Integrating In Silico and In Vitro Analysis of Peptide Binding Affinity to HLA-Cw*0102: A Bioinformatic Approach to the Prediction of New Epitopes

Background: Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102. Methodology/Findings: Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1. Conclusions/Significance: A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Using databases and data mining in vaccinology.

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology

Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction

Immunoinformatics is facilitating important change within immunology and is helping it to engage more completely with the dynamic post-genomic revolution sweeping through bioscience. Historically, predicting the specificity of peptide Major Histocompatibility Complex (MHC) interactions has been the major contribution made by bioinformatics disciplines to research in immunology and the vaccinology. This will be the focus of the current chapter. Initially, we will review some background to this problem, such as the thermodynamics of peptide binding and the known constraints on peptide selectivity by the MHC. We will then review artificial intelligence and machine learning approaches to the prediction problem. Finally, we will outline our own contribution to this field: the application of QSAR techniques to the prediction of peptide-MHC binding

Dose-response titration of the peptide CAPAGFAIL.

<p>The responsiveness of T cells from two HIV-infected individuals to 10-fold dilutions of the peptide CAPAGFAIL was assessed by IFN-γ ELISPOT assay. The results shown are the specific response elicited at each peptide concentration, expressed as a percentage of the maximum response (that elicited by 10⁻⁵ M peptide) observed in the individual concerned.</p

Test sets of newly-designed peptides.

a<p>Calculated pBL₅₀s generated using the QSAR model described in the text.</p>b<p>Experimental pBL₅₀s measured using a FACS-based MHC stabilisation assay.</p

Summary of amino acids at each position that favour or disfavour peptide binding to HLA-Cw*0102, as defined by the QSAR model.

a<p>Amino acids are included if they exceed a threshold of >±0.10 as favoured or disfavoured residues as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008095#pone-0008095-g001" target="_blank">Fig. 1</a>.</p>b<p>Amino acids are highlighted in bold if they exceed a threshold of >±0.20 as favoured or disfavoured residues as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008095#pone-0008095-g001" target="_blank">Fig. 1</a>.</p

HIV-1 peptides predicted to bind to HLA-Cw*0102.

a<p>Peptide number used in subsequent ELISPOT experiments.</p>b<p>Calculated pBL₅₀s generated using the QSAR model described in the text.</p>c<p>Experimental pBL₅₀s measured using a FACS-based MHC stabilisation assay.</p>d<p>Peptides listed here were unique to the HXB2 sequence.</p