Rational Design Of Peptide Vaccines Research Articles

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1.

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Homology Modeling Epitopes of Kirsten Rat Sarcoma (KRAS) G12D, G12V and G12R as Pancreatic Ductal Adenocarcinoma Vaccine Candidates

Pancreatic ductal adenocarcinoma (PDAC) is among the world's deadliest cancers. Multiple studies demonstrated that PDAC is frequently characterized by the presence of Kirsten Rat Sarcoma (KRAS) G12D, G12V, and G12R protein mutants. The mutants are potential immunotherapy targets due to their potential as cancer-specific neoantigens. KRAS G12D, G12V and G12R contain vaccine-immunogenic epitopes. KRAS G12D, G12V and G12R epitopes were presented at major histocompatibility complexes (MHC) class I. The rational design of peptide vaccines to enhance the efficacy of cancer immunotherapy is facilitated by developing a peptide structural data library and knowledge of the MHC and antigen presentation processes. Before predicting peptide activity against MHC, homology modeling must transform the peptide into a three-dimensional structure. In this study, I-TASSER was used to perform homology modeling with the assistance of other applications. In silico methods for predicting epitopes to produce rationally designed peptide vaccines can increase the efficacy of these vaccines. This study yielded four epitope models that are potential PDAC vaccination candidates, KSFEDIHHYR, GIPFIETSAK, VVVGARGVGK and VVVGADGVGK.

A Bioinformatics Protocol for Rational Design of Peptide Vaccines and the COVID-19 Rampage

The currently ongoing coronavirus pandemic, the SARSCOV- 2, interchangeably referred to as the COVID-19 infection, has in a short span of time altered the ways and means of almost all of mankind. So strong has been its effect that all human activity ceased in one way or another for a considerable time, led to significant loss of life and economic drain of untold proportions such that there are open debates on whether the world will forever now change from the way we were used to.

A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development.

Rational design of peptide vaccines against the Zika virus through sequence descriptors: Techniques and Problems

Rational design of peptide vaccines against the Zika virus through sequence descriptors: Techniques and Problems

Rational Design of Peptide Vaccines Against Multiple Types of Human Papillomavirus.

Human papillomavirus (HPV) occurs in many types, some of which cause cervical, genital, and other cancers. While vaccination is available against the major cancer-causing HPV types, many others are not covered by these preventive measures. Herein, we present a bioinformatics study for the designing of multivalent peptide vaccines against multiple HPV types as an alternative strategy to the virus-like particle vaccines being used now. Our technique of rational design of peptide vaccines is expected to ensure stability of the vaccine against many cycles of mutational changes, elicit immune response, and negate autoimmune possibilities. Using the L1 capsid protein sequences, we identified several peptides for potential vaccine design for HPV 16, 18, 33, 35, 45, and 11 types. Although there are concerns about the epitope-binding affinities for the peptides identified in this process, the technique indicates possibilities of multivalent, adjuvanted, peptide vaccines against a wider range of HPV types, and tailor-made different combinations of the peptides to address frequency variations of types over different population groups as required for prophylaxis and at lower cost than are in use at the present time.

Structural Characterization by NMR of a Double Phosphorylated Chimeric Peptide Vaccine for Treatment of Alzheimer’s Disease

Rational design of peptide vaccines becomes important for the treatment of some diseases such as Alzheimer’s disease (AD) and related disorders. In this study, as part of a larger effort to explore correlations of structure and activity, we attempt to characterize the doubly phosphorylated chimeric peptide vaccine targeting a hyperphosphorylated epitope of the Tau protein. The 28-mer linear chimeric peptide consists of the double phosphorylated B cell epitope Tau229-237[pThr231/pSer235] and the immunomodulatory T cell epitope Ag85B241-255 originating from the well-known antigen Ag85B of the Mycobacterium tuberculosis, linked by a four amino acid sequence -GPSL-. NMR chemical shift analysis of our construct demonstrated that the synthesized peptide is essentially unfolded with a tendency to form a β-turn due to the linker. In conclusion, the -GPSL- unit presumably connects the two parts of the vaccine without transferring any structural information from one part to the other. Therefore, the double phosphorylated epitope of the Tau peptide is flexible and accessible.

A Systems Biology Perspective on Rational Design of Peptide Vaccine Against Virus Infections

With the recent onset of influenza A (H1N1) pandemic, the need for improved vaccines against virus infections has become an international priority. Strategies for vaccine development have changed over time, from whole-virus to immunogenic proteins and further to antigenic viral peptides. Various algorithms and bioinformatics tools have been developed to predict immunogenic peptide regions in an antigenic protein sequence. Recent advances in next-generation sequencing technologies, as represented by real time DNA sequencing, provide increased throughput and yield of data on viral pathogens and host cells. This enables us to 'mine' the genomic sequence for putative vaccine candidates or targets, allowing a more rational approach to the peptide vaccine design. This review first describes current computational tools available for the rational design of peptide vaccines and then addresses recent attempts to define pathogenic peptides at '- omics' level. As there are interplay between antibody and T cells, as well as intersection between viruses and hosts, the vaccine-mediated immunity are orchestrated by multiple factors within an interaction network. Therefore, single viral peptide alone fails to provide optimal immunity. Systems biology offers a systems-level perspective of how the various arms of the immune response are integrated to give immune response, as well as how host and virus interact, thereby providing an integrated approach to select the most promising candidates for peptide vaccines development. We highlight in this article the system-level application of rational peptide vaccine design, which may be a general paradigm for future viral vaccine development.

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.

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.

Knowledge-based structure prediction of MHC class I bound peptides: a study of 23 complexes

The binding of T-cell antigenic peptides to MHC molecules is a prerequisite for their immunogenicity. The ability to identify binding peptides based on the protein sequence is of great importance to the rational design of peptide vaccines. As the requirements for peptide binding cannot be fully explained by the peptide sequence per se, structural considerations should be taken into account and are expected to improve predictive algorithms. The first step in such an algorithm requires accurate and fast modeling of the peptide structure in the MHC-binding groove. We have used 23 solved peptide-MHC class I complexes as a source of structural information in the development of a modeling algorithm. The peptide backbones and MHC structures were used as the templates for prediction. Sidechain conformations were built based on a rotamer library, using the 'dead end elimination' approach. A simple energy function selects the favorable combination of rotamers for a given sequence. It further selects the correct backbone structure from a limited library. The influence of different parameters on the prediction quality was assessed. With a specific rotamer library that incorporates information from the peptide sidechains in the solved complexes, the algorithm correctly identifies 85% (92%) of all (buried) sidechains and selects the correct backbones. Under cross-validation, 70% (78%) of all (buried) residues are correctly predicted and most of all backbones. The interaction between peptide sidechains has a negligible effect on the prediction quality. The structure of the peptide sidechains follows from the interactions with the MHC and the peptide backbone, as the prediction is hardly influenced by sidechain interactions. The proposed methodology was able to select the correct backbone from a limited set. The impairment in performance under cross-validation suggests that, currently, the specific rotamer library is not satisfactorily representative. The predictions might improve with an increase in the data.

A Structure-Based Algorithm to Predict Potential Binding Peptides to MHC Molecules with Hydrophobic Binding Pockets

ABSTRACT: Binding of peptides to MHC class I molecules is a prerequisite for their recognition by cytotoxic T cells. Consequently, identification of peptides that will bind to a given MHC molecule must constitute a central part of any algorithm for prediction of T-cell antigenic peptides based on the amino acid sequence of the protein. Binding motifs, defined by anchor positions only, have proven to be insufficient to ensure binding, suggesting that other positions along the peptide sequence also affect peptide-MHC interaction. The second phase of prediction schemes therefore take into account the effect of all positions along the peptide sequence, and are based on position-dependent-coefficients that are used in the calculation of a peptide score. These coefficients can be extracted from a large ensemble of binding sequences that were tested experimentally, or derived from structural considerations, as in the algorithm developed by us recently. This algorithm uses the coordinates of solved complexes to evaluate the interactions of peptide amino acids with MHC contact residues, and results in a peptide score that reflects its binding energy. Here we present our analysis for peptide binding to four MHC alleles (HLA-A2, HLA-A68, HLA-B27 and H-2K b), and compare the predictions of the algorithm to experimental binding data. The algorithm performs successfully in predicting peptide binding to MHC molecules with hydrophobic binding pockets but not when MHC molecules with hydrophilic, charged pockets are considered. For MHC molecules with hydrophobic pockets it is demonstrated how the algorithm succeeds in distinguishing binding from non-binding peptides, and in high ranking of immunogenic peptides within all overlapping same-length peptides spanning their respective protein sequences. The latter property of the algorithm makes it a useful tool in the rational design of peptide vaccines aimed at T-cell immunity.

Engineered topographic determinants with alpha beta, beta alpha beta, and beta alpha beta alpha topologies show high affinity binding to native protein antigen (lactate dehydrogenase-C4).

The use of peptides has attracted much interest in the development of synthetic vaccines. Although our current understanding of peptide antigens as immunogens has been greatly advanced recently, there still remain many obstacles. The B cell response elicited by a peptide antigen is governed by a number of poorly understood events such as epitope structure, T cell dependency and major histocompatibility complex restriction, adjuvancy, route of immunization, and immunogen stability. In this paper, we extend our previous studies on the problem of the topographical nature of antigenic sites on native protein antigens, in terms of how much molecular mimicry must be maintained in an antigenic determinant for the induction of high affinity antibodies specific for native protein. We show here that an antigenic epitope from the model contraceptive vaccine candidate lactate dehydrogenase (LDH-C4) can be rationally engineered into a highly structured conformation that mimics the corresponding site in the native three-dimensional protein. Our strategy is based on the selection of an antigenic segment which exhibits certain secondary structural properties and by design principles is fixed in three dimensions by appropriate grafting onto a supersecondary structural motif such as alpha beta, beta alpha beta, or beta alpha beta alpha. The biophysical data are consistent with the proposed secondary structures, and antibodies raised to the various construct show high affinity for the native protein. These studies lend further credence to the conformational nature of peptide epitopes and provide a basis for the rational design of peptide vaccines.