Host genotype and time dependent antigen presentation of viral peptides: predictions from theory

The influence of HLA/HIV genetics on the occurrence of elite controllers and a need for therapeutics geotargeting view

The interaction of HIV-1, human leukocyte antigen (HLA), and elite controllers (EC) compose a still intricate triad. Elite controllers maintain a very low viral load and a normal CD4 count, even without antiretrovirals. There is a lot of diversity in HIV subtypes and HLA alleles. The most common subtype in each country varies depending on its localization and epidemiological history. As we know EC appears to maintain an effective CD8 response against HIV. In this phenomenon, some alleles of HLAs are associated with a slow progression of HIV infection, others with a rapid progression. This relationship also depends on the virus subtype. Epitopes of Gag protein-restricted by HLA-B*57 generated a considerable immune response in EC. However, some mutations allow HIV to escape the CD8 response, while others do not. HLA protective alleles, like HLA-B*27, HLA-B*57 and HLA-B*58:01, that are common in Caucasians infected with HIV-1 Clade B, do not show the same protection in sub-Saharan Africans infected by HIV-1 Clade C. Endogenous pathway of antigen processing and presentation is used to present intracellular synthesized cellular peptides as well as viral protein fragments via the MHC class I molecule to the cytotoxic T-lymphocytes (CTLs). Some epitopes are immunodominant, which means that they drive the immune reaction to some virus. Mutation on an anchor residue of epitope necessary for binding on MHC class I is used by HIV to escape the immune system. Mutations inside or flanking an epitope may lead to T cell lack of recognition and CTL escape. Studying how immunodominance at epitopes drives the EC in a geographically dependent way with genetics and immunological elements orchestrating it may help future research on vaccines or immunotherapy for HIV.

When we can trust computers (and when we can't)

With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules

Major histocompatibility complex-I (MHC-I) molecules play a central role in the immune response to viruses and cancers. They present peptides on the surface of affected cells, for recognition by cytotoxic T cells. Determining which peptides are presented, and in what proportion, has profound implications for developing effective, medical treatments. However, our ability to predict peptide presentation levels is currently limited. Existing prediction algorithms focus primarily on the binding affinity of peptides to MHC-I, and do not predict the relative abundance of individual peptides on the surface of antigen-presenting cells in situ which is a critical parameter for determining the strength and specificity of the ensuing immune response. Here, we develop and experimentally verify a mechanistic model for predicting cell-surface presentation of competing peptides. Our approach explicitly models key steps in the processing of intracellular peptides, incorporating both peptide binding affinity and intracellular peptide abundance. We use the resulting model to predict how the peptide repertoire is modified by interferon-γ, an immune modulator well known to enhance expression of antigen processing and presentation proteins.