Prediction Of Peptide Binding Research Articles

Bioinformatic and Empirical Analysis of Novel Hypoxia-Inducible Targets of the Human Antituberculosis T Cell Response

We analyzed whole genome-based transcriptional profiles of Mycobacterium tuberculosis subjected to prolonged hypoxia to guide the discovery of novel potential Ags, by a combined bioinformatic and empirical approach. We analyzed the fold induction of the 100 most highly induced genes at 7 d of hypoxia, as well as transcript abundance, peptide-binding prediction (ProPred) adjusted for population-specific MHC class II allele frequency, and by literature search. Twenty-six candidate genes were selected by this bioinformatic approach and evaluated empirically using IFN-γ and IL-2 ELISPOT using immunodominant Ags (Acr-1, CFP-10, ESAT-6) as references. Twenty-three of twenty-six proteins induced an IFN-γ response in PBMCs of persons with active or latent tuberculosis. Five novel immunodominant proteins-Rv1957, Rv1954c, Rv1955, Rv2022c, and Rv1471-were identified that induced responses similar to CFP-10 and ESAT-6 in both magnitude and frequency. IL-2 responses were of lower magnitude than were those of IFN-γ. Only moderate evidence of infection stage-specific recognition of Ags was observed. Reconciliation of bioinformatic and empirical hierarchies of immunodominance revealed that Ags could be predicted, providing transcriptomic data were combined with peptide-binding prediction adjusted by population-specific MHC class II allele frequency.

지식기반 유전자알고리즘을 이용한 한국인 빈발 HLA 대립유전자에 대한 결합 펩타이드 예측

감염된 미생물에서 유래한 단백질 펩타이드가 HLA에 결합하여 숙주의 세포표면에 제시되면, T 세포가 이를 인식하여 면역반응을 유발함으로써 감염원을 제거하게 된다. HLA와 펩타이드간의 결합이 안정적일수록 T 세포반응이 강하게 일어나 효율적으로 감염원을 제거할 수 있다고 알려져 있다. 따라서 특정 HLA에 안정적으로 결합할 수 있는 펩타이드(HLA binder)를 찾아낼 수 있다면 감염질환이나 암의 예방을 위한 펩타이드 백신의 개발에 활용될 수 있다. 그런데 HLA는 매우 다형하기 때문에 하나의 집단 내에서도 어느 정도의 빈도를 가지는 대립유전자의 수가 매우 많다. 따라서 이들 모든 대립유전자들에 대해 가능한 펩타이드조합을 제작한 후 직접 실험을 통해 안정적으로 결합하는 펩타이드를 찾아내는 것은 매우 비효율적이다. 이를 극복하기 위하여 특정 HLA에 안정적으로 결합하는 펩타이드를 예측하는 정보전산적인 방법이 최근 개발되어 왔다. 이들 방법을 통해 제시된 펩타이드에 대해서만 직접 생물학적 실험을 시행함으로써 연구자는 검증해야 할 후보 펩타이드의 수를 현격히 감소시킬 수 있게 된다. 본 논문에서는 HLA 결합 펩타이드 예측을 위해 기계학습을 이용한 방법을 소개할 뿐만 아니라, 지금까지 HLA 결합 펩타이드 예측에 시도된 적이 없는 '지식기반 유전자 알고리즘(knowledge-based genetic algorithm)'이라는 새로운 모델을 제시하고자 한다. 이것은 유전자알고리즘(GA)에 기반한 것이었지만 전문가 지식을 접목함으로써 GA보다 더 향상된 성능으로 한국인에 흔한 HLA에 결합하는 펩타이드를 예측하였다. 뿐만 아니라 이것은 결합하는 펩타이드의 규칙을 한국인에 흔한 HLA 대립유전자에 대하여 추출해 줄 수 있는 새로운 방법이었다. T cells induce immune responses and thereby eliminate infected micro-organisms when peptides from the microbial proteins are bound to HLAs in the host cell surfaces, It is known that the more stable the binding of peptide to HLA is, the stronger the T cell response gets to remove more effectively the source of infection. Accordingly, if peptides (HLA binder) which can be bound stably to a certain HLA are found, those peptieds are utilized to the development of peptide vaccine to prevent infectious diseases or even to cancer. However, HLA is highly polymorphic so that HLA has a large number of alleles with some frequencies even in one population. Therefore, it is very inefficient to find the peptides stably bound to a number of HLAs by testing random possible peptides for all the various alleles frequent in the population. In order to solve this problem, computational methods have recently been developed to predict peptides which are stably bound to a certain HLA. These methods could markedly decrease the number of candidate peptides to be examined by biological experiments. Accordingly, this paper not only introduces a method of machine learning to predict peptides binding to an HLA, but also suggests a new prediction model so called 'knowledge-based genetic algorithm' that has never been tried for HLA binding peptide prediction. Although based on genetic algorithm (GA). it showed more enhanced performance than GA by incorporating expert knowledge in the process of the algorithm. Furthermore, it could extract rules predicting the binding peptide of the HLA alleles common in Koreans.

Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0: a quantitative molecular docking study

BackgroundHLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 – 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0.ResultsThe X-ray structure of the peptide – HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides.ConclusionsThe higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins.

Computational prediction and experimental assessment of an HLA-A*0201-restricted cytotoxic T lymphocyte epitope from neutral endopeptidase

Neutral endopeptidase (NEP) is the first target antigen identified on podocytes in human membranous nephropathy (MN). Cytotoxic T lymphocytes (CTLs) are considered essential for glomerular destruction in MN model. The aim of this study was to show that the CTL epitopes of NEP could be used to design more effective and better tolerated therapies. The CTL epitopes of NEP were screened using the long-distance prediction system SYFPEITHI and the Bioinformatics and Molecular Analysis Section of the MHC Peptide Binding Predictions program. Peptides were synthesized and immunoreactivity was assessed by peptide-MHC-binding affinity assay, cytotoxicity assay and HLA-A2.1/Kb transgenic mice immunization. Five candidates were identified according to the high scores generated by the computer predicting system. Peptide NEP(375-383) (FIMDLVSSL), which up-regulated HLA-A2.1 molecular expression, showed a high affinity to HLA-A2.1, whereas NEP(268-276), NEP(297--305) and NEP(492-500) (QLALEMNKV, MLLYNKMRL and KLNNEYLEL) showed a moderate affinity and NEP(559-567) (ILQPPFFSA) only had a low affinity. Cytotoxicity assay further showed that NEP(268-276) and NEP(375-383) could induce NEP-specific CTL responses in vitro. Unexpectedly, we found that a single CTL epitope, NEP(375-383), could induce proteinuria and glomerular injury in HLA-A2.1/K(b) transgenic mice in vivo. HLA-A*0201-restricted CTL epitope NEP(375-383) can serve as a potential candidate for designing MN vaccine.

TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules

MotivationAccurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.MethodWe have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.ResultThe performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.

Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Neurotoxin of Mesobuthustamulus

Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Neurotoxin of Mesobuthustamulus

DRB1*12:01 presents a unique subset of epitopes by preferring aromatics in pocket 9

This study characterized the unique peptide-binding characteristics of HLA-DRB1*12:01 (DR1201), an allele studied in the context of various autoimmune diseases, using a peptide competition assay and structural modeling. After defining Influenza A/Puerto Rico/8/34 Matrix Protein M1 (H1MP) 40–54 as a DR1201 restricted epitope, the critical anchor residues within this sequence were confirmed by measuring the relative binding of peptides with non-conservative substitutions in competition with biotin labeled H1MP40–54 peptide. Based on this information, a set of peptides was designed with single amino acid substitutions at these anchor positions. The overall peptide binding preferences for the DR1201 allele were deduced by incubating these peptides in competition with the reference H1MP40–54 to determine the relative binding affinities of each to recombinant DR1201 protein. As expected, pocket 1 preferred methionine and aliphatic residues, and tolerated phenylalanine. Pocket 4 was mostly composed of hydrophobic residues, thereby preferentially accommodating aliphatic residues, but could also weakly accommodate lysine due to its slightly acidic environment. Pocket 6 accepted a wide range of amino acids because of the diverse residues that comprise this pocket. Pocket 9 accepted aliphatic and negatively charged amino acids, but showed a remarkable preference for aromatic residues due to the conformation of the pocket, which lacks the typical salt bridge between β57Asp and α76Arg. These binding characteristics contrast with the closely related DR1104 allele, distinguishing DR1201 among the alleles of the HLA-DR5 group. These empirical results were used to develop an algorithm to predict peptide binding to DR1201. This algorithm was used to verify T cell epitopes within novel antigenic peptides identified by tetramer staining and within peptides from published reports that contain putative DR1201 epitopes.

Identification of MHC Class II Binders/ Non-binders using Negative Selection Algorithm

AbstractThe identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine design. These MHC class–II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptides is very helpful towards epitope-based vaccine design. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class–II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class–II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, and 0.69, and 0.84 respectively indicating good predictive performance for the small training set.

Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.

Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach

BackgroundMHC class II proteins bind oligopeptide fragments derived from proteolysis of pathogen antigens, presenting them at the cell surface for recognition by CD4+ T cells. Human MHC class II alleles are grouped into three loci: HLA-DP, HLA-DQ and HLA-DR. In contrast to HLA-DR and HLA-DQ, HLA-DP proteins have not been studied extensively, as they have been viewed as less important in immune responses than DRs and DQs. However, it is now known that HLA-DP alleles are associated with many autoimmune diseases. Quite recently, the X-ray structure of the HLA-DP2 molecule (DPA*0103, DPB1*0201) in complex with a self-peptide derived from the HLA-DR α-chain has been determined. In the present study, we applied a validated molecular docking protocol to a library of 247 modelled peptide-DP2 complexes, seeking to assess the contribution made by each of the 20 naturally occurred amino acids at each of the nine binding core peptide positions and the four flanking residues (two on both sides).ResultsThe free binding energies (FBEs) derived from the docking experiments were normalized on a position-dependent (npp) and on an overall basis (nap), and two docking score-based quantitative matrices (DS-QMs) were derived: QMnpp and QMnap. They reveal the amino acid preferences at each of the 13 positions considered in the study. Apart from the leading role of anchor positions p1 and p6, the binding to HLA-DP2 depends on the preferences at p2. No effect of the flanking residues was found on the peptide binding predictions to DP2, although all four of them show strong preferences for particular amino acids. The predictive ability of the DS-QMs was tested using a set of 457 known binders to HLA-DP2, originating from 24 proteins. The sensitivities of the predictions at five different thresholds (5%, 10%, 15%, 20% and 25%) were calculated and compared to the predictions made by the NetMHCII and IEDB servers. Analysis of the DS-QMs indicated an improvement in performance. Additionally, DS-QMs identified the binding cores of several known DP2 binders.ConclusionsThe molecular docking protocol, as applied to a combinatorial library of peptides, models the peptide-HLA-DP2 protein interaction effectively, generating reliable predictions in a quantitative assessment. The method is structure-based and does not require extensive experimental sequence-based data. Thus, it is universal and can be applied to model any peptide - protein interaction.

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptide-repertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations.

Comparative analysis of epitope predictions: proposed library of putative vaccine candidates for HIV

Designing a vaccine for a disease is one of the crucial tasks that involve millions and billions of dollars, several decades and yet there is no guarantee of successful results. Several pharmaceutical companies are investing their money and time in such activities. Computational biology could be of great help in these activities by proving a library of plausible candidates that might actually show some positive responses. MHC binding peptide prediction is one such area where the immense power of computers could be used to get a breakthrough. In this direction several databases and servers have been developed by many labs to predict the MHC binding peptides. These short peptides on the antigen surface are recognized by the MHC molecule and are presented to the receptors of T-cells for further immune response. Peptides that bind to a given MHC molecule share sequence similarity. Here we present a comparative study of servers that can predict the MHC binding peptides in a given protein sequence of the antigen. Based on this comparative analysis on HIV data, we are able to propose a library of putative vaccine candidates for the env GP-160 protein of HIV-1.

Bioinformatics-Based Predictions of Peptide Binding to Disease-Associated HLA Proteins Suggest Explanation for Shared Autoimmunity

Aim This study was designed to examine the immunogenetic basis for shared autoimmunity, resulting in autoantigen presentation that leads to the production of two or more disease-specific autoantibodies. Methods A bioinformatics approach based on peptide binding predictions to disease-associated HLA determinants has been developed and tested here using 11 disease associations between autoimmune systemic and mucocutaneous blistering disorders. Various HLAs associated with antigens within a given “disease model” (set of HLA class II and protein sequences known to be associated with a specific autoimmune disease) were tested and ranked against the antigenic proteins, first with proteins they are known to associate with and then with proteins known to be implicated in a second disease model. In every case binding predictions were compared for different proteins binding to the same HLA. Subsequently, disease-related autoantigens have been tested for their binding affinity against each disease-specific HLA class II protein. Results For a single HLA haplotype, several binders have been generated from a related autoantigen with the variable binding score. In most cases, the binding score corresponding to the interactions between the autoantigen-derived epitope and the HLA associated with one disease was similar or lower than the interactions between the epitope from proteins associated with the second disease and the same HLA. Notably, there was no compelling promiscuity in peptide binding to each of the HLA molecules, in spite of the promiscuous nature of HLA class II binding. Conclusions The data suggest that, in susceptible individuals, shared autoimmunity might be initiated by two types of HLA/peptide interaction; first between an autoantigen-derived epitope and its disease-associated HLA molecules, and second, between a different peptide of the same autoantigen and HLA proteins specific for the second disease.

MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration – referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/.

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

BackgroundThe binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.ResultsWe fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes.ConclusionsThe MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

Identification of CD4+ and CD8+ T cell epitopes of woodchuck hepatitis virus core and surface antigens in BALB/c mice

A therapeutic vaccine against chronic hepatitis B virus (HBV) infection requires the development of a strong and multispecific Th1 cell immune response. Woodchucks chronically infected with the woodchuck hepatitis virus (WHV) closely resemble HBV infection and represent the best animal model for this hepadnavirus-induced disease. Using the BIMAS “HLA Peptide Binding Predictions” program, we have identified and further characterized novel H-2d-restricted CD8+ epitopes within the WHV core (peptides C#12–21, C#18–32, C#19–27, C#61–69) and surface antigens (peptides preS2#10–18, preS2#27–35, S#76–84, S#133–140 and S#257–265), respectively. These peptides bind to H-2d with high efficiency and upon immunization of mice with peptide and Freund's adjuvant they induce the development of IFN-γ producing T cells. More importantly, WHV core peptides C#19–27 and C#61–69 and WHV surface peptides S#133–140 and S#257–265 were also recognized by CD8+ T cells after immunization of mice with DNA/PEI nanoparticles. Direct stimulation of splenocytes obtained from such DNA-immunized mice with peptides C#18–32, S#76–84, and S#257–265 resulted in significant production of IFN-γ. Thus, we have identified T cell determinants in mice from WHV core and surface antigens that have important value for designing and evaluating an effective vaccine against hepadnavirus infection.

MetaMHC: a meta approach to predict peptides binding to MHC molecules

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html.

A novel locally linear embedding and wavelet transform based encoding method for prediction of MHC-II binding affinity

The binding between peptides and MHC molecules is an important event to the cellular immunity against pathogens. The binding peptides are recognized as the epitopes, which are useful for the epitope-based vaccine design. Accurate prediction of the MHC-II binding peptides has long been a challenge in bioinformatics. Recently, most researchers are interested in predicting the binding affinity instead of categorizing peptides as "binders" or "non-binders". In this paper, we introduced a novel encoding scheme based on Locally Linear Embedding (LLE) and Wavelet Transform (WT), in which important amino acid properties were firstly selected from all properties (described in AAindex database) by using LLE, and then amino acids of peptides were replaced with these novel properties. Further, WT was adopted to extract the frequency attributes of the numerical sequences; thereby the peptides were transformed into homogeneous-length vectors. Finally, Support Vector machine Regression (SVR) was used to make quantitative prediction models based on these numerical vectors. When applied to the 16 datasets from IEDB database, our encoding scheme produced consistently better performance than other encoding schemes, indicating that our encoding scheme is an effective tool for the prediction of MHC-II binding affinity.

Abstract 4783: Identification of a novel TAA, SPARC, as a target for immunotherapy of various cancers

Abstract It is important to identify Tumor-associated antigens (TAAs) to direct the immune system to attack cancer in order to establish efficient anti-cancer-immunotherapy. We analyzed the gene expression profiles of gastric cancers by using the genome wide cDNA microarray consisting of probes for 20,340 genes, and found that SPARC (Secreted Protein Acidic and Rich in Cysteine) gene was overexpressed in diffuse-type gastric cancer tissues at higher levels in comparison to adjacent normal gastric tissues. The levels of expression of SPARC in pancreatic cancer and colorectal cancer tissues were also higher than those of normal counterparts. On the other hand, the expression of SPARC among the normal tissue specimens was detected only in limited tissue specimens at very low levels. Therefore, we evaluated the possibility of SPARC as a target antigen for anticancer immunotherapy. This study attempted to identify HLA-A24 (A*2402)-restricted and SPARC derived CTL epitopes. To identify the SPARC-derived and HLA-A24-restricted CTL epitopes, we selected a total of 4 different candidate 9- or 10- amino acid peptides that were expected to have a higher binding affinity to HLA-A24 (A*2402) by the HLA Peptide Binding Predictions in the BIMAS software program. The H-2 Kd-binding peptide motif is comparable to that of HLA-A24 binding peptide. Thus, we examined CTL response of BALB/c mice to these SPARC-derived epitopes. We found that two SPARC derived peptides elicited high magnitude of CTL response in BALB/c mice in an H-2Kd-restricted manner. Therefore, we attempted to generate SPARC-specific CTLs from the PBMCs of healthy donors and various cancer patients positive for HLA-A24 by stimulation of PBMCs with these two peptides. SPARC peptides-reactive CTLs were successfully induced from PBMCs by in vitro stimulation with these two peptides in HLA-A24 (A*2402) positive healthy donors and cancer patients. Moreover the SPARC-reactive CTLs established from PBMCs derived from cancer patients by stimulation with these two peptides, exhibited cytotoxicity to the cancer cells expressing both HLA-A24 and endogenous or transgene-derived SPARC. On the other hand, the CTLs did not kill the cells which were negative for either HLA-A24 or SPARC. Furthermore, the adoptive transfer of the SPARC-specific CTLs could inhibit the tumor growth in NOD/SCID mice bearing human cancer cells expressing both HLA-A24 (A*2402) and SPARC. These findings suggest that SPARC is a potentially useful target candidate for cancer immunotherapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4783.