Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression

Multi-Omics of Corynebacterium Pseudotuberculosis 12CS0282 and an In Silico Reverse Vaccinology Approach Reveal Novel Vaccine and Drug Targets

Corynebacterium pseudotuberculosis is an important animal pathogen, which is also able to infect humans. An optimal treatment of infections with this pathogen is not available today and consequently, more research is necessary to understand the infection process. Here, we present a combined -omics and bioinformatics approach to characterize C. pseudotuberculosis 12CS0282. The genome sequence of strain 12CS0282 was determined, analyzed in comparison with the available 130 C. pseudotuberculosis sequences and used as a basis for proteome analyses. In a reverse vaccinology approach, putative vaccine and drug targets for 12CS0208 were identified. Mass spectrometry analyses revealed the presence of multiple virulence factors even without host contact. In macrophage interaction studies, C. pseudotuberculosis 12CS0282 was highly resistant against human phagocytes and even multiplied within human THP-1 cells. Taken together, the data indicate a high pathogenic potential of the strain.

Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches

In spite of numerous studies on vaccination for various species of Leishmania, research on the development of an effective vaccine for L. tropica is very scarce. In silico epitope prediction is a new way to survey the best vaccine candidates. Here, we predicted the best epitopes of six L. tropica antigens with vaccine capability against this pathogen, using highly frequent HLA-I alleles. Based on the frequent HLA alleles, the protein sequences were screened individually using four different MHC prediction applications, namely SYFPEITHI, ProPredI, BIMAS, and IEDB. Several in silico assays including clustering, human similarity exclusion, epitope conservancy prediction, investigating in experimental records, immunogenicity prediction, and prediction of population coverage were performed to narrow the results and to find the best epitopes. The selected epitopes and their restricted HLA-I alleles were docked and the final epitopes with the lowest binding energy (the highest binding affinity) were chosen. Finally, the stability and the binding properties of the best epitope-HLA-I combinations were analyzed using molecular dynamics simulation studies. We found ten potential peptides with strong binding affinity to highly frequent HLA-I alleles that can be further evaluated as vaccine targets against L. tropica.

Identification of HLA‑A*1101‑restricted cytotoxic T lymphocyte epitopes derived from epidermal growth factor pathway substrate number 8

Epidermal growth factor receptor pathway substrate 8 (EPS8) is critical in the proliferation, progression and metastasis of solid and hematological types of cancer, and thus constitutes an ideal target for cancer immunotherapy. The present study aimed to identify human leukocyte antigen (HLA)‑A*1101‑restricted cytotoxic T lymphocyte (CTL) epitopes from EPS8 and characterize their immunotherapeutic efficacy in vitro. Two computer‑based algorithms were used to predict native EPS8 epitopes with potential high binding affinity to the HLA‑A*1101 molecule, which is the HLA‑A allele with the highest frequency in the Chinese population. The peptide‑induced cytokine production from the CTLs was examined using enzyme‑linked immunosorbent spot analysis. The cytotoxic effects on cancer cells by CTLs primed with the identified peptides were examined using flow cytometry. A total of five peptides, designated as P380, P70, P82, P30 and P529, presented with high affinity towards the HLA‑A*1101 molecule. In response to stimulation by these five peptides, enhanced secretion of interferon‑γ from the CTLs and increased cytolytic capabilities of the CTLs toward cancer cells were noted, with the most potent effects observed from the P380 peptide. Taken together, the present study identified five potential CTL epitopes from EPS8. Among these, P380 presented with the highest therapeutic efficacy in vitro. These peptides may benefit the development of EPS8‑based immunotherapy for the treatment of HLA‑A*1101‑positive hematological malignancies.

Computational elucidation of potential antigenic CTL epitopes in Ebola virus

Cell-mediated immunity is important for the control of Ebola virus infection. We hypothesized that those HLA A0201 and HLA B40 restricted epitopes derived from Ebola virus proteins, would mount a good antigenic response. Here we employed an immunoinformatics approach to identify specific 9mer amino acid which may be capable of inducing a robust cell-mediated immune response in humans. We identified a set of 28 epitopes that had no homologs in humans. Specifically, the epitopes derived from NP, RdRp, GP and VP40 share population coverage of 93.40%, 84.15%, 74.94% and 77.12%, respectively. Based on the other HLA binding specificity and population coverage, seven novel promiscuous epitopes were identified. These 7 promiscuous epitopes from NP, RdRp and GP were found to have world-wide population coverage of more than 95% indicating their potential significance as useful candidates for vaccine design. Epitope conservancy analysis also suggested that most of the peptides are highly conserved (100%) in other virulent Ebola strain (Mayinga-76, Kikwit-95 and Makona-G3816- 2014) and can therefore be further investigated for their immunological relevance and usefulness as vaccine candidates.

Computational prediction of broadly neutralizing HIV-1 antibody epitopes from neutralization activity data

Broadly neutralizing monoclonal antibodies effective against the majority of circulating isolates of HIV-1 have been isolated from a small number of infected individuals. Definition of the conformational epitopes on the HIV spike to which these antibodies bind is of great value in defining targets for vaccine and drug design. Drawing on techniques from compressed sensing and information theory, we developed a computational methodology to predict key residues constituting the conformational epitopes on the viral spike from cross-clade neutralization activity data. Our approach does not require the availability of structural information for either the antibody or antigen. Predictions of the conformational epitopes of ten broadly neutralizing HIV-1 antibodies are shown to be in good agreement with new and existing experimental data. Our findings suggest that our approach offers a means to accelerate epitope identification for diverse pathogenic antigens.

Development of predictive quantitative structure–activity relationship model and its application in the discovery of human leukotriene A4 hydrolase inhibitors

Background: Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4. Results & discussion: The quantitative structure–activity relationship (QSAR) models were developed using genetic function approximation and validated. A training set of 26 diverse compounds and their molecular descriptors were used to develop highly correlating QSAR models. A six-descriptor model explaining the biological activity of the training and test sets with correlation values of 0.846 and 0.502, respectively, was selected as the best model and used in a database screening of drug-like Maybridge database followed by molecular docking. Conclusion: Based on the predicted potent inhibitory activities, expected binding mode and molecular interactions at the active site of hLTA4H final leads were selected as to be utilized in designing future hLTA4H inhibitors.

Steric recognition of T-cell receptor contact residues is required to map mutant epitopes by immunoinformatical programmes

MHC class I-restricted CD8 T-lymphocyte epitopes comprise anchor motifs, T-cell receptor (TCR) contact residues and the peptide backbone. Serial variant epitopes with substitution of amino acids at either anchor motifs or TCR contact residues have been synthesized for specific interferon-γ responses to clarify the TCR recognition mechanism as well as to assess the epitope prediction capacity of immunoinformatical programmes. CD8 T lymphocytes recognise the steric configuration of functional groups at the TCR contact side chain with a parallel observation that peptide backbones of various epitopes adapt to the conserved conformation upon binding to the same MHC class I molecule. Variant epitopes with amino acid substitutions at the TCR contact site are not recognised by specific CD8 T lymphocytes without compromising their binding capacity to MHC class I molecules, which demonstrates two discrete antigen presentation events for the binding of peptides to MHC class I molecules and for TCR recognition. The predicted outcome of immunoinformatical programmes is not consistent with the results of epitope identification by laboratory experiments in the absence of information on the interaction with TCR contact residues. Immunoinformatical programmes based on the binding affinity to MHC class I molecules are not sufficient for the accurate prediction of CD8 T-lymphocyte epitopes. The predictive capacity is further improved to distinguish mutant epitopes from the non-mutated epitopes if the peptide-TCR interface is integrated into the computing simulation programme.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

In silico analysis of six known Leishmania major antigens and in vitro evaluation of specific epitopes eliciting HLA-A2 restricted CD8 T cell response

Background

As a potent CD8⁺ T cell activator, peptide vaccine has found its way in vaccine development against intracellular infections and cancer, but not against leishmaniasis. The first step toward a peptide vaccine is epitope mapping of different proteins according to the most frequent HLA types in a population.

Methods and Findings

Six Leishmania (L.) major-related candidate antigens (CPB,CPC,LmsTI-1,TSA,LeIF and LPG-3) were screened for potential CD8⁺ T cell activating 9-mer epitopes presented by HLA-A*0201 (the most frequent HLA-A allele). Online software including SYFPEITHI, BIMAS, EpiJen, Rankpep, nHLApred, NetCTL and Multipred were used. Peptides were selected only if predicted by almost all programs, according to their predictive scores. Pan-A2 presentation of selected peptides was confirmed by NetMHCPan1.1. Selected peptides were pooled in four peptide groups and the immunogenicity was evaluated by in vitro stimulation and intracellular cytokine assay of PBMCs from HLA-A2⁺ individuals recovered from L. major. HLA-A2⁻ individuals recovered from L. major and HLA-A2⁺ healthy donors were included as control groups. Individual response of HLA-A2⁺ recovered volunteers as percent of CD8⁺/IFN-γ⁺ T cells after in vitro stimulation against peptide pools II and IV was notably higher than that of HLA-A2⁻ recovered individuals. Based on cutoff scores calculated from the response of HLA-A2⁻ recovered individuals, 31.6% and 13.3% of HLA-A2⁺ recovered persons responded above cutoff in pools II and IV, respectively. ELISpot and ELISA results confirmed flow cytometry analysis. The response of HLA-A2⁻ recovered individuals against peptide pools I and III was detected similar and even higher than HLA-A2⁺ recovered individuals.

Conclusion

Using in silico prediction we demonstrated specific response to LmsTI-1 (pool II) and LPG-3- (pool IV) related peptides specifically presented in HLA-A*0201 context. This is among the very few reports mapping L. major epitopes for human HLA types. Studies like this will speed up polytope vaccine idea towards leishmaniasis.

Leishmaniasis is currently a serious health as well as economic problem in underdeveloped and developing countries in Africa, Asia, the Near and Middle East, Central and South America and the Mediterranean region. Cutaneous leishmaniasis is highly endemic in Iran, remarkably in Isfahan, Fars, Khorasan, Khozestan and Kerman provinces. Since effective prevention is not available and current curative therapy is expensive, often poorly tolerated and not always effective, alternative therapies including vaccination against leishmaniasis are of priority to overcome the problem. Although Th1 dominant response is so far considered as a pre-requisite for the immune system to overcome the infection, CD8⁺ T cell response could also be considered as a potent arm of immune system fighting against intracellular Leishmania. Polytope vaccine strategy may open up a new way in vaccine design against leishmaniasis, since they act as a potent tool to stimulate multi-CD8 T cell responses. Clearly there is a substantial need to evaluate the promising epitopes from different proteins of Leishmania parasite species. Some new immunoinformatic tools are now available to speed up this process, and we have shown here that in silico prediction can effectively evaluate HLA class I-restricted epitopes out of Leishmania proteins.

Mycobacterium tuberculosis peptides presented by HLA-E molecules are targets for human CD8 T-cells with cytotoxic as well as regulatory activity

Tuberculosis (TB) is an escalating global health problem and improved vaccines against TB are urgently needed. HLA-E restricted responses may be of interest for vaccine development since HLA-E displays very limited polymorphism (only 2 coding variants exist), and is not down-regulated by HIV-infection. The peptides from Mycobacterium tuberculosis (Mtb) potentially presented by HLA-E molecules, however, are unknown. Here we describe human T-cell responses to Mtb-derived peptides containing predicted HLA-E binding motifs and binding-affinity for HLA-E. We observed CD8(+) T-cell proliferation to the majority of the 69 peptides tested in Mtb responsive adults as well as in BCG-vaccinated infants. CD8(+) T-cells were cytotoxic against target-cells transfected with HLA-E only in the presence of specific peptide. These T cells were also able to lyse M. bovis BCG infected, but not control monocytes, suggesting recognition of antigens during mycobacterial infection. In addition, peptide induced CD8(+) T-cells also displayed regulatory activity, since they inhibited T-cell proliferation. This regulatory activity was cell contact-dependent, and at least partly dependent on membrane-bound TGF-beta. Our results significantly increase our understanding of the human immune response to Mtb by identification of CD8(+) T-cell responses to novel HLA-E binding peptides of Mtb, which have cytotoxic as well as immunoregulatory activity.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes

Background

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Methodology/Findings

Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.

Conclusions/Significance

A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure-activity relationship approach

In this paper, we have handpicked 23 kinds of electronic properties, 37 kinds of steric properties, 54 kinds of hydrophobic properties and 5 kinds of hydrogen bond properties from thousands of amino acid structural and property parameters. Principal component analysis (PCA) was applied on these parameters and thus ten score vectors involving significant nonbonding properties of 20 coded amino acids were yielded, called the divided physicochemical property scores (DPPS) of amino acids. The DPPS descriptor was then used to characterize the structures of 152 HLA-A*0201-restricted CTL epitopes, and significant variables being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure-activity relationship (QSAR) model by partial least square was established to predict the peptide-HLA-A*0201 molecule interactions. Statistical analysis on the resulted DPPS-based QSAR models were consistent well with experimental exhibits and molecular graphics display. Diversified properties of the different residues in binding peptides may contribute remarkable effect to the interactions between the HLA-A*0201 molecule and its peptide ligands. Particularly, hydrophobicity and hydrogen bond of anchor residues of peptides may have a significant contribution to the interactions. The results showed that DPPS can well represent the structural characteristics of the antigenic peptides and is a promising approach to predict the affinities of peptide binding to HLA-A*0201 in a efficient and intuitive way. We expect that this physical-principle based method can be applied to other protein-peptide interactions as well.

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.