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Improving MHC binding peptide prediction by incorporating binding data of auxiliary

Vol.22no.132006,pages1648–1655

doi:10.1093/bioinformatics/btl141 BIOINFORMATICS ORIGINAL PAPER

Data and text mining

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

Shanfeng Zhu1,Keiko Udaka2,John Sidney3,Alessandro Sette3,Kiyoko F.Aoki-Kinoshita1 and Hiroshi Mamitsuka1,?

1Bioinformatics Center,Institute for Chemical Research,Kyoto University,Gokasho,Uji611–0011,Japan,

2Department of Immunology,Kochi Medical School,Nankoku,Kochi783–8505,Japan and3La Jolla Institute for Allergy and Immunology,10335Science Center Drive,La Jolla,CA92121,USA

Received on June23,2005;revised on March8,2006;accepted on April8,2006

Advance Access publication April13,2006

Associate Editor:Satoru Miyano

ABSTRACT

Motivation:Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule.These methods are based on known binding peptide sequences.However,current available peptide databases do not have very abundant amounts of examples and are highly redundant.Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities.Therefore,we first give a method for reducing the redundancy in a given dataset based on information entropy,then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules.

Results:We experimented on the HLA-A family with the binding non-amers of A1supertype(HLA-A?0101,A?2601,A?2902,A?3002),A2 supertype(A?0201,A?0202,A?0203,A?0206,A?6802),A3supertype (A?0301,A?1101,A?3101,A?3301,A?6801)and A24supertype (A?2301and A?2402),whose data were collected from six publicly available peptide databases and two private sources.The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype.Our approach can thus be used to help find new binders for MHC molecules. Contact:mami@kuicr.kyoto-u.ac.jp

Supplementary information:Supplementary data are available at Bioinformatics online.

1INTRODUCTION

Major histocompatibility complex(MHC)molecules bind short peptides from antigens and present them on the surface of a cell for recognition by T Cell Receptors(TCR)[For general information on MHC see Janeway et al.(2001)].The presented peptide and MHC complexes induce the na?¨ve T Cells to proliferate and differ-entiate into armed effector T cells that help to remove the antigens. MHC molecules show high diversity in their selectivity of peptides, making it dif?cult for pathogens to escape immune response. Each different MHC molecule can bind a set of different peptides. As antigen recognition by MHC molecules is the prerequisite of cellular immune response,it is of great immunological importance to have the ability to accurately predict those peptides that bind to speci?c MHC molecules.The experimental identi?cation of peptide binding af?nity to MHC molecules requires a binding assay of each peptide,which is a time consuming and costly process.Therefore,a number of alternative research efforts have been carried out in an attempt to discover the laws of binding peptide sequence patterns. Past predictive approaches can be divided into two main groups: MHC molecule structure based approaches and binding peptide sequence based approaches.In the former case,the crystal structure of the MHC molecule is required,which may not be possible to obtain for many MHC molecules.In the latter approach,the sequences of peptides are studied in order to ascertain binding patterns.After the discovery of main anchor residues by pooling sequences(Falk et al.,1991),secondary anchors(Ruppert et al., 1993)and peptide sequence motifs(Rammense et al.,1995),posi-tion speci?c quantitative matrix methods have been proposed to predict the binding af?nity of a given peptide.Quantitative matrices such as BIMAS,SYFPEITHI and RANKPEP are constructed by analyzing the amino acid frequency in the binding peptides during pool sequencing(Rammensee et al.,1999),side chain scanning (Hammer et al.,1994;Parker et al.,1994;Gulukota et al., 1997),by revealing positional amino acid preferences with the use of combinatorial peptide libraries(Udaka et al.,2000),and sequence alignment of binding peptides(Reche et al.,2002).Fur-thermore,machine learning based approaches,such as arti?cial neural network(ANN)(Gulukota et al.,1997;Brusic et al., 1998a),hidden markov model(HMM)(Mamitsuka,1998;Udaka et al.,2002),classi?cation and regression tree(CART)(Segal et al., 2001)and support vector machine(SVM)(Do¨nnes,and Elofsson 2002;Riedesel et al.,2004),have been introduced.Several studies comparing the performance of quantitative matrix and machine learning based methods found that machine learning based methods need more training data than matrix based methods to achieve good performance.(Yu et al.,2002;Peters et al.,2003).

To develop an effective computer model in a bioinformatics approach,we need to understand the characteristics of the biological data at hand(Brusic et al.,1998c,1999).There are several concerns regarding the MHC binder databases currently available.First,the number of peptides for each MHC molecule is very limited,due to high experimental costs.In addition,although it may be easy to?nd a new binder that is similar to an existing one,it is dif?cult to?nd a

?To whom correspondence should be addressed.

unique and completely new binder.For example,seven or eight amino acid positions out of the nine in peptides’sequences may be the same,such as‘ALAKAAYAV’,an HLA-A?0201binder in MHCPEP(Brusic et al.,1998b).Correspondingly,10peptides with the pattern‘ALAKAAXXV’,where X is an amino acid, can easily be found in MHCPEP as well.In short,any currently available binder database is highly redundant.We thus need to reduce the redundancy of the current database,assuming that the true data space of peptide binders is more general.

Our work attempts to overcome these data issues.(1)We give a new method for reducing the redundancy of an MHC binding pep-tide database.This method is based on entropy,and by reducing the redundancy we can obtain a dataset representing a general data space of MHC binders which is more representative than any exist-ing database.(2)We propose a novel computational method for predicting MHC-binding peptides by learning the predictive model from both the binding data of the MHC molecule of interest,as well as other MHC molecules.Studies show that MHC molecules can be classi?ed into a relatively few number of supertypes(superfamilies) in terms of binding speci?cities by different criteria,such as motifs (supermotifs)of binding peptides(Sette and Sidney,1999), amino acid sequence similarities(Cano et al.,1998;McKenzie et al.,1999),functional pockets in the binding groove (Chelvanayagam,1996;Zhang et al.,1998),structural similarities (Doytchinova et al.,2004)and binding speci?city matrices(Lund et al.,2004).Different MHC alleles in the same supertype have highly similar structure in the main binding peptide pocket and bind largely overlapping sets of peptides,which is also recognized in chimpanzees(Bertoni et al.,1998).Cross-reactive peptides are frequently observed in the process of cancers and infectious diseases (Bertoni et al.,1997;Doolan et al.,1997).Speci?cally,Sette and Sidney(1999)divided HLA class I molecules into nine supertypes, A1,A2,A3,A24,B7,B27,B44,B58and B62.Brusic et al.found that HLA class I binding data of multiple alleles in the same super-type could accurately predict binding peptides for alleles that have no experimental data available(Brusic et al.,2002;Srinivasan et al., 2004).Sturniolo et al.(1999)made use of pocket pro?les to build virtual matrices for predicting promiscuous HLA-DR ligands.In contrast to these studies,we combine the binding data of the MHC allele of interest with the binding data of another MHC allele, regardless of supertype,to improve prediction accuracy.Through this study,the effect of combining the binding data of two different alleles,in the same or different supertypes,can be examined. We examine this novel idea for nonameric peptide binding pre-diction to16HLA-A molecules in four different supertypes with respect to several studies(Sette and Sidney,1999;Lund et al., 2004):A1(HLA-A?0101,A?2601,A?2902,A?3002),A2 (A?0201,A?0202,A?0203,A?0206,A?6802),A3(A?0301, A?1101,A?3101,A?3301,A?6801)and A24(A?2301and A?2402).The results show that our approach signi?cantly improves the prediction accuracy of peptides that bind a speci?c HLA mole-cule when we combine binding data of MHC molecules in the same supertype.

2MATERIALS AND METHODS

2.1Source data

In this study,HLA-A binding nonamers were collected from six public databases,MHCPEP(Brusic et al.,1998b),SYFPEITHI(Rammensee et al.,1999),FIMM(Scho¨nbach et al.,2002),MHCBN(Bhasin et al., 2003),AntiJen(Blythe et al.,2002)and Ligand(Sathiamurthy et al., 2003)in March2005,as well as from two private data sources(A.Sette, unpublished data;K.Udaka,unpublished data).Because of varying experi-mental conditions on binding assays,the real-valued binding af?nity mea-surements produced by these different research groups are incompatible. Moreover,these values are often unavailable in these databases.Therefore, we make binary predictions on peptide binding ability to HLA-A molecules. After deleting peptides that have undetermined amino acids in their sequences and removing redundant peptides,our dataset consists of alto-gether16alleles in different supertypes.As shown in Table1,12of these that have no less than95distinct binding nonamers used in the combination experiment.Considering the lack of experimentally veri?ed nonamers that do not bind MHC molecules,we note that it was estimated that<1%of any nonameric peptide would bind a particular MHC molecule(Udaka et al., 1995)and that randomly generated putative non-binding peptides have been

Table1.The number of binding nonamers in each database and total number of distinct nonamers in all databases for each allele

MHCPEP MHCBN AntiJen FIMM Ligand SYFPEITHI Sette Udaka Total distinct A?0101691354100210162

A?2601072033414039

A?29021010033156067

A?3002010004446056

A?0201356821700186251241145661438

A?02022971932221660269

A?02031362774131280213

A?02061962742421170204

A?680264173237660150

A?030159769411178700184

A?11016985129202030830254

A?31011819566713530122

A?330133347410121095

A?680158617771112530147

A?230100000027027

A?2402841537420152827135357

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used in other studies(Do¨nnes and Elofsson,2002).Therefore,we randomly generated putative non-binding nonamers from proteins in the human gen-ome from the KEGG database(Kanehisa et al.,2004),ensuring that they are distinct from the known binding peptides of the MHC molecules of interest.

2.2Predictive model

We utilize the popular inductive learning algorithm C4.5,which generates a decision tree classi?er for prediction(Quinlan,1993).The original technique of decision tree was established in the1970s(Friedman,1977;Quinlan, 1979),and it has been developed and matured in1990s.C4.5is one of the most popular and basic decision tree learning methods.The learned results are comprehensive and easily understandable,which is important to allow further veri?cation by biologists.We construct the prediction model using C4.5release8(downloadable from https://www.wendangku.net/doc/294662490.html,/Personal/). The generation of a decision tree is a recursive data partitioning,and C4.5maximizes information content to split data,i.e.it maximizes informa-tion entropy.We denote the instances in the binding class as positive instances,and those in the non-binding class as negative instances.In each MHC molecule’s binding dataset,we set the ratio of positive to nega-tive instances to1:1to obtain a balanced training dataset,shown to achieve better classi?er performance than natural class distributions(Weiss and Provost,2003).We note that the main purpose of this research is to deter-mine whether or not the predictive accuracy of peptide binding to the HLA molecule of interest could be improved by incorporating the binding data of other HLA molecules,as opposed to improving the performance of existing computational prediction models.That is to say,other computational meth-ods such as HMM may easily be used to build the predictive model. 2.3Evaluation

For each dataset of binding nonamers to an MHC molecule,we conduct24?ve-fold cross validation experiments.Prediction accuracy is the percentage of correctly identi?ed instances out of all instances in the test set.The average prediction accuracy on the test sets over all24rounds is used to evaluate performance.That is,we build the predictive model120times to obtain an average prediction accuracy to reduce any bias in the random partitioning.In this way,each dataset S has a corresponding C4.5model predictive accuracy that is calculated by24?ve-fold cross validations.The paired sample two-tailed t-test is used to compare the performance of two predictive models for the same test.If the t-value is larger than a certain value,say3.373when comparing two sets of120prediction accuracy values, then the performance of one model is statistically signi?cant over the other at con?dence level99.9%.

2.4Dataset combination

To describe the dataset combination procedure in detail,we?rst de?ne two terms:base dataset and auxiliary dataset.The base dataset is the peptide binding dataset of the MHC molecule of interest.The auxiliary dataset is the peptide binding dataset of another MHC molecule,which will be added into the base dataset for improving the prediction accuracy of the MHC molecule of interest.The detailed procedure we used to combine the base and auxiliary datasets is as follows:

Input.A peptide binding dataset S a for MHC molecule A(base dataset), a peptide binding dataset S b for MHC molecule B(auxiliary dataset),and C4.5program to build the predictive model(decision tree).

Output.The prediction accuracy before and after combination,and their corresponding t-value.

(1)Run24five-fold cross validation experiments on original data set S a.

That is,divide S a into a training set S training and a test set S testing.

(2)After training the predictive model M on S training using C4.5with

default settings,obtain the prediction accuracy A initial on S testing with M.

(3)Keeping S testing unchanged,add all instances of S b to S training except for

the instances that already exist in S a,calling this the new training dataset S0training.Then train a new predictive model M0using C4.5 with default settings.Finally,predict the binding ability of peptides in testing set S testing with M0to obtain a new accuracy A combine.

(4)Evaluate all120pairs of A initial and A combine using the statistical t-test. Note that it is important to maintain consistency between the base and auxiliary datasets.That is,since we verify accuracy improvement by com-bining these two datasets,the prediction accuracy of each dataset should be made the same.This prediction accuracy is controlled by reducing the redundancy of each dataset using a new technique which we describe in the next section.

2.5Redundancy reduction

In general,redundancy reduction techniques enable predictive models to avoid over?tting and to reproduce well on unseen data.Different redundancy reduction techniques are already used in various studies on MHC peptide binding prediction(Yu et al.,2002;Do¨nnes and Elofsson,2002;Buus et al., 2003;Nielsen et al.,2004),but they tended to be rather ad hoc and primitive. Yu et al.(2002)simply removed all peptides from the training set that differed by only a single amino acid from the test peptides,Do¨nnes and Elofsson(2002)ensured that no two peptides shared more than four amino acids in the binding dataset,and Buus et al.(2003)discarded peptides whose pairwise alignment scores exceeded a given threshold.Nielsen et al.(2004) performed homology reduction to make sure no peptide in test set shared sequence identity>90%with the peptides in training set.On the other hand, we use the measurement of entropy from information theory to reduce redundancy in the binding peptides.Given a set S of binding peptides (without non-binding peptides)for a speci?c MHC molecule,we derive a 20row·9column matrix C containing the count of each distinct amino acid occurring in a speci?c position in S.Denoting each element n ij in matrix C as the number of amino acid i occurring in position j among all peptides,and N as the total number of peptides in the set S,the entropy of dataset S is:

EntropyeST?à

X20

i?1

j?1

n ij

·log

n ij

In our procedure,we repeat the process of removing the peptide that maximizes this entropy function.That is,we repeatedly select a set of Nà1peptides from the set of N peptides and measure the information content to?nd the set that maximizes this measure.The maximization of information content by C4.5to recursively partition the data is thus consis-tent with our approach of reducing the redundancy of a peptide dataset.Since the prediction accuracies need to be kept consistent during our experiments, for each HLA molecule’s binding peptide dataset,we produce peptide bind-ing datasets whose predictive accuracies from the24cross-validation runs are$80%.

Given a binding peptide dataset S of size N,the pseudocode for creating peptide binding dataset Set(A)at predictive accuracy level A(say80%)from S is shown in Figure1.The speci?c percentage of peptides that are removed by redundancy reduction depends on the size of the initial dataset,the redundancy in the initial dataset,and the target prediction accuracy.In Table2,the number of instances in each dataset of each HLA molecule at80%accuracy levels is listed.

2.6Experimental procedure

Our experimental procedure consists the following:

(1)Preliminary experiments on controlled datasets.We carried out our experiments on two types of preliminary datasets:one combining homo-geneous datasets(binding data from the same MHC molecule)and one incorporating a randomly generated dataset.

Combining homogeneous datasets.As shown in Figure2,the base dataset S a is constructed by randomly selecting200nonameric peptides

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from the dataset S of all available nonamers that bind to the HLA-A ?0201molecule (positive instances)and by randomly generating 200nonameric peptides from the human genome database (negative instances).Then an auxiliary dataset S h is created similarly from S and the human genome database,ensuring that no peptide existing in S h is found in S a .S h is then added to S a ,and two prediction accuracies A initial and A combine are obtained.To reduce any bias that may exist in the particular dataset selected,we generate S a and S h randomly 50times to calculate a set of A initial and A combine values,which are then analyzed using the paired sample t -test.

Combining random datasets.The base dataset is constructed in the same way as the base dataset in the previous section,but the secondary dataset is generated differently.To create an auxiliary random dataset S r ,both the 200positive and 200negative instances,none of which occur in S a ,are randomly generated from the human genome database.Further-more,to investigate the effect of the size of combined random peptide binding datasets,eight random datasets S r 1,S r 2,...,S r 8of varying sizes are generated:200,400,800,1200,1600,2000,3000and 4000.Each of

these random datasets are then individually combined with the base dataset to calculate the prediction accuracy.These procedures are repeated 50times to reduce bias.

(2)Main experiments combining peptide binding datasets of different HLA molecules .As the dataset of our focus,the peptide binding datasets of different HLA molecules at the same accuracy level,resulting from our redundancy reduction technique,are combined in pairs to analyze the effect of combination.All these datasets were examined under the same experi-mental procedure using our predictive model.We are especially interested in the HLA molecule pairs whose initial predictive accuracies improve after combination.

3RESULTS

3.1

Preliminary experiments

Combining homogeneous datasets As illustrated in Table 3,the prediction accuracy is improved significantly after incorporating S h into S a .The results of this experiment show that when two homogeneous datasets are combined together to build our pre-dictive model,the predictive accuracy is improved significantly.

Combining random datasets As shown in Table 4,incorporat-ing a random dataset into the base dataset reduces the accuracy of prediction.As the size of the random datasets increase,the pre-diction accuracy of the combined datasets decrease correspond-ingly.The prediction accuracy of the combined peptide binding dataset decreases monotonically as the size of the added random dataset increases.The initial predication accuracy is 80.6.From these preliminary experiments combining homogeneous and random datasets,we can see that adding a similar peptide binding dataset into the base dataset improves the predictive accuracy of our model,while adding random peptide binding datasets decreases performance.

3.2

Combining peptide binding datasets of different HLA molecules

We combined the datasets of two different HLA molecules at the same accuracy level.The result of the combination at accuracy

level

Table 2.The number of all (binding and non-binding)nonamers in the datasets for each HLA molecule at 80%accuracy level Molecule A ?0101A ?0201A ?0202A ?0203A ?0206A ?6802A ?0301A ?1101A ?3101A ?3301A ?6801A ?2402Set(80%)

226

1294

350

276

266

210

184

204

122

132

176

286

Fig.2.The creation of homogeneous datasets in our experiment.

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80%is shown in Table 5,which can be viewed as a prediction accuracy matrix A ij .In this matrix,cell a ii in the principal diagonal of the matrix represents the prediction accuracy of the dataset for the corresponding molecule before adding any auxiliary binding data.The other cells a ij represent the prediction accuracy of the HLA molecule at row i after incorporating the auxiliary dataset of the HLA molecule at column j .Each cell (except along the principal diagonal)contains not only the prediction accuracy after combination,but also the t -value in the paired sample t -test comparing prediction accuracy before and after combination over the 24?ve-fold cross validation runs.If there exists a statis-tically signi?cant difference (99.9%or above)in prediction accuracy before and after combination,the t -value is printed in bold.An improvement in the prediction accuracy at a statistically signi?cant level (99.9%or above)is indicated by a bolded prediction accuracy value after combination.Our experimental results show that combining binding data of different types of molecules to improve prediction accuracy works in various combinations.Examining these cases,we are especially interested in HLA molecule pairs A and B such that the addition of the binding data of A to B improves the prediction accuracy of B ,and vice versa.

From these experimental results,we ?nd that the improvement in prediction accuracy mainly comes from the combination of two alleles in the same supertype who have similar peptide binding speci?cities.Out of 40statistically signi?cantly improved combi-nations from all 132possible combinations,34(85%)belong to the combination of two alleles in the same supertype.On the other hand,out of all 40combinations of two alleles in the same supertype,38(95%)improve the original prediction accuracy,and 34(85%)are statistically signi?cant at the 99.9%level.Thus we next focus on the combinations of two alleles in the same supertype.In our experi-ment,there are only two supertypes that have more than two alleles,the A3supertype (A ?0301,A ?1101,A ?3101,A ?3301and A ?6801)and the A2supertype (A ?0201,A ?0202,A ?0203,A ?0206and A ?6802). 3.2.1A ?0301,A ?1101,A ?3101,A ?3301and A ?6801Among the 20possible combinations of alleles from A ?0301,A ?1101,A ?3101,A ?3301and A ?6801at the 80%level,all improve the original prediction accuracies,and 19(95%)are statistically sig-ni?cant at the 99.9%level.This indicates that the combination of peptide binding datasets between HLA molecules in this group of HLA-A ?0301,A ?1101,A ?3101,A ?3301and A ?6801always improves the original prediction accuracy.

3.2.2A ?0201,A ?0202,A ?0203,A ?0206and A ?6802Among the 20possible combinations of alleles from A ?0201,A ?0202,A ?0203,A ?0206and A ?6802at the 80%accuracy level,18(90%)improve the original prediction accuracies and 15(75%)are statistically signi?cant at the 99.9%level.This indicates that the combination of peptide binding datasets between HLA mole-cules in this group of HLA-A ?0201,A ?0202,A ?0203,A ?0206and A ?6802usually improves the original prediction accuracy.

On the other hand,the combination of two alleles in different supertypes can hardly improve the original prediction accuracy.Among all 92of such possible combinations,only 6(6.5%)improve the original prediction accuracies statistically signi?cantly at the 99.9%level.We also ?nd that the combination of peptide binding data of two alleles in the A2and A3supertypes,respectively,decreases the initial prediction accuracy signi?cantly.One notable example is that the combination of A ?6801and A ?6802decreases the original prediction accuracy signi?cantly.Although A ?6801and A ?6802belong to the same allotype,they have distinctly different peptide binding speci?cities and are classi?ed into A3and A2supertype,respectively.Among the 50possible combinations of two alleles each from the A2and A3supertypes,48(96%)decrease the initial prediction accuracies statistically signi?cantly at the 99.9%level,implying that these alleles in two different supertypes differ greatly in peptide binding speci?cities.An interesting result came from the fact that the combination of A ?0101and A ?0301,and A ?0101and A ?1101,improve the original prediction accuracy.Since these alleles represent two different supertypes that are asso-ciated with somewhat different main anchor speci?cities,we believe that the improvements observed can be attributed to shared prefer-ences at non-anchor positions.It has indeed been noted that A ?0101,A ?0301and A ?1101bear a close evolutionary kinship (McKenzie et al .,1999;Lawlor et al .,1990).It has also been observed that mutation rates are faster at residues forming the main peptide bind-ing pockets than at other sites along the peptide binding region (Sette and Hughes.,2003;Hughes and Hughes,1995).Together,these observations suggest that the similarities between A ?0101,A ?0301and A ?1101re?ect their common ancestry.

3.2.3Summary Based on the experimental results at accuracy level 80%,we have three basic observations.

The combination of peptide binding data of HLA-A alleles in the same supertype,e.g.within the A2or A3supertypes,improves original prediction accuracy.

The combination of peptide binding data of HLA-A alleles in different supertypes hardly improves original prediction accu-racy,and sometimes decreases statistically significantly. Even though in different supertypes,the combinations of A ?0101and A ?0301,A ?0101and A ?1101improved the original prediction accuracy.

Table 4.The prediction accuracy of the combined peptide binding dataset decreases monotonically as the size of the added random dataset increases.

S r 1S r 2S r 3S r 4S r 5S r 6S r 7S r 8A 78.676.475.072.572.069.767.164.2t 9.415.915.219.919.021.917.618.0S

200

400

800

1200

1600

2000

3000

4000

The initial prediction accuracy is 80.6

Table 3.The comparison of A initial (predictive accuracy before combination)and A combine (predictive accuracy after combining a homogeneous dataset)over 50experiments.eA initial is the mean of A initial ,and A combine is the mean of A combine ).A initial A combine t -value 80.9

83.7

5.6

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We also carried out combination experiment of different HLA molecules at accuracy level75%,and obtained similar observations (see Supplementary information I).Furthermore,to verify the gen-eralization of our method,we examined the performance of incorp-orating binding data of different HLA alleles in the same supertype by another predictive SVM model.Similar observations were obtained and the experimental results are provided under Supple-mentary information II.In addition,we explored the sequence simi-larity of different peptide binding datasets during combination.The experimental results are provided in Supplementary information III. We show that the sequence similarity of binding peptide datasets of the alleles in the same supertype is signi?cantly higher than those in different supertypes,which helps to explain the improvement in prediction accuracy after incorporating the binding data of alleles in the same supertype.

4DISCUSSION

Incorporating new data for predicting MHC binding peptide has also been examined by other researchers.Yu et al.found that with more training data,the performance of a prediction system by ANN and HMM could be improved in general(Yu et al.,2002).Brusic et al. cyclically re?ned the predictive model to improve prediction accu-racy by inclusion of new data(Brusic et al.,2001),i.e.the binding data of the MHC molecule of interest.On the other hand,to predict binding peptide to the MHC molecule without experimental data, some researchers incorporated binding data of MHC molecules in the same supertype(Brusic et al.,2002;Srinivasan et al.,2004).To verify their proposed supertypes of HLA class I alleles,Lund et al. (2004)used the peptide binding weight matrices of HLA molecules to predict the binding af?nity of peptides to other HLA molecules in the same supertype.They found that the predictive value is posi-tively correlated with experimental value.In contrast to these stud-ies,with original binding data,we further combine the binding data of other MHC molecules that belong to any supertype,whether it be the same or different supertype.In this way,the effect of combining peptide binding data of two different MHC molecules in the same or different supertype is examined.From the experimental results,we ?nd that combining binding data of two MHC molecules in the same supertype usually improves the prediction accuracy.Thus the key in improving prediction accuracy is to identify the group of MHC molecules with similar peptide binding speci?cities.

The development of vaccines that can cover a broad distribution of the human population stimulates researchers to classify HLA alleles into supertypes with similar speci?cities.Based on super-motifs shared by different HLA molecules,Sette and Sidney(1999) reported nine supertypes(A1,A2,A3,A24,B7,B27,B44,B58, B62)in HLA Class I molecules.For example,the HLA alleles in A3 supertype prefer A,V,I,L,M,S or T in position2,and R,K or Y at the C-terminus.The HLA alleles in A2supertype prefer to L,I,V, M,A,T or Q at position2,and L,I,V,M,A or T at the C-terminal position.This evidence can explain the improvement of prediction accuracy in our experiment of combining peptide binding data of alleles in the same A2or A3supertype,and the decrease of pre-diction accuracy when combining peptide binding data of two alle-les respectively in A2and A3supertype.The improvement of prediction accuracy in combining A?0101with A?0301,and A?0101with A?1101can also be explained in this way.Even though A?0101is phylogenetically and structurally similar to A?0301and A?1101(McKenzie et al.,1999;Doytchinova et al.,2004),A?0101 has different peptide binding speci?cities,and can not be classi?ed into the A3supertype.The most telling difference is in position77, which constitutes a major peptide contact residue in the F pocket. Unlike alleles(such as A?0301and A?1101)in the A3supertype, A?0101has an N,rather that the acidic residue D.This difference changes the ability of the pocket to accommodate the basic residues (R and K)preferred by A3-supertype molecules.In spite of obvious differences,A?0101still has some common features with A?0301 and A?1101in terms of peptide binding speci?cities.A?0101pre-fers T,S,I,V,L and M at position2,which is similar to the preferences of A?0301and A?1101,and Y at the C-terminus. Since A?0301and A?1101also prefer Y in the C-terminus,we can see that A?0101has some common features with A?0301 and A?1101in terms of peptide binding speci?cities.This interme-diate level of cross activity is only one-way,as A?0101does not prefer R or K in the C-terminus.It can also explain the phenomenon in our experiment that when A?0101binders are added to A?0301 (or A?1101)binders,the accuracy was improved to around84%, which is higher than around just82%obtained by adding A?0301 (or A?1101)to A?0101.

Table5.The prediction accuracy of the combined dataset from different HLA molecules at the same accuracy level of80%(corresponding t-values are given in parentheses)

Molecule A?0101A?0201A?0202A?0203A?0206A?6802A?0301A?1101A?3101A?3301A?6801A?2402

A?010180.378.0(3.9)79.8(1.1)81.4(2.2)80.3(0.01)81.7(3.3)82.2(4.1)82.2(4.1)79.9(0.8)80.3(0.1)80.5(0.4)80.2(0.1) A?020180.0(1.3)80.481.3(5.6)80.4(0.03)81.6(7.5)80.4(0.3)79.2(10.9)79.9(4.4)79.2(10.2)79.8(4.2)79.6(5.7)77.6(17.3) A?020279.3(0.2)88.0(23.7)79.481.2(4.9)82.9(9.6)80.8(4.2)77.3(5.6)74.7(10.4)74.7(12.5)76.2(8.3)76.7(6.4)79.5(0.1) A?020380.3(0.9)84.7(10.1)81.9(4.2)79.880.8(1.9)79.9(0.3)78.1(3.55)70.6(15.7)70.9(19.9)76.9(5.8)77.0(4.9)79.1(1.3) A?020681.9(3.36)86.3(14.0)82.9(5.9)82.0(3.55)80.481.9(3.41)75.6(10.3)75.3(9.96)75.8(11.3)78.4(4.04)77.3(5.13)80.2(0.36) A?680281.1(1.2)85.4(8.9)81.1(1.5)82.3(3.8)82.8(4.0)80.573.1(15.6)76.5(7.0)74.6(11.8)76.5(6.9)75.9(8.6)78.8(3.4) A?030184.0(11.3)76.0(5.7)73.7(9.4)73.2(8.7)68.9(16.5)71.0(11.7)80.184.5(12.8)83.7(13.4)83.3(10.7)84.2(13.4)74.4(7.2) A?110183.8(9.6)69.4(12.9)77.7(4.8)73.7(7.1)77.1(5.9)80.7(0.9)83.6(8.9)80.283.7(9.0)83.4(9.1)83.8(9.2)76.0(7.8) A?310179.3(1.35)64.8(16.2)71.0(11.2)65.8(13.3)75.3(8.4)72.7(9.55)84.4(8.1))84.4(8.1)80.280.6(0.64)83.1(5.5)59.4(17.7) A?330180.3(0.1)75.9(5.0)68.9(15.5)71.4(9.2)72.3(9.7)72.0(9.3)83.0(6.4)83.1(6.8)82.6(6.4)80.383.2(7.3)71.0(8.9) A?680180.3(0.2)79.0(1.4)71.2(11.8)71.4(10.1)72.5(11.1)71.0(12.1)83.0(8.9)83.0(8.9)81.5(4.8)82.4(5.7)80.268.9(13.9) A?240282.3(4.5)72.2(10.0)82.0(3.44)80.9(1.3)80.9(1.3)81.2(2.3)78.7(3.15)79.0(2.3)76.8(8.9)78.8(3.44)76.5(11.4)80.1

Improving MHC binding peptide prediction

1653

This work also sheds light on the study of evolution of HLA class I genes.In a review article Klein et al.(1993)indicated that the evolution of MHC molecules has occurred through accumulated mutations,and they compared the evolutionary rate of MHC class I with that of class II.Many studies found that there are many gene conversions in HLA class I molecules(Hughes et al., 1993;Parham et al.,1988).In addition,it was reported in Hughes et al.(1993)that HLA-A?0101,A?0301and A?1101are all close to each other in terms of evolution.More often than HLA class II genes,HLA class I genes have exploited gene conversion like recombination events in order to transplant an anchor preference en bloc.Although this appears to have been a rather effective tactic in changing the anchor amino acids,the present analysis of binding similarities between A?0101,A?0301and A?1101may show that HLA evolution is still a slow process carried on over a substantial part of the repertoire.

In this article,we have proposed a new approach for predicting binders to an MHC molecule by incorporating auxiliary peptide binding data from other MHC molecules.We have also presented a method for reducing redundancy in a set of binding peptides.Our experimental results show that our approach signi?cantly improves the accuracy of predicting peptides binding an MHC molecule, especially when the base and auxiliary molecules belong to the same supertype having similar peptide binding speci?cities.Inter-esting future work should explore the effect of the combination of binding data from multiple alleles in the same supertype. ACKNOWLEDGEMENTS

The authors would like to thank Nicolas Majeux for performing preliminary experiments,and the reviewers who provided many suggestions to improve the original manuscript.This work is sup-ported in part by Bioinformatics Education Program‘Education and Research Organization for Genome Information Science’and Kyoto University21st Century COE Program‘Knowledge Information Infrastructure for Genome Science’with support from MEXT (Ministry of Education,Culture,Sports,Science and Technology),Japan.

Conflict of Interest:none declared.

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2014年医学免疫学与微生物学形成性考核作业(含答案)

2014年医学免疫学与微生物学形成性考核作业（含答案）作业1 1 免疫系统具有哪些功能？这些功能正常或市场表现出何种生物学效应？答：①免疫防御，是机体抗御，清除病原微生物等外来抗原性异物侵袭的一种免疫保护功能，即通常所指的抗感染免疫作用。免疫防御反应异常增高可引发超敏反应；反应过低或缺失，则可引发免疫缺陷病或对病原体高度敏感。②免疫自稳，是机体免疫系统及时清除体内衰老，损伤或变性细胞，而对自身免疫成分处于耐受状态，以维持内环境相对稳定的一种生理功能。免疫自稳功能失调，可引发自身免疫性疾病。③免疫监视，是机体免疫系统及时识别，清除体内突变细胞和病毒感染的一种生理性保护作用。免疫监视功能失调，可引发肿瘤或病毒持续性感染。 2 简述影响抗原免疫原性的因素答：①抗原的理化性质，⑴化学性质具有免疫原性的物质通常是大分子有机物质，蛋白质最具有免疫原性。⑵分子大小，具有免疫原性的物质分子量一般大于10KD，通常分子量越大，免疫原性越强。⑶化学结构和组成，大分子有机物质并不一定都有良好的免疫原性。 ⑷分子构像和易接近性，当抗原分子构想发生改变，使表面特殊化学基团隐藏在抗原分子内部，这时该分子免疫原性即消失。⑸物理状态，聚合状态抗原的免疫原性通常较其单体要强。 ②宿主的因素，⑴异物性，通常机体仅对外源性物质产生免疫应答，而对自身物质产生免疫耐受。⑵遗传因素。⑶年龄，性别，健康状态。 3 简述免疫球蛋白的主要生物学功能答：①可变区功能，可变区功能主要是识别特异性抗原，主要靠超变区识别。②恒定区功能，⑴激活补体系统，导致补体经典途径激活。⑵调理作用，促进吞噬细胞对抗原的吞噬作用。⑶抗体依赖性细胞介导的细胞毒作用，增加其对靶细胞的破坏作用。⑷介导I型超敏反应，引起过敏。⑸穿过胎盘屏障和粘膜，对新生儿抗感染具有重要意义。 4 简述补体的主要生物学活性答：①溶解细菌和细胞溶解作用。补体激活产生的膜攻击复合物在细菌或细胞表面形成穿膜亲水通道，可以产生溶解细菌和靶细胞的作用。②调理作用，补体产生一类非特异性调理素，它们与细菌或其它颗粒性抗原结合后，可以被吞噬细胞识别结合。③免疫黏附与清除免疫复合物作用。④炎症介质作用，包括过敏作用，趋化和激活作用，即激肽样作用。⑤参与特异性免疫应答。 5 简述II型干扰素的主要生物学功能。答：①上调抗原提呈细胞MHC-II类分子的表达，提高抗原提呈能力。②上调靶细胞表面MHC-I类分子，增强杀伤性T细胞对肿瘤和病毒感染的细胞的杀伤作用。③促进CD4阳性T细胞向Th1细胞转化。④与Il-4具有拮抗作用，可以一直Th2细胞生成，从而降低体液免疫。⑤激活巨噬细胞，中性粒细胞，NK细胞。⑥诱导靶细胞产生抗病毒蛋白，⑦诱导血管内皮细胞表达黏附分子。作业2

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E．免疫调节 7. 机体免疫系统识别和清除突变细胞的功能称为 A．免疫监视 B．免疫缺陷 C．免疫耐受 D．免疫防御 E．免疫调节 8. 首次用于人工被动免疫的制剂是 A.破伤风抗毒素 B.破伤风类素素 C.肉毒类毒素 D.白喉类素素 E.白喉抗毒素 9. 首次应用类毒素进行预防接种的科学家是 A.Pasteur B.Behring C.Jenner D.Border E.Burner 10．最早发明减毒活疫苗的科学家是 A.Jenner B.Koch C.Porter D.Burnet E.Pasteur 11．英国科学家Jenner发明了 A. 白喉抗毒素 B.狂犬疫苗 C.人痘苗 D.牛痘苗 E.卡介苗 12．创建杂交瘤技术制备单克隆抗体的学者是 A. .Koch和Pasteur B. Miller和Good C. Milstein和K?hler D. Tislius和Kabat E. Porter和Edelman 13．最早提出克隆选择学说的科学家是 A. Burnet B. Border C.Porter D. Jenner E.Pasteur 14．免疫系统的组成是 A、中枢免疫器官、周围免疫器官 B、免疫细胞、粘附免疫系统、中枢免疫器官 C、中枢免疫器官、免疫细胞、皮肤免疫系统 D、免疫分子、粘附免疫系统、皮肤免疫系统 E、免疫器官、免疫组织、免疫细胞、免疫分子【X型题】 1．免疫细胞包括 A．淋巴细胞 B．单核-巨噬细胞 C．抗原提呈细胞 D．粒细胞 E．红细胞 2．下列哪些细胞具有特异性识别抗原的能力？ A．巨噬细胞 B．T细胞

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免疫学名词解释第一章：免疫学概论 1.免疫防御：防止外界病原体的入侵及清除已入侵病原体及其他有害物质。 2.免疫监视：是机体免疫系统及时识别并清除体内出现的非己成分的一种生理功能。该功能失调会导致肿瘤发生或持续性病毒感染。 3.免疫自身稳定：通过自身免疫耐受或免疫调节两种主要机制来达到免疫系统内环境的稳定。 4.适应性免疫应答的特点：特异性、耐受性、记忆性第二章：免疫器官和组织 1.免疫系统：是机体执行免疫功能的物质基础，由免疫器官和组织、免疫细胞及免疫分子组成。 2.淋巴细胞归巢：血液中的淋巴细胞选择性趋向迁移并定居于外周免疫器官的特定区域或特定组织的过程。包括淋巴细胞再循环和淋巴细胞向炎症部位迁移。 3.淋巴细胞再循环：是指定居在外周免疫器官的淋巴细胞，由输出淋巴管经淋巴干、胸导管或右淋巴导管进入血液循环；经血液循环到达外周免疫器官后，穿越HEV，重新分布于全身淋巴器官和组织的反复循环过程。第三章：抗原 1.抗原（Ag）：是指能与T细胞、B淋巴细胞的TCR或BCR识别并结合，激活T、B细胞，促使其增殖、分化，产生抗体或致敏淋巴细胞，并与免疫应答效应产物特异性结合，进而发挥适应性免疫效应应答的物质。 2.半抗原：又称不完全抗原，是指仅具有免疫反应性而无免疫原性的小分子物质，当半抗原与应答效应产物结合后即可成为完全抗原，刺激机体产生针对半抗原的特异性抗体。 3.抗原表位：存在于抗原分子中决定抗原特异性的特殊化学基团，又称抗原决定簇，是与TCR、BCR或抗体特异性结合的最小结构和功能单位。 4.异嗜性抗原：一类与种属无关，存在于人、动物及微生物之间的共同抗原。6.独特型抗原：TCR、CER或Ig的V区所具有的独特的氨基酸顺序和空间构型，可诱导自体产生相应的特异性抗体。 7.超抗原：指在极低浓度下即可非特异性激活大量T细胞克隆，产生极强的免疫应答，且不受MHC限制，故称超抗原。 8.佐剂：预先或与抗原同时注入体内，可增强机体对该抗原的免疫应答或改变免疫类型的非特异性免疫增强性物质，称佐剂。 10.完全抗原:同时具有免疫原性和免疫反应性的物质称为完全抗原 11.胸腺依赖性抗原：指刺激B细胞产生抗体需要Th细胞辅助的抗原，简称TD 抗原。 12.胸腺非依赖性抗原：刺激B细胞产生抗体无需Th细胞辅助的抗原，简称TI 抗原。第四章：免疫球蛋白 1.抗体（Ab）：是介导体液免疫的重要效应分子，是B细胞或记忆B细胞接受抗原刺激后增殖分化为浆细胞所产生分泌的一类能与相应抗原特异性结合的、具有免疫功能的球蛋白。 6.单克隆抗体：是由单一杂交瘤细胞所产生的、只作用于单一抗原表位的高度均一的特异性抗体。 7.抗体依赖的细胞介导的细胞毒作用（ADCC）：抗体的Fab段结合靶细胞表面的

分子免疫学-复习总结

一、绪论免疫:指机体识别和排除抗原性异物的功能免疫功能: 1免疫防御:机体抵抗病原微生物入侵, 中和毒素的能力。过高：超敏反应过低：免疫缺陷2免疫稳定:机体清除损伤和衰老的自身细胞，维持体内生理平衡。异常：自身免疫病 3免疫监视:发现并清除体内突变细胞，防止细胞癌变。异常：肿瘤免疫器官:中枢免疫器官:胸腺：T细胞分化成熟的场所骨髓：免疫细胞产生的场所,B细胞分化成熟的场所免疫细胞发生、分化、成熟的场所外周免疫器官:脾脏，淋巴结，扁桃体，黏膜伴随淋巴组织免疫细胞定居，发生免疫应答的场所免疫细胞:参与免疫反应或与免疫反应有关的细胞适应性（特异性）免疫细胞：T细胞、B细胞、抗原提呈细胞（APC）固有（非特异性）免疫细胞：Mφ、NK 、B1、单核细胞、中性粒细胞免疫分子:膜型分子（结合在细胞膜上）：TCR、BCR、CD分子、MHC分子分泌型分子（存在于体液中）: Ig分子、补体分子、细胞因子二、免疫球蛋白将具有抗体活性或化学结构与抗体相似的球蛋白统称为----免疫球蛋白Ig 分泌型：血清抗体膜型：B细胞膜上的抗原受体将机体受抗原刺激后出现的能与抗原发生特异性结合，具活性的球蛋白--- 抗体Ab。抗体是Ig，而Ig并非都是抗体。命名:H链：分五类δ、μ、γ、ε、α链IgD 、IgM、IgG、IgE、IgA L链：分两型κ型和λ型高变区（hypervariable regio，HVR）可变区中某些区域的aa组成和排列特别易变化或具更高的变易性。 CDR（互补决定区）：Ig的抗原结合部位和抗原表位互补结合部位，决定抗体的特异性。铰链区:位于CH1和CH2之间，富含脯aa，富有弹性，可自由折叠意义：能使V区与不同距离的抗原结合补体结合位点易于暴露功能区作用:VL、VH：抗原结合部位HVR（CDR）与抗原表位结合CH1、CL：遗传标志所在 IgG-- CH2：补体结合位点，通过胎盘部位CH3：与各种组织表面IgG Fc受体（FcγR）结合部位 IgM：CH3 ：补体结合位点 IgE：CH2、CH3 ：与肥大细胞、嗜碱性粒细胞的（IgEFc受体FcεR）结合部位 J链（Joining Chain）：连接两个或两个以上Ig单体作用SIgA：二聚体IgM：五聚体分泌片SP（Secretory Piece）：是SIgA上的一个辅助成分。上皮细胞合成，分泌到黏膜细胞表面作用：具抵抗外分比液中蛋白水解酶的降解作用，稳定SIgA的作用。免疫球蛋白的生物学特性：1特异性结合抗原：2活化补体：IgM，IgG1，IgG2，IgG3-------经典途径；凝聚的IgA，IgG4，IgE --------旁路途径3结合Fc受体（1，介导I型超敏反应2，调理吞噬作用3，发挥ADCC作用）：Ig + Ag Ig的Fc 段活化与细胞表面的Fc受体结合4通过胎盘抗体恒定区的异质性：Ig 类型[类：IgG、IgM 、IgA、IgD、IgE 亚类：IgA：IgA1、IgA2（α1、α2）] 抗体异质性产生因素：外源性－－Ig 多样性(自然界多种不同的抗原（表位）诱导机体产生多种不同的特异性抗体,同一种抗原（表位）诱导机体产生特异性相同、类型不同的抗体) 内源性－－Ig 血清型( Ig具有双重性：1与相应抗原发生特异性结合－抗体特性;2可诱导机体产生特异性抗体－抗原特性。血清型类型：同种型：是同一种属所有个体Ig分子共有的抗原特异性标志，为种属型标志，存在于Ig 的C区同种异型：存在于同种不同个体中的抗体分子也具有免疫原性，称为同种异型。是同一种属不同个体间Ig分子所具有的不同抗原特异性标志，为个体型标志，存在于Ig 的C区或V区独特型：同一种属、同一个体来源的抗体分子,其免疫原性亦不尽相同,称为独特型,主要由于其CDR区的氨基酸序列的不同，是每个免疫球蛋白分子所特有的抗原特异性标志 Ig基因多样性形成机制：1组合造成的多样性（V-51,D-30,J-6）2连接造成的多样性3体细胞高频突变造成的多样性免疫球蛋白的生物合成:Ig主要由脾、淋巴结和其他淋巴组织内的桨细胞所产生。重链和轻链分别合成，然后装配。 Ig的合成过程：转录、mRNA剪切、合成重链和轻链；在粗面内织网装配四肽链；转运、加糖基、分泌胞外。 B细胞在抗原刺激后，最初只合成IgM，后合成IgG。

微生物学与免疫学基础作业集第6次作业

微生物学与免疫学基础作业集第三章抗原与免疫分子一、名词解释 1.抗原是指能够刺激机体产生（特异性）免疫应答，并能与免疫应答产物抗体和致敏淋巴细胞在体内外结合，发生免疫效应（特异性反应）的物质。 2.抗体指机体的免疫系统在抗原刺激下，由B淋巴细胞或记忆细胞增殖分化成的浆细胞所产生的、可与相应抗原发生特异性结合的免疫球蛋白。 3.补体系统补体并非单一分子，而是存在于血清、组织液和细胞膜表面的的一组不耐热的经活化后具有酶活性的蛋白质，包括30余种可溶性蛋白和膜结合蛋白，故被称为补体系统。 4.细胞因子是一类能在细胞间传递信息、具有免疫调节和效应功能的蛋白质或小分子多肽。二、简答题 1.为何HLA基因或表型测定可用于亲子鉴定与确定个体身份？由于1)HLA的高度多态性、2）单元型遗传以及3）HLA基因型终身不变，因此，HLA 基因或表型检测可用于亲子鉴定和确定个体身份。 2.简述IgG与IgM的特性与功能。 IgG――含量最高的Ig、半寿期约20～23天、能通过胎盘、是主要的抗菌抗病毒抗体； IgM――五聚体结构、结合补体能力最强、胚胎发育晚期即可合成、体液免疫最早出现的抗体，单体结构镶嵌在B细胞膜上，是B细胞抗原受体的主要成分。第四章免疫器官与免疫细胞第五章免疫应答一、名词解释 1.适应性免疫指体内抗原特异性T/B淋巴细胞接受抗原刺激后，自身活化、增殖、分化为效应细胞，产生一系列生物学效应的全过程。 2.固有免疫是机体在种系发育和进化过程中形成的天然免疫防御功能，即出生后就已具备的非特异性防御功能，也称为非特异性免疫(nonspecific immunity)。二、简答题 1.简述Th1细胞和Th2 细胞的免疫效应。 Th1细胞分泌IFN-γ，能强力激活巨噬细胞，活化的巨噬细胞可清除所吞噬的病原体并引起局部炎症。Th1细胞分泌的TNF可诱导靶细胞凋亡和促进炎症反应。 Th2细胞分泌IL-4、IL-5、IL-6，其主要功能是促进B细胞增殖、分化成浆细胞产生抗体，和抗寄生虫感染。 2.试比较抗体初次应答和再次应答。 . 抗体的初次应答抗体的再次应答应答细胞初始B细胞记忆性B细胞潜伏期较长较短抗体类型先产生IgM，后产生IgG、IgA IgG为主抗体浓度、维持时间浓度低，维持时间短浓度高，维持时间长

免疫学作业问题详解

第一章免疫学发展简史及其展望 1.免疫、免疫学的概念？答：免疫：是指机体识别和排除抗原性异物的功能，从而维持机体的生理平衡和稳定。正常情况下，对机体是有利的；但在某些情况下，则对机体是有害的。免疫学：研究机体免疫系统结构和功能的科学。研究免疫系统的结构和功能，机体的感染、抗肿瘤免疫机制及免疫病理机制，为生命科学提供有效的免疫学诊断和免疫防治方法等的一门科学。 2.免疫学在生物科学中的地位？答：现代免疫学已成为生命科学和医学中的前沿科学，免疫学发展水平是一个国家综合科学实力及发展水平的指标之一。免疫学在20世纪取得的辉煌成就，在消灭传染病及人类感染及非感染疾病方面获得的巨大成效，在揭示生命活动基本规律，发展生物论和方法上的任何一次突破和进展，均会极促进生命科学和医学的发展。 3.免疫系统的组成与功能？答：免疫系统是由免疫器官（胸腺、骨髓、脾、淋巴结等）、免疫组织（黏膜相关淋巴组织）、免疫细胞（吞噬细胞、自然杀伤细胞、T及B淋巴细胞）及免疫分子（细胞表面分子、抗体、细胞因子、补体等等）组成。免疫系统具有： ①免疫防御功能②免疫耐受③免疫监视功能④免疫调节选择题 1.免疫对机体是：（ E ） A.有害的 B.有利的 C.有利也有害 D.有利无害 E.正常条件下有利，异常条件下有害 2.固有免疫应答的作用特点是（ C ） A.维持时间较长； B.经克隆扩增和分化，有免疫记忆； C.不经克隆扩增和分化，作用迅速，无免疫记忆； D.主要参与的分子是特异性抗体； E.以上均不是。 3.机体免疫系统识别和清除突变的细胞的功能称为（ A ） A.免疫监视 B.免疫自稳 C.免疫耐受 D.免疫防御 E.免疫识别 4. 免疫防御功能低下的机体易发生：（ C ） A.肿瘤 B.超敏反应 C.反复感染 D.自身免疫病 E.移植物排斥反应 5.免疫监视功能低下的机体易发生：（ A ） A.肿瘤 B.超敏反应 C.反复感染 D.自身免疫病 E.移植物排斥反应填空题： 1.免疫系统是由免疫器官、免疫分子、免疫细胞、免疫组织组成。 2. 在体有两种免疫应答类型，一种是固有性免疫应答，另一种是适应性免疫应答。 3. 特异性免疫应答有特异性、记忆性和获得性三大特点。 4.非特异性免疫应答有无特异性、先天具备、初次与抗原接触即能发挥效应和可稳定遗传四大特点。第三章抗原 1.抗原的概念、特性答：抗原（antigen）是指能与TCR/BCR或抗体结合，具有启动免疫应答潜能的物质。特性：①免疫原性②抗原性 2、为什么说医用破伤风抗毒素既是抗体又是抗原？答：医用破伤风抗毒素（属于动物免疫血清）Array 破伤风类毒素→免疫动物（马）→动物血清中含大量抗毒素（动物免疫血清经精制）人体（特异性治疗和紧急预防用）动物免疫血清对人体具有两重性： ①提供了特异性抗体，中和细菌的外毒素，防治疾病。 ②是异种动物蛋白质，可引起超敏反应，有免疫原性。

免疫学名词解释

免疫 ,通过结合细胞表面的相应受体发挥生 B细胞后,通过同一静脉区基因与不同细胞区基因的重组, IgM转换为IgG等其他类别的过程 ,其编码分 ,出现或消失的细胞表面标记 ,将其包围,形成小泡,并吞入细胞内的过程,又称内化 ,广泛参与机体的免疫应答以及免疫调节 T细胞科隆,产生极强的免疫应答的抗原 B细胞交叉瘤产生的只识别抗原分子中特定抗原表位的特异性抗体.优点-结构均一,纯度高,特异性强,交叉反应少;缺点-具有鼠源性 TCR及BCR或Ig的γ层所具有的独特的结构组成的自身抗原 .优点-作用全面,来源广泛,制备容易;缺点-特异性不高,易发生交叉反应,应用受限 ,多基因性着重于向一个个体中MHC 基因,座位的变化,而多态性非群体中各座位等位基因的变化 CK,可干扰病毒感染和复制(抗病毒),分为αβγ三种类型,其IFN-α和IFN-β合称为I型,IFN-γ由活化T细胞合NK细胞产生,称II 型干扰素 MHC分子结合的各个不同抗原肽所具有的相同或相似的AA序列

,是生物体在长期种族进化过程中形成的一系列防御机制 DNA水平,对Ig基因进行切割拼接或修饰,导入受体细胞表达的抗体增殖分化 ,是B细胞接受抗原刺激后增殖分化为浆细胞所产生的糖蛋白,主要存在于血清等体液中 T细胞的TCR及B细胞的CBR结合,促进其增殖,分化,产生或致敏淋巴细胞,并与之结合,进而发挥免疫效应的物质 ,加工,处理抗原并将抗原信息提呈给T淋巴细胞的细胞,单核-巨噬细胞,树突状细胞DC,B淋巴是专职的APC,内皮细胞,纤维母细胞,各种上皮间皮细胞接受外界刺激后也可提呈Ag为非专职APC ,经血液循环趋向性迁移并定居于外周免疫器官或组织的特定区域 ,淋巴液,淋巴器官或组织间反复循环的过程 MHC分子的抗原结合槽结合的特定的AA残基 ,其结果是维持正常的生理功能具有抗体活性或化学结构与抗体相似的球蛋白黏膜相关淋巴组织MALT,主要指呼吸道,肠道及泌尿生殖道黏膜固有层和上皮细胞下散在的无被膜淋巴组织,以及某些带生发中心的器官化的淋巴组织型λ型 ,中性粒细胞,淋巴细胞等进

免疫学名词解释

免疫学名词解释 1、免疫（Immunity）：免疫是指机体识别和清除一切抗原异物以保持自身稳定的生理反应，如果免疫系统失调，免疫反应过强、过弱或对自身成分发生免疫应答都将对机体造成损害。 2、免疫防御（immunologic defense）：免疫防御指防止外界病原体入侵和清除已入侵病原体及有害的生物性分子，此功能就是机体的抗感染免疫。但异常情况下，免疫反应过强可引起超敏反应，而免疫功能过低则表现为易受感染或免疫缺陷病等。 3、免疫自稳：（immune homeostasis）：免疫自稳指机体对自身成分的耐受，对自身衰老和损伤细胞的清除，阻止外来异物入侵并通过免疫调节达到维持机体内环境稳定的功能。 4、免疫监视（immunologic surveillance）：免疫监视是指监督机体内环境出现的突变细胞及早期肿瘤，并予以清除。若此功能失调，体内突变细胞失控，可导致肿瘤发生，若病毒感染不能及时被清除，而出现病毒持续性感染状态。 5、淋巴细胞归巢(lymphocyte homing )：成熟淋巴细胞离开中枢淋巴器官后，经血液循环趋向性迁移并定居在外周淋巴器官或组织的特定区域，称为淋巴细胞归巢。 6、淋巴细胞再循环(lymphocyte recirculation)：定居在外周淋巴器官的淋巴细胞，可由输出淋巴管经淋巴干、胸导管或右淋巴导管进入血液循环，淋巴细胞随血液循环到达外周免疫器官后，可穿越HEV，并重新分布于全身淋巴器官和组织。淋巴细胞在血液、淋巴液、淋巴器官或组织间反复循环的过程称为淋巴细胞再循环。 7、抗原（Antigen，Ag）：是一类能刺激机体免疫系统产生特异性免疫应答,并能与相应的免疫应答产物在体内或体外发生特异性结合的物质。

细胞与分子免疫学---第三部分问答

第14章细胞凋亡与免疫 PCD, programmed cell death 第一节细胞凋亡概述诱导凋亡制剂： 1、Ca＋＋/Mg++：为内源性DNA内切酶所依赖，Zn2＋能拮抗之. 2、糖皮质激素：常见的凋亡诱导剂,机制为促进凋亡相关蛋白质合成，可被蛋白合成抑制剂抑制。 3、细胞因子： IL-2:可增强Fas途径介导的AICD,增加FasL的转录和表达,并抑制ＦＬＩＰ(Fas信号抑制剂)的转录和表达 IL-10:可通过Fas/ FasL，使活动型SLE病人PBMC凋亡 IL-12:促进TNF /TNFR途径引起的凋亡; IFN-γ:使高表达IFN-γR的T细胞凋亡； TNF、TGF- β：促进凋亡。 4、抗原：Ag结合sIgM，sIgM交联，PKC激活，胞内钙库释放，诱发细胞凋亡。 5、抗体：抗sIgM、Fas、CD3/TCR、CD4、CD8 CD23等抗体诱导表达相应膜抗原的细胞发生凋亡。 6、超抗原及丝裂原：SAg金葡菌肠毒素诱导胸腺内DP细胞凋亡，PWM诱导T细胞凋亡抑制凋亡制剂 ?细胞因子： IL-2：可抑制糖皮质激素诱导的Ｔｈ1细胞的凋亡,其抑制凋亡的机制可能是通过蛋白激酶Ｃ(PKC)活化途径实现的； IL-4:可抑制糖皮质激素诱导的Th2细胞凋亡。这可能是通过bcl-2的高表达或通过活化PKC途径实现的； IL-10:可抑制感染细胞的凋亡； IL-12:可抵抗60Ｃo,γ射线引起的小鼠骨髓细胞凋亡； IFN -γ:抑制低表达IFN –γR T细胞凋亡。免疫相关的凋亡信号转倒 (一)DR介导的信号途径 ?caspase：含半胱氨酸的天冬氨酸蛋白酶 ?caspase家族: 酶作用点为天冬氨酸残基 caspase 以酶原形式存在于胞内-激活-剪切 caspase-2,8,10,9为起始（上游）caspase caspase-3,6,7是凋亡效应（下游）caspase. caspase -3可以激活ＤＮＡ降解酶,降解ＤＮＡ导致细胞凋亡 FasL+Fas －Fa多聚体化－Fas-DD+DD-FADD(Fas associated protein with death domain) －（N端）FADD-DED+DED-proCaspase8(10)---DISC Cas8(10)活化tBid －剪切和激活下游Cas pase 释放细胞色素C,pro-Cas2,3,9 －激活Cas3、6、7 激活Cas9 －apoptosis (二)线粒体途径线粒体是各种死亡刺激的感受器。促凋亡信号如ＤＮＡ损伤、生长因子去除以及大部分化疗药物通常可诱导线粒体释放细胞色素Ｃ(cytochrome-c，Cyt C)和其他促凋亡多肽,释放的Cyt C与凋亡蛋白酶激活因子1(Apaf1)相结合，促使Apaf1形成寡聚体。在ATP/dATP存在下, Apaf1招募procaspase- 9聚集形成称为凋亡体(apoptosome)的复合体, procaspase 9通过自我活化产生具有活性的caspase- 9.最终导致细胞凋亡。调控机制 1、Bcl-2家族：。抑制凋亡/促进增殖： bcl-2、bcl-xl等阻止线粒体外膜通透化，从而阻止cyto-c释放。 bcl-2为原癌基因，凋亡抑制基因通过抑制细胞内内源性氧族的产生而抑制凋亡抑制P53,c-myc的凋亡作用，抗激素、辐射等诱导的凋亡. bcl-2/bax抑制凋亡；bcl-xl与bcl-2有协同作用促进凋亡:分为二个亚类: ?bax、bak和bcl-xs等, ?bad、bim、bid等， ?bax,bcl-xs促进凋亡；bax/bax促进凋亡

免疫学课程作业答案汇总

一、单选题 1.(4分)与HBV的致病机理不符的叙述是 A. 使肝细胞表面抗原改变引起自身免疫应答 B. 免疫复合物可引起免疫病理损伤 C. HBV在肝细胞内增殖可直接损伤肝细胞 D. 效应T细胞可杀伤带HBV抗原的肝细胞 E. HBsAg引起的Ⅰ型超敏反应答案E 2.(4分)病毒体感染细胞的关键物质是 A. 核壳体 B. 核酸 C. 衣体 D. 刺突 E. 包膜答案B 3.(4分)大肠杆菌在肠外感染中最常见的病是 A. 尿路感染 B. 肺炎 C. 败血症 D. 盆腔炎 E. 腹膜炎答案A

4.(4分)具有过敏毒素样作用的补体裂解片段是 A. C2a B. C3a C. C1 D. C3 E. C2 答案B 5.(4分)最常见的化脓性球菌是 A. 金黄色葡萄球菌 B. A群链球菌 C. B群链球菌 D. 脑膜炎奈瑟菌 E. 淋病奈瑟菌答案A 6.(4分)立克次体与病毒的共同点 A. 对抗生素不敏感 B. 以二分裂方式繁殖 C. 专性细胞内寄生 D. 没有细胞壁和细胞膜 E. 必须以节肢动物作为传播媒介纠错答案C 7.(4分)下列消毒灭菌法的选择,哪项是错的

A. 金属器械用漂白粉 B. 排泄物用生石灰 C. 饮水用氯气 D. 培养基用高压蒸气法 E. 血清用滤过除菌法答案A 8.(4分)肥达（Widal）氏反应常用于何种疾病的辅助诊断 A. 伤寒沙门氏菌感染 B. 痢疾志贺氏菌感染 C. 变形杆菌感染 D. 脑膜炎奈瑟菌感染答案A 9.(4分)狂犬病毒的包涵体最易出现的部位是 A. 肌肉 B. 淋巴结 C. 外周神经组织 D. 骨髓 E. 大脑海马回部位答案E 10.(4分)细胞因子不包括 A. 淋巴毒素 B. 干扰素

免疫学考博试题说课讲解

四军医大2013 1. 试比较T细胞受体（TCR）、B细胞受体（BCR）和NK细胞受体（NKCR）的组成，识别配体以及信号转导的异同点。 2. 以胸腺依赖抗原刺激机体产生抗体的免疫应答为例，T细胞和B细胞是如何相互作用？有哪些粘附分子和共刺激分子参与T、B细胞的相互作用？ 3. 肾综合征出血热(HFRS)病毒的结构基因已经搞清楚,为了证实HFRS病毒感染机体(以Balb/C小鼠为例)可产生HFRS病毒核衣壳蛋白(NP)特异性CTL,并在免疫防护中起重要作用,请应用免疫学理论和方法,设计一系统实验,加以证实。 4. 例举二个近年来细胞和分子免疫学研究中出现的新的热点,请分别评述其研究意义、发展趋势以及应用前景华中科技大学2013年同济医学院免疫学考博真题抗原表位，肿瘤特异性抗原，adcc，细胞表面标志，细胞因子，MHC，调理作用，中枢耐受，排斥反应的T细胞直接识别，沉淀反应。 1，抗体的功能区极其特点2，天然免疫及适应性免疫的特点 3，乙肝疫苗接种后，抗体产生的过程及免疫机制4，迟发型型超敏的免疫学机制 2013年北京协和医学院免疫试题二、选择题三、填空题 MHCII与之相连的结构（恒定连I i ） IgE恒定区有（）个结构域克隆选择学说是（）提出的，因此他获得了诺贝尔奖（）指的是单条染色体上基因的特定组合四、简答题 1、半抗原与载体连接成为完全抗原，试述载体蛋白的作用 2、T细胞表位与B细胞表位的区别 3、双向琼脂扩散的工作原理五、论述题 1、初次免疫应答与再次免疫应答的区别，举出三个并说明其原理 2、举出B细胞所有的亚群，并说明其各自的特点和功能北京协和医学院2013考博免疫学真题回顾（部分）一名解: hybridoma homing receptor surogate light chain MAC Antibody idotype 比如补体精典与旁路途径共用的分子，BCR与TCR发育区别，HLA-DM分子参与MHC2类分子组装与转运，重症肌无力产生针对何种物质的自身抗体，BCR由细胞表面免疫球蛋

细胞与分子免疫学名解(整理)

名词解释免疫(immunity)：是指机体识别“自己”与“非己”抗原，对自身抗原形成天然免疫耐受，对非己抗原发生排斥作用的一种生理功能。正常情况下，对机体有利；免疫功能失调时，会产生对机体有害的反应。固有免疫反应(innate immune response)：也称非特异性或获得性免疫应答，是生物体在长期种系发育和进化过程中逐渐形成的一系列防御机制。此免疫在个体出生时就具备，可对外来病原体迅速应答，产生非特异性抗感染免疫作用，同时在特异性免疫应答过程中也起作用。适应性免疫反应(adaptive immune response)：也称特异性免疫应答，是在非特异性免疫基础上建立的，该种免疫是个体在生命过程中接受抗原性异物刺激后，主动产生或接受免疫球蛋白分子后被动获得的。中枢免疫器官(central)：是免疫细胞发生、发育、分化与成熟的场所；同时对外周免疫器官的发育亦起主导作用。中枢免疫器官包括骨髓、胸腺和腔上囊（禽类）外周免疫器官(peripheral)：是成熟T、B淋巴细胞等免疫细胞定居的场所，也是产生免疫应答的部位。有淋巴结、脾及与黏膜有关的淋巴组织和皮下组织等。免疫防御(immunologic defence)：是机体排斥外来抗原性异物的一种免疫保护功能。该功能正常时，机体可抵御病原微生物及其毒性产物的感染和损害，即抗感染免疫；异常情况下，反应过高会引起超敏反应，反应过低或缺失可发生免疫缺陷。免疫自稳(immunologic homeostasis)：是机体免疫系统维持内环境稳定的一种生理功能。该功能正常时，机体可及时清除体内损伤、衰老、变性的细胞和免疫复合物等异物，而对自身成分保持免疫耐受；该功能失调时，可发生生理功能紊乱或自身免疫性疾病。免疫监视(immunologic surveillance)：是机体免疫系统及时识别、清除体内突变、畸变细胞和病毒感染细胞的一种生理功能。该功能失调时，有可能导致肿瘤发生，或因病毒不能清除而出现持续感染。 MALT(mucosal-associated lymphoid tissue)：即黏膜伴随的淋巴组织。是指分布在呼吸道、肠道及泌尿生殖道的粘膜上皮细胞下的无包膜的淋巴组织。除执行固有免疫外，还可执行局部特异性免疫。抗原（antigen，Ag）是一类能刺激机体免疫系统使之产生特异性免疫应答、并能与相应免疫应答产物（抗体和致敏淋巴细胞）在体内外发生特异性结合的物质。抗原的前一种性能称为免疫原性即抗原能刺激特异性免疫细胞，使之活化、增生、分化，最终产生免疫效应物质的特性；后一种性能称为抗原性(又称：免疫反应性），即抗原可在体内外与相应的免疫效应物质发生特异性结合的特性抗原决定簇(antigenic determinant) 指抗原分子中决定抗原特异性的特殊化学基团,又称表位（epitope)，是被免疫细胞识别的靶结构，也是免疫反应具有特异性的物质基础 T细胞决定簇：T细胞决定簇一般位于抗原分子内部，必须由APC将抗原加工处理为小分子多肽并与MHC分子结合，然后才能被TCR所识别。又称线性决定簇。 B细胞决定簇：BCR能与未经APC加工的抗原发生反应，其识别的靶结构主要位于抗原分子表面的决定簇。又称构象决定簇（有三维结构）。免疫佐剂（adjuvant）:某些物质若先于抗原或与抗原一起注入机体，可增强机体对该抗原的特异性免疫应答或改变免疫应答类型，此物质称免疫佐剂超抗原(superantigen , SAg):某些微量的抗原物质具有强大的刺激T细胞活化的能力，称此物质为超抗原。常见于某些细菌外毒素、逆转录病毒（HIV）（是指在极低浓度下即可非特异性激活大量T

免疫学基础课程作业第一套

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