In-silico design of peptides for inhibition of HLA-A*03-KLIETYFSK complex as a new drug design for treatment of multiples sclerosis disease

Computational insights in design of Crimean-Congo hemorrhagic fever virus conserved immunogenic nucleoprotein peptides containing multiple epitopes

Crimean-Congo hemorrhagic fever virus (CCHFV) belongs to Nairoviridae family and has tripartite RNA genome. It is endemic in various countries of Asia, Africa, and Europe and is primarily transmitted by Hyalomma ticks but nosocomial transmission also been reported. Vaccines for CCHF are in early phase of clinical trial; therefore, this work is centered on identification of potential immunogenic peptide as vaccine candidates with application of different immunoinformatics approaches. Eleven conserved (>90%) peptides of CCHFV nucleoprotein were selected for CD8+ T-cell (NetMHCpan 4.1b and NetCTLpan 1.1 server) and CD4+ T-cell (NetMHCIIpan-4.0 server and Tepitool) epitope prediction. Three peptides containing multiple CD8+ and CD4+ T-cell and B-cell epitopes were identified on basis of consensus prediction approach. Peptides displayed good antigenicity score of 0.45-0.68 and predicted to bind with diverse human leukocyte antigen (HLA) alleles. Molecular docking was performed with epitopes to HLA and HLA-epitopes complex to T-cell receptor (TCR). In most of the cases, docked complex of HLA-epitope and HLA-epitopes-TCR have the binding energy close to respective natural bound peptide complex with HLA and TCR. Molecular dynamic simulation also revealed that HLA-peptide complexes have minimum fluctuation and deviation than HLA-peptide-TCR docked over 50 ns simulation run. Considering these findings, identified peptides can serve as potential vaccine candidates for CCHFV disease.

Identification of potential antigenic proteins and epitopes for the development of a monkeypox virus vaccine: an in silico approach

Virus assembly, budding, or surface proteins play important roles such as viral attachment to cells, fusion, and entry into cells. The present study aimed to identify potential antigenic proteins and epitopes that could be used to develop a vaccine or diagnostic assay against the Monkeypox virus (MPXV) which may cause a potential epidemic. To do this, 39 MPXV proteins (including assembly, budding, and surface proteins) were analyzed using an in silico approach. Of these 39 proteins, the F5L virus protein was found to be the best vaccine candidate due to its signal peptide properties, negative GRAVY value, low transmembrane helix content, moderate aliphatic index, large molecular weight, long-estimated half-life, beta wrap motifs, and being stable, soluble, and containing non-allergic features. Moreover, the F5L protein exhibited alpha-helical secondary structures, making it a potential "structural antigen" recognized by antibodies. The other viral protein candidates were A9 and A43, but A9 lacked beta wrap motifs, while A43 had a positive GRAVY value and was insoluble. These two proteins were not as suitable candidates as the F5L protein. The KRVNISLTCL epitope from the F5L protein demonstrated the highest antigen score (2.4684) for MHC-I, while the GRFGYVPYVGYKCI epitope from the A9 protein exhibited the highest antigenicity (1.754) for MHC-II. Both epitopes met the criteria for high antigenicity, non-toxicity, solubility, non-allergenicity, and the presence of cleavage sites. Molecular docking and dynamics (MD) simulations further validated their potential, revealing stable and energetically favorable interactions with MHC molecules. The immunogenicity assessment showed that GRFGYVPYVGYKCI could strongly induce immune responses through both IFN-γ and IL-4 pathways, suggesting its capacity to provoke a balanced Th1 and Th2 response. In contrast, KRVNISLTCL exhibited limited immunostimulatory potential. Overall, these findings lay the groundwork for future vaccine development, indicating that F5L, particularly the GRFGYVPYVGYKCI epitope, may serve as an effective candidate for peptide-based vaccine design against MPXV.

Bioinformatics design of peptide binding to the human cardiac troponin I (cTnI) in biosensor development for myocardial infarction diagnosis

Cardiovascular disease has reached a mortality rate of 470,000 patients each year. Myocardial infarction accounts for 49.2% of these deaths, and the cTnI protein is a crucial target in diagnosing myocardial infarction. A peptide-based bioreceptor design using a computational approach is a good candidate to be developed for a rapid, effective, and selective detection method for cTnI although it is still lacking in study. Hence, to address the scientific gap, we develop a new candidate peptide for the cTnI biosensor by bioinformatics method and present new computational approaches. The sequential point mutations were made to the selected peptide to increase its stability and affinity for cTnI. Next, molecular docking was performed to select the mutated peptide, and one of the best results was subjected to the molecular dynamics simulation. Finally, the results showed that the best peptide showed the lowest affinity and good stability among other mutated peptide designs for interacting with the cTnI protein. In addition, the peptide has been tested to have a higher specificity towards cTnI than its major isomer, sTnI, through molecular docking and molecular dynamics simulation. Therefore, the peptide is considered a good potential bioreceptor for diagnosing myocardial infarction diseases.

In silico advancements in Peptide-MHC interaction: A molecular dynamics study of predicted glypican-3 peptides and HLA-A*11:01

Our study employed molecular dynamics (MD) simulations to assess the binding affinity between short peptides derived from the tumor-associated antigen glypican 3 (GPC3) and the major histocompatibility complex (MHC) molecule HLA-A*11:01 in hepatocellular carcinoma. We aimed to improve the reliability of in silico predictions of peptide-MHC interactions, which are crucial for developing targeted cancer therapies. We used five algorithms to discover four peptides (TTDHLKFSK, VINTTDHLK, KLIMTQVSK, and STIHDSIQY), demonstrating the substantial potential for HLA-A11:01 presentation. The Anchored Peptide-MHC Ensemble Generator (APE-Gen) was used to create the initial structure of the peptide-MHC complex. This was followed by a 200 ns molecular dynamics (MD) simulation using AMBER22, which verified the precise positioning of the peptides in the binding groove of HLA-A*11:01, specifically at the A and F pockets. Notably, the 2nd residue, which serves as a critical anchor within the 2nd pocket, played a pivotal role in stabilising the binding interactions.VINTTDHLK (ΔG SIE = -14.46 ± 0.53 kcal/mol and ΔG MM/GBSA = -30.79 ± 0.49 kcal/mol) and STIHDSIQY (ΔG SIE and ΔG MM/GBSA = -14.55 ± 0.16 and -23.21 ± 2.23 kcal/mol) exhibited the most effective binding potential among the examined peptides, as indicated by both their binding free energies and its binding affinity on the T2 cell line (VINTTDHLK: IC50 = 0.45 nM; STIHDSIQY: IC50 = 0.35 nM). The remarkable concordance between in silico and in vitro binding affinity results was of particular significance, indicating that MD simulation is a potent instrument capable of bolstering confidence in in silico peptide predictions. By employing MD simulation as a method, our study provides a promising avenue for improving the prediction of potential peptide-MHC interactions, thereby facilitating the development of more effective and targeted cancer therapies.

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology

Antigen processing is an immunological mechanism by which intracellular peptides are transported to the cell surface while bound to Major Histocompatibility Complex molecules, where they can be surveyed by circulating CD8+ or CD4+ T-cells, potentially triggering an immunological response. The antigen processing pathway is a complex multistage filter that refines a huge pool of potential peptide ligands derived from protein degradation into a smaller ensemble for surface presentation. Each stage presents unique challenges due to the number of ligands, the polymorphic nature of MHC and other protein constituents of the pathway and the nature of the interactions between them. Predicting the ensemble of displayed peptide antigens, as well as their immunogenicity, is critical for improving T cell vaccines against pathogens and cancer. Our predictive abilities have always been hindered by an incomplete empirical understanding of the antigen processing pathway. In this review, we highlight the role of computational and structural approaches in improving our understanding of antigen processing, including structural biology, computer simulation, and machine learning techniques, with a particular focus on the MHC-I pathway.