Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Role of the Host Genetic Susceptibility to 2009 Pandemic Influenza A H1N1

Influenza A virus (IAV) is the most common infectious agent in humans, and infects approximately 10-20% of the world's population, resulting in 3-5 million hospitalizations per year. A scientific literature search was performed using the PubMed database and the Medical Subject Headings (MeSH) "Influenza A H1N1" and "Genetic susceptibility". Due to the amount of information and evidence about genetic susceptibility generated from the studies carried out in the last influenza A H1N1 pandemic, studies published between January 2009 to May 2020 were considered; 119 papers were found. Several pathways are involved in the host defense against IAV infection (innate immune response, pro-inflammatory cytokines, chemokines, complement activation, and HLA molecules participating in viral antigen presentation). On the other hand, single nucleotide polymorphisms (SNPs) are a type of variation involving the change of a single base pair that can mean that encoded proteins do not carry out their functions properly, allowing higher viral replication and abnormal host response to infection, such as a cytokine storm. Some of the most studied SNPs associated with IAV infection genetic susceptibility are located in the FCGR2A, C1QBP, CD55, and RPAIN genes, affecting host immune responses through abnormal complement activation. Also, SNPs in IFITM3 (which participates in endosomes and lysosomes fusion) represent some of the most critical polymorphisms associated with IAV infection, suggesting an ineffective virus clearance. Regarding inflammatory response genes, single nucleotide variants in IL1B, TNF, LTA IL17A, IL8, IL6, IRAK2, PIK3CG, and HLA complex are associated with altered phenotype in pro-inflammatory molecules, participating in IAV infection and the severest form of the disease.

HLA class I loss in metachronous metastases prevents continuous T cell recognition of mutated neoantigens in a human melanoma model

T lymphocytes against tumor-specific mutated neoantigens can induce tumor regression. Also, the size of the immunogenic cancer mutanome is supposed to correlate with the clinical efficacy of checkpoint inhibition. Herein, we studied the susceptibility of tumor cell lines from lymph node metastases occurring in a melanoma patient over several years towards blood-derived, neoantigen-specific CD8⁺ T cells. In contrast to a cell line established during early stage III disease, all cell lines generated at later time points from stage IV metastases exhibited partial or complete loss of HLA class I expression. Whole exome and transcriptome sequencing of the four tumor lines and a germline control were applied to identify expressed somatic single nucleotide substitutions (SNS), insertions and deletions (indels). Candidate peptides encoded by these variants and predicted to bind to the patient's HLA class I alleles were synthesized and tested for recognition by autologous mixed lymphocyte-tumor cell cultures (MLTCs). Peptides from four mutated proteins, HERPUD1^G161S, INSIG1^S238F, MMS22L^S437F and PRDM10^S1050F, were recognized by MLTC responders and MLTC-derived T cell clones restricted by HLA-A*24:02 or HLA-B*15:01. Intracellular peptide processing was verified with transfectants. All four neoantigens could only be targeted on the cell line generated during early stage III disease. HLA loss variants of any kind were uniformly resistant. These findings corroborate that, although neoantigens represent attractive therapeutic targets, they also contribute to the process of cancer immunoediting as a serious limitation to specific T cell immunotherapy.

A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as −log10 of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705.

Designing immunogenic peptides

Peptides fulfill many roles in immunology, yet none are more important than their role as immunogenic epitopes driving the adaptive immune response, our ultimate bulwark against infectious disease. Peptide epitopes are mediated primarily by their interaction with major histocompatibility complexes (T-cell epitopes) and antibodies (B-cell epitopes). As pathogen genomes continue to be revealed, both experimental and computational epitope mapping are becoming crucial tools in vaccine discovery. Immunoinformatics offers many tools, techniques and approaches for in silico epitope characterization, which is capable of greatly accelerating epitope design.

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.

Immunoproteomics: current technology and applications

The varied landscape of the adaptive immune response is determined by the peptides presented by immune cells, derived from viral or microbial pathogens or cancerous cells. The study of immune biomarkers or antigens is not new and classical methods such as agglutination, enzyme-linked immunosorbent assay, or Western blotting have been used for many years to study the immune response to vaccination or disease. However, in many of these traditional techniques, protein or peptide identification has often been the bottleneck. Recent advances in genomics and proteomics, has led to many of the rapid advances in proteomics approaches. Immunoproteomics describes a rapidly growing collection of approaches that have the common goal of identifying and measuring antigenic peptides or proteins. This includes gel based, array based, mass spectrometry, DNA based, or in silico approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers. This review gives an overview of immunoproteomics and closely related technologies that are used to define the full set of antigens targeted by the immune system during disease.

HLA-Cw*0102-restricted HIV-1 p24 epitope variants can modulate the binding of the inhibitory KIR2DL2 receptor and primary NK cell function

Accumulating evidence suggests an important role for Natural Killer (NK) cells in the control of HIV-1 infection. Recently, it was shown that NK cell-mediated immune pressure can result in the selection of HIV-1 escape mutations. A potential mechanism for this NK cell escape is the selection of HLA class I-presented HIV-1 epitopes that allow for the engagement of inhibitory killer cell immunoglobulin-like receptors (KIRs), notably KIR2DL2. We therefore investigated the consequences of sequence variations within HLA-Cw*0102-restricted epitopes on the interaction of HLA-Cw*0102 with KIR2DL2 using a large panel of overlapping HIV-1 p24 Gag peptides. 217 decameric peptides spanning the HIV-1 p24 Gag consensus sequence were screened for HLA-Cw*0102 stabilization by co-incubation with Cw*0102⁺/TAP-deficient T2 cells using a flow cytometry-based assay. KIR2DL2 binding was assessed using a KIR2DL2-IgG fusion construct. Function of KIR2DL2⁺ NK cells was flow cytometrically analyzed by measuring degranulation of primary NK cells after co-incubation with peptide-pulsed T2 cells. We identified 11 peptides stabilizing HLA-Cw*0102 on the surface of T2 cells. However, only one peptide (p24 Gag₂₀₉₋₂₁₈ AAEWDRLHPV) allowed for binding of KIR2DL2. Notably, functional analysis showed a significant inhibition of KIR2DL2⁺ NK cells in the presence of p24 Gag₂₀₉₋₂₁₈-pulsed T2 cells, while degranulation of KIR2DL2⁻ NK cells was not affected. Moreover, we demonstrated that sequence variations in position 7 of this epitope observed frequently in naturally occurring HIV-1 sequences can modulate binding to KIR2DL2. Our results show that the majority of HIV-1 p24 Gag peptides stabilizing HLA-Cw*0102 do not allow for binding of KIR2DL2, but identified one HLA-Cw*0102-presented peptide (p24 Gag₂₀₉₋₂₁₈) that was recognized by the inhibitory NK cell receptor KIR2DL2 leading to functional inhibition of KIR2DL2-expressing NK cells. Engagement of KIR2DL2 might protect virus-infected cells from NK cell-mediated lysis and selections of sequence polymorphisms that increase avidity to KIR2DL2 might provide a mechanism for HIV-1 to escape NK cell-mediated immune pressure.

PeptX: using genetic algorithms to optimize peptides for MHC binding

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

High throughput T epitope mapping and vaccine development

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost.