Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders

Exploring diverse approaches for predicting interferon-gamma release: utilizing MHC class II and peptide sequences

Therapeutic proteins are in high demand due to their significant potential, driving continuous market growth. However, a critical concern for therapeutic proteins is their ability to trigger an immune response, while some treatments rely on this response for their therapeutic effect. Therefore, to assess the efficacy and safety of the drug, it is pivotal to determine its immunogenicity potential. Various experimental methods, such as cytokine release or T-cell proliferation assays, are used for this purpose. However, these assays can be costly, time-consuming, and often limited in their ability to screen large peptide sets across diverse major histocompatibility complex (MHC) alleles. Hence, this study aimed to develop a computational classification model for predicting the release of interferon-gamma based on the peptide sequence and the MHC class II (MHC-II) allele pseudo-sequence, which represents the binding environment of the MHC-II molecule. The dataset used in this study was obtained from the Immune Epitope Database and labeled as active or inactive. Among the approaches explored, the random forest algorithm combined with letter-based encoding resulted in the overall best-performing model. Consequently, this model's generalizability to other T-cell activities was further evaluated using a T-cell proliferation dataset. Furthermore, feature importance analysis and virtual single-point mutations were conducted to gain insights into the model's decision-making and to improve the interpretability of the model.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Promoting the formation of Pi-stacking interaction to improve CTL cells activation between modified peptide and HLA

OBJECTIVE

This study aims to investigate the use of single residue substitution to promote the formation of pi-stacking interactions between peptides and Human leukocyte antigen (HLA)-A*2402 molecules to improve the affinity of peptides and HLA molecules, as well as the level of cytotoxic T lymphocyte (CTL) cells activated by peptides-HLA (p-HLA) complex.

METHODS

Molecular docking and molecular dynamics simulation were used to simulate and analyze the interactions and binding free energies between HLA-A*2402-restricted antigen peptides and HLA molecules, before and after the single residue substitution. HLA-A*2402 restricted antigen peptides before and after the single residue replacement were loaded into dendritic cells (DCs) in vitro, and further Enzyme-Linked ImmunoSpot (ELispot) test was carried out to evaluate the effect of modified antigen peptides on the immune activation of CTL cells.

RESULT

After replacing the antigen peptides with a single residue, some of them could promote the formation of pi-stacking interaction. The binding free energy between the modified antigen peptides and HLA-A*2402, as well as the level of immune activation of CTL cells were mostly higher than before, especially after the replacement of the 9th residue of the polypeptide, such as C9F and C9W. There was a significant negative correlation between the level of activated CTL cells by modified antigen peptides and the total interaction amount of hydrogen bonds and salt bridges.

CONCLUSION

Promoting the formation of pi-stacking interaction between antigen peptides and HLA-A*2402 molecules could increase the total binding free energy of p-HLA complex and the level of CTL cells activation. In addition, the amount of hydrogen bonds and salt bridges between peptides and HLA could reduce the level of immune activation. All the characteristics above can improve the immunogenicities of the weak antigens.

Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol

Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.