Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.     

烟曲霉抗原Asp f16HLA-A*0201限制性的CD8+CTL抗原表位生物信息学预测与实验室鉴定

目的 预测与鉴定烟曲霉抗原Asp f16的HLA-A *0201限制性CD8+细胞毒性T细胞(CTL)抗原表位.方法 以国人常见的HLA-A*0201位点为靶点,依据生物信息学软件扫描烟曲霉特异性抗原Asp f16的全部427个氨基酸序列.使用HLA-A *0201转基因小鼠制备骨髓来源的树突状细胞(DC)和CTL.流式细胞仪技术检测DC表面MHC Ⅱ类抗原,CD80,CD86和CD11c的表达来验证其是否成熟.ELISPOT试验检测烟曲霉抗原多肽特异性CTL产生的细胞因子IFN-γ.四聚体(Tetramer)试验证实烟曲霉特异性CTL与抗原肽,HLA-A*0201分子复合体的亲和性.结果 根据与MHC I类分子结合的半衰期评分,选择了3个HLA-A*0201限制性抗原表位.流式细胞仪分析示成熟DC高表达HLA Ⅱ类抗原,CD80,CD86和CD11c.Tetramer试验证实烟曲霉特异性T细胞受体与抗原肽,HLA-A*0201分子复合体的高亲和性.ELISPOT实验结果 表明烟曲霉抗原肽体外可以活化CD8+CTL,被负载了抗原肽的DC刺激活化后可以产生IFN-γ.结论 本研究成功鉴定烟曲霉抗原Asp f16的HLA-A*0201限制性CD8+CTL表位,可作为疫苗设计的候选表位,为进一步研发新型抗烟曲霉疫苗提供参考.     

Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile

Knowledge of structural class plays an important role in understanding protein folding patterns. In this study, a simple and powerful computational method, which combines support vector machine with PSI-BLAST profile, is proposed to predict protein structural class for low-similarity sequences. The evolution information encoding in the PSI-BLAST profiles is converted into a series of fixed-length feature vectors by extracting amino acid composition and dipeptide composition from the profiles. The resulting vectors are then fed to a support vector machine classifier for the prediction of protein structural class. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence similarity lower than 40% and 25%, respectively. The overall accuracies attain 70.7% and 72.9% for 1189 and 25PDB datasets, respectively. Comparison of our results with other methods shows that our method is very promising to predict protein structural class particularly for low-similarity datasets and may at least play an important complementary role to existing methods.     

Prediction of MHC class II binding peptides based on an iterative learning model

Background

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

Results

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE.

Conclusion

The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem.     

Prediction and characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitidis serogroup B

Neisseria meningitidis serogroup B (MC58) is a leading cause of meningitis and septicaemia, principally infects the infants and adolescents. No vaccine is available for the prevention of these infections because the serogroup B capsular polysaccharide is unable to stimulate an immune response, due to its similarity with polysialic acid. To overcome these obstacles, we proposed to develop a peptide based epitope vaccine from outer membrane protein contained in outer membrane vesicles (OMV) based on our computational analysis. In OMV a total of 236 proteins were identified, only 15 (6.4%) of which were predicted to be located in outer membrane. The major requirement is the identification and selection of T-cell epitopes that act as a vaccine target. We have selected 13 out of 15 outer membrane proteins from OMV proteins. Due to similarity of the fkpA and omp85 with the human FKBP2 and SAMM50 protein, we removed these two sequences from the analysis as their presence in the vaccine is likely to elicit an autoimmune response. ProPred and ProPred1 were used to predict promiscuous helper T Lymphocytes (HTL) and cytotoxic T Lymphocytes (CTL) epitopes and MHCPred for their binding affinity in N. meningitidis serogroup B (MC58), respectively. Binding peptides (epitopes) are distinguished from nonbinding peptides in properties such as amino acid preference on the basis of amino acid composition. By using this dataset, we compared physico-chemical and structural properties at amino acid level through amino acid composition, computed from ProtParam server. Results indicate that porA, porB, opc, rmpM, mtrE and nspA are more suitable vaccine candidates. The predicted peptides are expected to be useful in the design of multi-epitope vaccines without compromising the human population coverage.     

Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: Implications for vaccine design

T cell recognition of the peptide–MHC complex initiates a cascade of immunological events necessary for immune responses. Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms based on sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex (MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aim of obtaining a better differentiation between binding and nonbinding sequence. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. We used the Miyazawa & Jernigan and Betancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria (Nearest atom ≫ 4.0 Å & C-beta ≫ 7.0 Å) for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highly ranked. The predictions from threading improved when used multiple templates and average scoring scheme. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein. The proposed scheme may significantly reduce the number of peptides to be tested in wet laboratory for epitope based vaccine design.     

Identification of seventeen new simian immunodeficiency virus-derived CD8+ T cell epitopes restricted by the high frequency molecule, Mamu-A*02, and potential escape from CTL recognition

MHC class I-restricted CD8+ T cells play an important role in controlling HIV and SIV replication. In SIV-infected Indian rhesus macaques (Macaca mulatta), comprehensive CD8+ T cell epitope identification has only been undertaken for two alleles, Mamu-A*01 and Mamu-B*17. As a result, these two molecules account for virtually all known MHC class I-restricted SIV-derived CD8+ T cell epitopes. SIV pathogenesis research and vaccine testing have intensified the demand for epitopes restricted by additional MHC class I alleles due to the shortage of Mamu-A*01+ animals. Mamu-A*02 is a high frequency allele present in over 20% of macaques. In this study, we characterized the peptide binding of Mamu-A*02 using a panel of single amino acid substitution analogues and a library of 497 unrelated peptides. Of 230 SIVmac239 peptides that fit the Mamu-A*02 peptide-binding motif, 75 peptides bound Mamu-A*02 with IC50 values of < or = 500 nM. We assessed the antigenicity of these 75 peptides using an IFN-gamma ELISPOT assay with freshly isolated PBMC from eight Mamu-A*02+ SIV-infected macaques and identified 17 new epitopes for Mamu-A*02. The synthesis of five Mamu-A*02 tetramers demonstrated the discrepancy between tetramer binding and IFN-gamma secretion by SIV-specific CD8+ T cells during chronic SIV infection. Bulk sequencing determined that 2 of the 17 epitopes accumulated amino acid replacements in SIV-infected macaques by the chronic phase of infection, suggestive of CD8+ T cell escape in vivo. This work enhances the use of the SIV-infected macaque model for HIV and increases our understanding of the breadth of CD8+ T cell responses in SIV infection.     

Different Strategies Adopted by Kb and Ld to Generate T Cell Specificity Directed against Their Respective Bound Peptides

Mouse T cell clone 2C recognizes two different major histocompatibility (MHC) ligands, the self MHC K^b and the allogeneic MHC L^d. Two distinct peptides, SIY (SIYRYYGL) and QL9 (QLSPFPFDL), act as strong and specific agonists when bound to K^b and L^d, respectively. To explore further the mechanisms involved in peptide potency and specificity, here we examined a collection of single amino acid peptide variants of SIY and QL9 for 1) T cell activity, 2) binding to their respective MHC, and 3) binding to the 2C T cell receptor (TCR) and high affinity TCR mutants. Characterization of SIY binding to MHC K^b revealed significant effects of three SIY residues that were clearly embedded within the K^b molecule. In contrast, QL9 binding to MHC L^d was influenced by the majority of peptide side chains, distributed across the entire length of the peptide. Binding of the SIY-K^b complex to the TCR involved three SIY residues that were pointed toward the TCR, whereas again the majority of QL9 residues influenced binding of TCRs, and thus the QL9 residues had impacts on both L^d and TCR binding. In general, the magnitude of T cell activity mediated by a peptide variant was influenced more by peptide binding to MHC than by binding the TCR, especially for higher affinity TCRs. Findings with both systems, but QL9-L^d in particular, suggest that many single-residue substitutions, introduced into peptides to improve their binding to MHC and thus their vaccine potential, could impair T cell reactivity due to their dual impact on TCR binding.     

IMPARO: inferring microbial interactions through parameter optimisation

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route. Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis. We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors. Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.     

T-cell epitope prediction with combinatorial peptide libraries.

T cell receptors (TCR) recognize antigenic peptides in complex with the major histocompatibility complex (MHC) molecules and this trimolecular interaction initiates antigen-specific signaling pathways in the responding T lymphocytes. For the study of autoimmune diseases and vaccine development, it is important to identify peptides (epitopes) that can stimulate a given TCR. The use of combinatorial peptide libraries has recently been introduced as a powerful tool for this purpose. A combinatorial library of n-mer peptides is a set of complex mixtures each characterized by one position fixed to be a specified amino acid and all other positions randomized. A given TCR can be fingerprinted by screening a variety of combinatorial libraries using a proliferation assay. Here, we present statistical models for elucidating the recognition profile of a TCR using combinatorial library proliferation assay data and known MHC binding data.     

Structural and immunological reactivity of the principal neutralizing determinant V3 of glycoprotein gp 120 of HIV-1

The variable domain V3 in the outer glycoprotein gp 120 of HIV-1 is a highly important region with respect to immune response during the course of viral infection. Neutralizing antibodies are produced against this domain; in addition, it has been shown to be a functionally active epitope for T helper and cytotoxic T cells. The high degree of amino acid variability in individual HIV-isolates, however, limits the use of the V3-domain in approaches to vaccine development. In order to characterize the residues important for antibody interaction and binding to MHC class I proteins, we constructed a consensus sequence of the V3-domain with broad reactivity [1] and used synthetic peptides derived from this consensus with individual residues altered to alanine. These peptides were used as antigens in ELISA tests to define the amino acids which are important for binding to human and rabbit/anti-peptide immunoglobulins. In addition, we used these alanine-derived peptides in interaction studies with human HLA-A2.1 and mouse H-2D^d by testing their capacity to stabilize the respective MHC class I protein complexes on the surface of mutant cell lines T2 and RMA-S transfected with D^d gene. The experimental tests allowed us to define individual residues involved in antibody and MHC-protein interaction, respectively. In a further approach, we used those results to design interaction models with HLA-A2.1 and H-2D^d. Therefore, a structural model for H-2D^d was built that exhibits an overall similar conformation to the parental crystal structure of HLA-A2.1. The resulting interaction models show V3-peptide bound in an extended β-conformation with a bulge in its centre for both H-2D^d and HLA-A2.1 complexes. The N- and C-termini of V3 peptide reside in conserved pockets within both MHC-proteins. Anchoring residues could be determined that are crucial for the binding of the respective MHC class I haplotype. The cross-reactivity of V3-peptide in enhancing the expression of two different MHC class I molecules (H-2D^d and HLA-A2.1) is shown to be based on similar peptide binding that induces an almost identical peptide conformation.     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

Peptide binding to HLA-DR1: a peptide with most residues substituted to alanine retains MHC binding

Major histocompatibility complex (MHC) glycoproteins play an important role in the development of an effective immune response. An important MHC function is the ability to bind and present 'processed antigens' (peptides) to T cells. We show here that the purified human class II MHC molecule, HLA-DR1, binds peptides that have been shown to be immunogenic in vivo. Detergent-solubilized HLA-DR1 and a papain-cleaved form of the protein lacking the transmembrane and intracellular regions have similar peptide binding properties. A total of 39 single substitutions were made throughout an HLA-DR1 restricted hemagglutinin epitope and the results determine one amino acid in this peptide which is crucial to binding. Based on this analysis, a synthetic peptide was designed containing two residues from the original hemagglutinin epitope embedded in a chain of polyalanine. This peptide binds to HLA-DR1, indicating that the majority of peptide side chains are not required for high affinity peptide binding.     

Analysis of peptide residues interacting with MHC molecule or T cell receptor. Can a peptide bind in more than one way to the same MHC molecule?

It has generally been assumed implicitly that one can define amino acid residues of a T cell antigenic determinant peptide that interact with the MHC molecule, i.e., residues that form the "agretope." However, if the same peptide can be seen in different conformations or orientations in the same MHC molecule by different T cells, then we would predict that some residues would appear to interact with the TCR of one T cell clone but with the MHC molecule as the peptide is seen by another T cell clone. To test this hypothesis, we synthesized 36 analogue peptides of an immunogenic fragment (P133-146) of sperm whale myoglobin with three different substitutions for each of 12 amino-acid residues and analyzed the role of each residue for I-Ed-binding and for activation of two Th clones, 14.1 and 14.5, specific for the peptide. The two T cell clones showed slightly different fine specificity from each other in that the truncated peptide P136-144 could stimulate 14.5 but not 14.1. The binding activity of nonstimulatory analogues to the I-Ed molecule was measured by functional inhibition analyses using truncated wild-type peptides as stimulators and nonstimulatory analogues as inhibitors. Paradoxical results were obtained that could not be explained by the peptide binding in a single way to the same I-Ed molecule. Some residues appeared to reciprocally reverse their roles for binding to I-Ed vs binding to the TCR when assessed using T-cell clone 14.5 compared to clone 14.1. These results fit the prediction of the above hypothesis and indicate the possibility that the same peptide, P133-146, can bind in more than one way to the same Ia molecule. The T cell clones, 14.1 and 14.5, appear to recognize different P133-146-I-Ed complexes in which the peptide is bound differently. Moreover, a given residue may not have a unique function of always interacting with the MHC molecule or TCR, but may change from one role to the other as it is presented to different T cells.     

POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties

MOTIVATION: Both modeling of antigen-processing pathway including major histocompatibility complex (MHC) binding and immunogenicity prediction of those MHC-binding peptides are essential to develop a computer-aided system of peptide-based vaccine design that is one goal of immunoinformatics. Numerous studies have dealt with modeling the immunogenic pathway but not the intractable problem of immunogenicity prediction due to complex effects of many intrinsic and extrinsic factors. Moderate affinity of the MHC-peptide complex is essential to induce immune responses, but the relationship between the affinity and peptide immunogenicity is too weak to use for predicting immunogenicity. This study focuses on mining informative physicochemical properties from known experimental immunogenicity data to understand immune responses and predict immunogenicity of MHC-binding peptides accurately. RESULTS: This study proposes a computational method to mine a feature set of informative physicochemical properties from MHC class I binding peptides to design a support vector machine (SVM) based system (named POPI) for the prediction of peptide immunogenicity. High performance of POPI arises mainly from an inheritable bi-objective genetic algorithm, which aims to automatically determine the best number m out of 531 physicochemical properties, identify these m properties and tune SVM parameters simultaneously. The dataset consisting of 428 human MHC class I binding peptides belonging to four classes of immunogenicity was established from MHCPEP, a database of MHC-binding peptides (Brusic et al., 1998). POPI, utilizing the m = 23 selected properties, performs well with the accuracy of 64.72% using leave-one-out cross-validation, compared with two sequence alignment-based prediction methods ALIGN (54.91%) and PSI-BLAST (53.23%). POPI is the first computational system for prediction of peptide immunogenicity based on physicochemical properties. AVAILABILITY: A web server for prediction of peptide immunogenicity (POPI) and the used dataset of MHC class I binding peptides (PEPMHCI) are available at http://iclab.life.nctu.edu.tw/POPI     

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.     

Functional mapping of MHC class II polymorphic residues. The alpha-chain controls the specificity for binding an Ad-versus an A k-restricted peptide and the beta-chain region 65-67 controls T cell recognition but not peptide binding.

MHC proteins are polymorphic cell surface glycoproteins involved in the binding of peptide Ag and their presentation to T lymphocytes. The polymorphic amino acids of MHC proteins are primarily located in the N-terminal domains and are thought to influence T cell recognition both by influencing the binding of peptide Ag and by direct contact with the T cell receptor. In order to determine the relative importance of individual polymorphic amino acids in Ag presentation, a number of groups have taken the approach of interchanging polymorphic amino acids between different alleles of MHC protein in an attempt to define which of the polymorphisms influence peptide binding and which influence T cell recognition by direct contact with the TCR. The peptide OVA323-339 has been previously shown to bind to the MHC class II protein Ad and to have a much lower affinity for Ak, whereas the peptide hen egg lysozyme 46-61 binds well to Ak and poorly to Ad. In the present report, we have analyzed the ability of purified wild-type MHC class II proteins as well as the ability of three different hybrid molecules between Ad and Ak to bind and present these peptides. We find that the alpha-chain of the MHC class II protein plays a critical role in the binding of HEL46-61 and confers the specificity for binding OVA323-339, regardless of which beta-chain is present. We also find that the beta-chain region 65-67 does not control the specificity of peptide binding to the MHC protein, but is important in T cell responses to preformed MHC-peptide complexes, suggesting a role for this region in contacting the TCR.     

Identification of residues necessary for clonally specific recognition of a cytotoxic T cell determinant.

The residues in an influenza nucleoprotein (NP) cytotoxic T cell determinant necessary for cytotoxic T cell (CTL) recognition, were identified by assaying the ability of hybrid peptides to sensitize a target cell to lysis. The hybrid peptides were formed by substituting amino acids from one determinant (influenza NP 147-158) for the corresponding residues of a second peptide (HLA CW3 171-182) capable of binding to a common class I protein (H-2Kd). Six amino acids resulted in partial recognition; however, the presence of a seventh improved the potency of the peptide. Five of the six amino acids were shown to be required for recognition. The spacing of the six amino acids was consistent with the peptide adopting a helical conformation when bound. The importance of each amino acid in CTL recognition and binding to the restriction element was investigated further by assaying the ability of peptides containing point substitutions either to sensitize target cells or to compete with the natural NP sequence for recognition by CTL. The T cell response was much more sensitive to substitution than the ability of the peptide to bind the restriction element. Collectively the separate strategies identified an approximate conformation and orientation of the peptide when part of the complex and permitted a potential location in the MHC binding site to be identified. The model provides a rationalization for analogues which have previously been shown to exhibit greater affinity for the class I molecule and suggests that the binding site in major histocompatibility complex (MHC) class I molecules might have greater steric constraints that the corresponding area of class II proteins.