Identification Of Potential Epitopes Research Articles

Peptide Hamming Graphs: A network representation of peptides presented through specific HLAs to identify potential epitope clusters

BackgroundClass I Major Histocompatibility Complex plays a critical role in the adaptive immune response by binding to peptides processed by Proteasome and Transporter associated with antigen processing complex and presenting them on the cell surface to cytotoxic T-cells. Understanding the process of peptide presentation and studying how presented peptides are distributed in the huge space of all potential epitopes could have a dramatic impact in the context of vaccine design, transplantation, autoimmunity, and cancer development. MethodsIn the present work we propose a graph-driven approach to investigate the landscape of both self (human) and viral (254 organisms) peptides presented on cell surface through class I Major Histocompatibility Complex considering specific HLAs. For each considered HLA (N = 89) we designed a network, namely Peptide Hamming Graph, where nodes are peptides predicted to be presented by a given HLA and an edge is set when the Hamming distance between two peptides is equal or smaller than 2 (i.e. the same amino acid occurs in at least 7 positions of the two sequences). ResultsThrough the analysis of Peptide Hamming Graphs we studied how predicted presented peptides are distributed in the whole configurational space for different HLAs, identifying sets of viral peptides that can constitute a potential target for the immune system. In particular we selected connected components of the graph made exclusively of viral peptides and sets of viral peptides with high node degree interacting exclusively with viral neighbours. ConclusionsThis work constitutes an innovative approach to study potential cytotoxic T-cell epitopes relying on a network approach, overcoming the classical paradigm based on the identification of potential epitopes only considering their features as single peptides. T-cell cross-reactivity plays a focal role for the efficacy of this strategy increasing the probability of recognition, and consequently a stronger immune response, of presented peptides far from self, sharing a common pattern in terms of sequence similarity.

Predicting peptide vaccine candidates against H1N1 influenza virus through theoretical approaches.

Identification of potential epitopes that might activate the immune system has been facilitated by the employment of algorithms that use experimental data as templates. However, in order to prove the affinity and the map of interactions between the receptor (major histocompatibility complex, MHC, or T-cell receptor) and the potential epitope, further computational studies are required. Docking and molecular dynamics (MDs) simulations have been an effective source of generating structural information at molecular level in immunology. Herein, in order to provide a detailed understanding of the origins of epitope recognition and to select the best peptide candidate to develop an epitope-based vaccine, docking and MDs simulations in combination with MMGBSA free energy calculations and per-residue free energy decomposition were performed, taking as starting complexes those formed between four designed epitopes (P1-P4) from hemagglutinin (HA) of the H1N1 influenza virus and MHC-II anchored in POPC membrane. Our results revealed that the energetic contributions of individual amino acids within the pMHC-II complexes are mainly dictated by van der Waals interactions and the nonpolar part of solvation energy, whereas the electrostatic interactions corresponding to hydrogen bonds and salt bridges determine the binding specificity, being the most favorable interactions formed between p4 and MHC-II. Then, P1-P4 epitopes were synthesized and tested experimentally to compare theoretical and experimental results. Experimental results show that P4 elicited the highest strong humoral immune response to HA of the H1N1 and may induce antibodies that are cross-reactive to other influenza subtypes, suggesting that it could be a good candidate for the development of a peptide-based vaccine.

Computer aided prediction and identification of potential epitopes in the receptor binding domain (RBD) of spike (S) glycoprotein of MERS-CoV.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) belongs to the coronaviridae family. In spite of several outbreaks in the very recent years, no vaccine against this deadly virus is developed yet. In this study, the receptor binding domain (RBD) of Spike (S) glycoprotein of MERS-CoV was analyzed through Computational Immunology approach to identify the antigenic determinants (epitopes). In order to do so, the sequences of S glycoprotein that belong to different geographical regions were aligned to observe the conservancy of MERS-CoV RBD. The immune parameters of this region were determined using different in silico tools and Immune Epitope Database (IEDB). Molecular docking study was also employed to check the affinity of the potential epitope towards the binding cleft of the specific HLA allele. The N-terminus RBD (S367-S606) of S glycoprotein was found to be conserved among all the available strains of MERS-CoV. Based on the lower IC50 value, a total of eight potential T-cell epitopes and 19 major histocompatibility complex (MHC) class-I alleles were identified for this conserved region. A 9-mer epitope CYSSLILDY displayed interactions with the maximum number of MHC class-I molecules and projected the highest peak in the B-cell antigenicity plot which concludes that it could be a better choice for designing an epitope based peptide vaccine against MERSCoV considering that it must undergo further in vitro and in vivo experiments. Moreover, in molecular docking study, this epitope was found to have a significant binding affinity of -8.5 kcal/mol towards the binding cleft of the HLA-C*12:03 molecule.