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

The rate of progression of HIV infected individuals to AIDS is known to vary with the genotype of the host, and is linked to their allele of human leukocyte antigen (HLA) proteins, which present protein degradation products at the cell surface to circulating T-cells. HLA alleles are associated with Gag-specific T-cell responses that are protective against progression of the disease. While Pol is the most conserved HIV sequence, its association with immune control is not as strong. To gain a more thorough quantitative understanding of the factors that contribute to immunodominance, we have constructed a model of the recognition of HIV infection by the MHC class I pathway. Our model predicts surface presentation of HIV peptides over time, demonstrates the importance of viral protein kinetics, and provides evidence of the importance of Gag peptides in the long-term control of HIV infection. Furthermore, short-term dynamics are also predicted, with simulation of virion-derived peptides suggesting that efficient processing of Gag can lead to a 50% probability of presentation within 3 hours post-infection, as observed experimentally. In conjunction with epitope prediction algorithms, this modelling approach could be used to refine experimental targets for potential T-cell vaccines, both for HIV and other viruses

TAPBPR: a new player in the MHC class I presentation pathway.

In order to provide specificity for T cell responses against pathogens and tumours, major histocompatibility complex (MHC) class I molecules present high-affinity peptides at the cell surface to T cells. A key player for peptide loading is the MHC class I-dedicated chaperone tapasin. Recently we discovered a second MHC class I-dedicated chaperone, the tapasin-related protein TAPBPR. Here, we review the major steps in the MHC class I pathway and the TAPBPR data. We discuss the potential function of TAPBPR in the MHC class I pathway and the involvement of this previously uncharacterised protein in human health and disease.C.H was supported by a Wellcome Trust PhD Studentship (Grant 089563) and L.H.B was funded by a Wellcome Trust Career Development Fellowship (Grant 085038).This is the author accepted manuscript. The final published version is available via Wiley at http://onlinelibrary.wiley.com/doi/10.1111/tan.12538/abstract;jsessionid=3D6AF64F5BD8C64E84634A4303842BE2.f04t01

Selector function of MHC I molecules is determined by protein plasticity

The selection of peptides for presentation at the surface of most nucleated cells by major histocompatibility complex class I molecules (MHC I) is crucial to the immune response in vertebrates. However, the mechanisms of the rapid selection of high affinity peptides by MHC I from amongst thousands of mostly low affinity peptides are not well understood. We developed computational systems models encoding distinct mechanistic hypotheses for two molecules, HLA-B*44:02 (B*4402) and HLA-B*44:05 (B*4405), which differ by a single residue yet lie at opposite ends of the spectrum in their intrinsic ability to select high affinity peptides. We used <em>in vivo</em> biochemical data to infer that a conformational intermediate of MHC I is significant for peptide selection. We used molecular dynamics simulations to show that peptide selector function correlates with protein plasticity, and confirmed this experimentally by altering the plasticity of MHC I with a single point mutation, which altered <em>in vivo</em> selector function in a predictable way. Finally, we investigated the mechanisms by which the co-factor tapasin influences MHC I plasticity. We propose that tapasin modulates MHC I plasticity by dynamically coupling the peptide binding region and {\alpha}<sub>3</sub> domain of MHC I allosterically, resulting in enhanced peptide selector function

Extraction and Biochemical Characterization of Sulphated Glycosaminoglycans from Chicken Keel Cartilage

The present study was conducted to explore the potential and cheaper source of major and abundantly found sulphated glycosaminoglycans (GAGs) in chicken keel cartilage. Chicken is comparatively readily accessible to all the communities of Pakistan and its cartilages are the rich source of sulphated GAGs. The GAGs were extracted from prewashed and ground keel cartilages (n=3) of chicken using 3 M MgCl2, dialyzed, digested with papain, precipitated with three volumes of ethanol, and finally lyophilized to dry powder. The dry products were used for proximate analysis (carbohydrates 65.49±0.10, crude protein 12.82±0.26, ash 11.12±.56, moisture 9.88±0.32 and fat 0.69±0.14%). Dimethylmethylene blue binding (DMMB) assay was performed to determine the quantity of total GAGs in each group of product and protein contents were estimated by Bradford method. Identification of extracted samples of GAGs was performed with FTIR spectrometer using KBr disc and purity of the samples was determined by SDS-PAGE. Quantity of total GAGs in extracted samples was 70.77±2.27% and estimated amount of protein was 4.64±0.29%. FTIR spectra of standard and samples of CS showed identical and characteristic peaks in finger print region. Finger print region revealed the presence of C-O-S, S=O, -COO, -C-C, R-SO2–R, -CONH2 and R-SO2-NH2 molecules. SDS-PAGE analysis revealed the presence of 77.8 and 50.5 kDa proteins in all extracted samples of GAGs. It can be concluded that chicken keel cartilage is the potential and cheap source of GAGs. Analysis by SDS-PAGE revealed that most of the non-collagen protein can be removed by three volumes of solvent extraction and FTIR is an advance technique for identification of GAGs in mid infrared region (400-4000 cm-1)

A mechanistic model predicting cell surface presentation of peptides by MHC class I proteins, considering peptide competition, viral intracellular kinetics and host genotype factors

Major histocompatability complex class I (MHC-I) proteins present short fragments of pathogenic or cancerous proteins (peptides) on the surface of infected cells for recognition by T lymphocytes which are stimulated upon recognition of foreign peptides. Due to the diversity of peptide sequences and the sequence-specificity of MHC-I alleles, being able to determine which peptides will be presented by which MHC-I alleles and in what proportion could be important for the development of vaccines and treatments based on the presented peptiodome. Machine learning tools, trained on experimental data, are widely used to predict immunogenic peptides. However they are unable to account for the impact the intracellular kinetics of the pathogenic or cancerous protein which will greatly influence the resultant peptidome. Here we describe a mechanistic model of peptide presentation, validated against experimental data, which accounts for intracellular peptide concentration, and can predict the relative cell surface presentation of competing peptides with varying affinities for MHC-I proteins. We demonstrate how combining this mechanistic model with the intracellular kinetics of HIV proteins can provide insight in to the experimentally reported immunogenicity of the viral protein Gag, and show how such a model can be used to predict the most abundant viral peptides presented on the cell surface. Similarly, we predict the HeLa cell peptidome and demonstrate how a simple metric can be used to approximate the abundance of a peptide based solely on protein synthesis and degradation, peptide-MHC affinity and proteasomal cleavage

Dynamic modelling of the processing of peptides for presentation on major histocompatibility complex class I proteins

Antigen presentation is broadly implicated in disease and represents an important target for prophylactic and therapeutic treatments. A better understanding of the components of this system is fundamental to our understanding of disease path- ways and to treatment design. This thesis focuses on modelling the processing of peptides by enzymes in the cytosol and in the endoplasmic reticulum (ER) in the context of major histocompatibility complex class I (MHC) antigen presentation, and expounds upon current knowledge of the mechanistic details and specificity of both the proteasome and the endoplasmic reticulum aminopeptidase-1 (ERAP1). We use nonlinear ordinary differential equations to model the biochemical reaction pathways of amino-terminal peptide trimming by ERAP1 and distinguish parameter dependencies of two prevailing theories for the mechanism of ERAP1 trimming us- ing algebraic and numerical analysis. Importantly, we show that ERAP1 has a role in peptide optimisation when MHC acts as a template, but not when it trims free peptide using an internal molecular ruler. We present testable hypotheses that may elucidate the dominant trimming mechanism used by ERAP1 in vivo, which has been the subject of debate for more than 25 years. We show that all ERAP1 trimming mechanism hypotheses are able to predict the qualitative distribution of cell surface presentation of SIINFEKL derived from amino-terminally extended precursors. Notably, we find that the molecular ruler trimming mechanism is more robust than the MHC-as-template mechanism. Finally, we use neural networks to predict carboxyl-terminal cleavage by the proteasome, and demonstrate that we are able to distinguish between cleavage and non-cleavage sites on an unseen set of known peptide epitopes. Overall, this thesis contributes a more thorough quantitative and mechanistic understanding of the generation of peptides presented on MHC class I molecules

The Relationship Between Inhibition, Conformation, and Catalysis of the Aminopeptidase ERAP1

ERAP1 is an aminopeptidase that is a component of antigen processing. To distinguish the role of ERAP1 from homologs ERAP2 and IRAP, I identified three specific ERAP1 inhibitors via a high-throughput screen. These compounds inhibit hydrolysis of a decamer peptide, and some inhibit ERAP1 in a cellular assay. These inhibitors enable dissection of ERAP1 mechanism. ERAP1 has been crystallized in two conformations: open and closed. I collected SAXS data on ERAP1 in the presence of various inhibitors. ERAP1 adopts an open conformation in solution, but some inhibitors stabilize the closed form. Compound 3 docks to a distal pocket 28Å from the active site zinc, while DG013 and DG014 bind to the active site. This distal pocket is an allosteric activation site, and allostery is mediated by stabilizing the closed state. I also identified an intermediate step in substrate binding where helix 4a becomes ordered while ERAP1 maintains an open conformation. Helix 4a then rotates and engages substrate when ERAP1 closes. The nonsynonymous SNP rs30187 at position 528 (Lys/Arg) subtly alters ERAP1 activity in vitro and correlates with disease incidence. Position 528 forms a conformation-dependent electrostatic interaction with Glu913 in the closed structure. The energetic contribution of this interaction is stronger for Lys528 than Arg528. Inhibitors that induce closing are more potent for Lys528 than Arg528. I propose a model where either helix 4a stabilization or allosteric site occupancy shift the conformational equilibrium towards a closed state, while substitution at position 528 alters the opening rate

Discovering discriminative and class-specific sequence and structural motifs in proteins

Finding recurring motifs is an important problem in bioinformatics. Such motifs can be used for any number of problems including sequence classi cation, label prediction, knowledge discovery and biological engineering of proteins t for a speci c purpose. Our motivation is to create a better foundation for the research and development of novel motif mining and machine learning methods that can extract class-speci c and discriminative motifs using both sequence and structural features. We propose the building blocks of a general machine learning framework to act on a biological input. This thesis present a combination of elements that are aimed to be applicable to a variety of biological problems. Ideally, the learner should only require a number of biological data instances as input that are classi- ed into a number of di erent classes as de ned by the researchers. The output should be the factors and motifs that discriminate between those classes (for reasonable, non-random class de nitions). This ideal work ow requires two main steps. First step is the representation of the biological input with features that contain the signi cant information the researcher is looking for. Due to the complexity of the macromolecules, abstract representations are required to convert the real world representation into quanti able descriptors that are suitable for motif mining and machine learning. The second step of the proposed work ow is the motif mining and knowledge discovery step. Using these informative representations, an algorithm should be able to nd discriminative, class-speci c motifs that are over-represented in one class and under-represented in the other. This thesis presents novel procedures for representation of the proteins to be used in a variety of machine learning algorithms, and two separate motif mining algorithms, one based on temporal motif mining, and the other on deep learning, that can work with the given biological data. The descriptors and the learners are applied to a wide range of computational problems encountered in life sciences

Computational immunogenetics in allogeneic immunotherapy

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