A large-scale computational study of inhibitor risk in non-severe haemophilia A.

Over 500 missense F8 mutations have been reported to cause non-severe haemophilia A. Some F8 genotypes appear to confer a higher risk of inhibitor formation than others and individuals with the same F8 genotype may have differing risks of inhibitor formation. We present an in silico strategy demonstrating the heterogeneity of factor VIII (FVIII)-derived antigen presentation whilst identifying patterns of human leucocyte antigen (HLA) peptide binding that might predict future inhibitor risk. A well-validated computational tool, NetMHCII, enabled large-scale comparison of predicted antigen presentation between endogenous, mutated FVIII-derived peptides and wild-type, therapeutic FVIII-derived peptides spanning all F8 missense mutation positions reported to The Haemophilia A Mutation, Structure and Resource Site (HADB). We identify 40 F8 genotypes to be 'low risk' at a 50% inhibitory concentration (IC50 )-binding threshold of 300 nmol/l (P = 0·00005), defined as absence of novel peptide-major histocompatibility complex (MHC) surfaces for all 14 common HLA-DR alleles assessed. Analysing each of the possible 7280 F8 genotype/HLA-DR permutations individually at an IC50 threshold of 300 nmol/l, 65% are predicted to not generate a novel peptide-MHC surface that would be necessary to engage T cell help for subsequent anti-FVIII antibody generation. This study demonstrates the future importance of interpreting F8 genotype in the context of an individual's HLA profile to personalize inhibitor risk prediction.

Identification of the HLA-DM/HLA-DR interface.

Human leukocyte antigen (HLA)-DM is a critical participant in antigen presentation that catalyzes the dissociation of the Class II-associated Invariant chain-derived Peptide (CLIP) from the major histocompatibility complex (MHC) Class II molecules. There is competition amongst peptides for access to an MHC Class II groove and it has been hypothesised that DM functions as a 'peptide editor' that catalyzes the replacement of one peptide for another within the groove. It is established that the DM catalyst interacts directly with the MHC Class II but the precise location of the interface is unknown. Here, we combine previously described mutational data with molecular docking and energy minimisation simulations to identify a putative interaction site of >4000A2 which agrees with known point mutational data for both the DR and DM molecule. The docked structure is validated by comparison with experimental data and previously determined properties of protein-protein interfaces. A possible dissociation mechanism is suggested by the presence of an acidic cluster near the N terminus of the bound peptide.

The ninth International Conference of Anticancer Research, 6-10 October 2014, Sithonia, Greece.

The ninth conference of the International Institute for Anticancer Research, held in Sithonia, Greece in October 2014, included over 700 abstracts presented in 79 separate sessions and featured a wide range of topics in basic and clinical cancer research. This report describes a small but representative sample of these sessions. It covers some recent developments in research into the basic signal transduction pathways involved in carcinogenesis; a special session on the role of homeobox genes in cancer development; and clinical sessions covering advances in breast cancer, haematological cancers, and chemotherapy.

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale.

The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system-that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it-together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?

A computational Grid framework for immunological applications.

We have developed a computational Grid that enables us to exploit through a single interface a range of local, national and international resources. It insulates the user as far as possible from issues concerning administrative boundaries, passwords and different operating system features. This work has been undertaken as part of the European Union ImmunoGrid project whose aim is to develop simulations of the immune system at the molecular, cellular and organ levels. The ImmunoGrid consortium has members with computational resources on both sides of the Atlantic. By making extensive use of existing Grid middleware, our Grid has enabled us to exploit consortium and publicly available computers in a unified way, notwithstanding the diverse local software and administrative environments. We took 40 000 polypeptide sequences from 4000 avian and mammalian influenza strains and used a neural network for class I T-cell epitope prediction tools for 120 class I alleles and haplotypes to generate over 14 million high-quality protein-peptide binding predictions that we are mapping onto the three-dimensional structures of the proteins. By contrast, the Grid is also being used for developing new methods for class T-cell epitope predictions, where we have running batches of 120 molecular dynamics free-energy calculations.

Proteins accessible to immune surveillance show significant T-cell epitope depletion: Implications for vaccine design.

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.

Are bacterial vaccine antigens T-cell epitope depleted?

For many infectious diseases, protective immunity can be elicited by vaccination with pathogen-derived proteins. Peptides derived from these proteins are bound to major histocompatibility complex (MHC) molecules and presented to T-cell receptors to stimulate an immune response. We show here that, paradoxically, bacterial proteins known experimentally to elicit a protective immune response are relatively depleted in peptides predicted to bind to human MHC alleles. We propose three nonconflicting reasons for this: the lack of precision of current predictive software, the low incidence of hydrophobic residues in vaccine antigens or evolutionary pressure exerted on bacteria by the immune system. We suggest that there is little value in predicting candidate vaccines based on high MHC-binding epitope density.

A novel predictive technique for the MHC class II peptide-binding interaction.

Antigenic peptide is presented to a T-cell receptor through the formation of a stable complex with a Major Histocompatibility Complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC Class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. We have developed a novel predictive technique that uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC Class II peptide complex. This is the 1st structure-based technique, as previous methods have been based on binding data. ROC curves are used to quantify the accuracy of the molecular modeling technique. The novel predictive technique is found to be comparable with the best predictive software currently available.