jī)輔助的方法應(yīng)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(xué)位授予單位】：中國人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：R392

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