Genomewide Conserved Epitope Profiles of HIV-1 Predicted by Biophysical Properties of MHC Binding Peptides

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Short communication: association of HLA-A*1101 with resistance and B*4006 with susceptibility to HIV and HIV-TB: an in silico analysis of promiscuous T cell epitopes

We have shown the association of HLA-A*11 with resistance and HLA-B*40 and -DR2 with susceptibility to HIV and HIV-TB. In the present study, we performed high-resolution subtyping of HLA-A*11 and -B*40 to identify the subtype level association, using the polymerase chain reaction-based sequence-specific oligonucleotide probe method. Underrepresentation of HLA-A*1101 was observed in overall HIV [p(c) = 0.012, OR 0.42 (95% confidence interval (CI) 0.24-0.72)] and HIV(+)TB(+) [p(c) = 0.001, OR 0.18 (95% CI 0.06-0.46)] compared to healthy controls. Significantly higher frequencies of HLA-B*4006 were observed in overall HIV [p = 0.0001, p(c) = 0.004, OR 2.71 (95% CI 1.58-4.75)], HIV(+)TB(-) [p = 0.0003, p(c) = 0.008, OR 2.82 (95% CI 1.56-5.17)], and HIV(+)TB(+) [p = 0.003, p(c) = 0.086, OR 2.56 (95% CI 1.33-4.95)] compared to healthy controls. An in silico analysis of potential T cell epitopes of consensus Gag and Pol sequences of HIV-1 subtype C Indian strains revealed relatively higher number of promiscuous HLA-B40, HLA-DRB1*1501, and -DRB1*1502 (HLA-DR2)-restricted epitopes in contrast to limited numbers of promiscuous binders restricted by HLA-A*1101. The results suggest that HLA-A*1101 may be associated with protection against HIV and the development of TB in HIV patients while HLA-B*4006 may be associated with susceptibility to HIV and TB development in HIV patients. The present study also suggests that the extent of promiscuity of T cell epitopes of HIV-1 subtype C restricted by HLA alleles exerting opposing effects might differ.

Towards in silico design of epitope-based vaccines

BACKGROUND

Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered.

METHODS

Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs.

CONCLUSION

State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.

Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Background

A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the identification of appropriate peptides to effect this process. There are several algorithms currently in use for making such predictions, but these are limited to a small number of MHC molecules and have good but imperfect prediction power.

Results

We have undertaken an exploration of the power gained by taking advantage of a natural representation of the amino acids in terms of their biophysical properties. We used several well-known statistical classifiers using either a naive encoding of amino acids by name or an encoding by biophysical properties. In all cases, the encoding by biophysical properties leads to substantially lower misclassification error.

Conclusion

Representation of amino acids using a few important bio-physio-chemical property provide a natural basis for representing peptides and greatly improves peptide-MHC class I binding prediction.

Current situation in the development of a preventive HIV vaccine

The uncontrolled progression of the aids epidemic has made the development of an efficacious human immunodeficiency virus (HIV) vaccine a major objective of scientific research. No effective preventive vaccine against HIV is currently available and sterilizing immunity has not yet been achieved in animal models. This review analyses the major challenges in developing an aids vaccine, in particular the mechanisms involved in viral escape from the immune response, and summarizes the results obtained with the different prototypes of therapeutic and preventive vaccines. Finally, social, economic and healthcare aspects of research into HIV vaccines and current controversies regarding the development of clinical trials are discussed.

Situación actual en el desarrollo de una vacuna preventiva frente al VIH

El avance de la epidemia de sida ha convertido la obtención de una vacuna eficaz frente al virus de la inmunodeficiencia humana (VIH) como un objetivo científico prioritario. En el momento actual no disponemos de una vacuna preventiva frente a la infección por el VIH y en ningún modelo animal se ha conseguido la protección frente a la infección. En esta revisión se analizan las dificultades existentes en el desarrollo de una vacuna contra el sida, en especial los mecanismos de escape viral a la respuesta inmunitaria y se describen los prototipos de vacunas preventivas y terapéuticas en desarrollo y los resultados obtenidos. Por otra parte se sitúa esta investigación en el contexto sanitario, económico y social de la pandemia de sida y se analizan las polémicas actualmente planteadas en el desarrollo de ensayos clínicos con los diferentes tipos de vacunas.

Candidate epitope identification using peptide property models: application to cancer immunotherapy

Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.