Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules

Algorithm-supported, mass and sequence diversity-oriented random peptide library design

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.

Identification of B and T cell epitope based peptide vaccine from IGF-1 receptor in breast cancer

The insulin-like growth factor-1 receptor (IGF-1R) plays a key role in proliferation, growth, differentiation, and development of several human malignancies including breast and pancreatic adenocarcinoma. IGF-1R targeted immunotherapeutic approaches are particularly attractive, as they may potentially elicit even stronger antitumor responses than traditional targeted approaches. Cancer peptide vaccines can produce immunologic responses against cancer cells by triggering helper T cell (Th) or cytotoxic T cells (CTL) in association with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells. In our previous study, we set a technique based on molecular docking in order to find the best MHC class I and II binder peptides using GOLD. In the present work, molecular docking analyses on a library consisting of 30 peptides mimicking discontinuous epitopes from IGF-1R extracellular domain identified peptides 249 and 86, as the best MHC binder peptides to both MHC class I and II molecules. The receptors most often targeted by peptide 249 are HLA-DR4, HLA-DR3 and HLA-DR2 and those most often targeted by peptide 86 are HLA-DR4, HLA-DP2 and HLA-DR3. These findings, based on bioinformatics analyses, can be conducted in further experimental analyses in cancer therapy and vaccine design.

Expansion of quiescent lung adenocarcinoma CD8+ T cells by MUC1-8-mer peptide-T2 cell-β2 microglobulin complexes

Adoptive immunotherapy requires the isolation of CD8+ T cells specific for tumor-associated antigens, their expansion in vitro and their transfusion to the patient to mediate a therapeutic effect. MUC1 is an important adenocarcinoma antigen immunogenic for T cells. The MUC1-derived SAPDTRPA (MUC1-8-mer) peptide is a potent epitope recognized by CD8+ T cells in murine models. Likewise, the T2 cell line has been used as an antigen-presenting cell to activate CD8+ T cells, but so far MUC1 has not been assessed in this context. We evaluated whether the MUC1-8-mer peptide can be presented by T2 cells to expand CD25+CD8+ T cells isolated from HLA-A2+ lung adenocarcinoma patients with stage III or IV tumors. The results showed that MUC1-8-mer peptide-loaded T2 cells activated CD8+ T cells from cancer HLA-A2+ patients when anti-CD2, anti-CD28 antibodies and IL-2 were added. The percentage of CD25+CD8+ T cells was 3-fold higher than those in the non-stimulated cells (P=0.018). HLA-A2+ patient cells showed a significant difference (2.3-fold higher) in activation status than HLA-A2+ healthy control cells (P=0.04). Moreover, 77.6% of MUC1-8-mer peptide-specific CD8+ T cells proliferated following a second stimulation with MUC1-8-mer peptide-loaded T2 cells after 10 days of cell culture. There were significant differences in the percentage of basal CD25+CD8+ T cells in relation to the cancer stage; this difference disappeared after MUC1-8-mer peptide stimulation. In conclusion, expansion of CD25+CD8+ T cells by MUC1-8 peptide-loaded T2 cells plus costimulatory signals via CD2, CD28 and IL-2 can be useful in adoptive immunotherapy.

DockTope: a Web-based tool for automated pMHC-I modelling

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8⁺ T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

Prediction of structural stability of short beta-hairpin peptides by molecular dynamics and knowledge-based potentials

BACKGROUND

The structural stability of peptides in solution strongly affects their binding affinities and specificities. Thus, in peptide biotechnology, an increase in the structural stability is often desirable. The present work combines two orthogonal computational techniques, Molecular Dynamics and a knowledge-based potential, for the prediction of structural stability of short peptides (< 20 residues) in solution.

RESULTS

We tested the new approach on four families of short beta-hairpin peptides: TrpZip, MBH, bhpW and EPO, whose structural stabilities have been experimentally measured in previous studies. For all four families, both computational techniques show considerable correlation (r > 0.65) with the experimentally measured stabilities. The consensus of the two techniques shows higher correlation (r > 0.82).

CONCLUSION

Our results suggest a prediction scheme that can be used to estimate the relative structural stability within a peptide family. We discuss the applicability of this predictive approach for in-silico screening of combinatorial peptide libraries.

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.