DCTD: BIOMETRIC RESEARCH PROGRAM

Much research in the etiology and treatment of auto-immune diseases is directed toward identifying the self antigens that are the target of attack by T-cells and identifying the events that result in proliferation of such T-cell clones. Identifying tumor antigens and peptide epitopes that can elicit a T-cell response is also a key component in the development of therapeutic cancer vaccines. Drs. Simon, Zhao, Sung, Koo and Mr. Grover have been collaborating with Dr. Roland Martin's laboratory of neuroimmunology in the National Institute of Neurological Diseases and with Dr. Clemencia Pinilla of the Torry Pines Institute of Molecular Medicine on the use of combinatorial peptide libraries to provide data that enables the decryption of the motifs recognized by T-cell clones. We have developed statistical models that use the data from T-cell stimulation assays using combinatorial peptide libraries to estimate positional scoring matrices for predicting the potential of any peptide for stimulating the T-cell clone in an MHC dependent manner. More complex decision tree and neural network models have also been developed to facilitate understanding the principles of the peptide-MHC molecule-T cell receptor interactions. We have built a web based bioinformatic analysis system (TEST) that enables the searching of a translated version of Genbank (Genpept) to identify self peptides and pathogen peptides that may have served as mimics in the initial stimulation of the T-cell clone. In order to perform efficient searches, we maintain the current version of Genpept in our MySql database on our four processor Linux server. The models have been extended to use of combinatorial peptide libraries with other types of assays. T-cell clones from a variety of disease states, including cancers have been evaluated.

Prediction of immune response based on TCR stimulation models constructed from combinatorial peptide library assay and MHC binding

T cell receptors (TCR) recognize antigenic peptides in complex with the major histocompatibility complex (MHC) molecules and this trimolecular interaction initiates antigen-specific signaling pathways in the responding T lymphocytes. For the study of autoimmune diseases and vaccine development it is important to identify peptides (epitopes) that can stimulate a given TCR. The use of combinatorial peptide libraries has recently been introduced as a powerful tool for this purpose. A combinatorial library of n-mer peptides is a set of complex mixtures each characterized by one position fixed to be a specified amino acid and all other positions randomized. A given TCR can be fingerprinted by screening a variety of combinatorial peptide mixtures using a proliferation assay. We have developed statistical models for elucidating the recognition profile of a TCR using combinatorial library proliferation assay data and known MHC binding data. The model can be used to predict potential molecular mimic peptides that are recognized by the given TCR.

Prediction of MHC binding peptides with a model capturing their biophysical properties from a database of known binders

We have developed a new method for predicting MHC binding of peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and accommodates any inter-residue interactions. The method discovers the most common 9-mer "property profile" within the peptides in the publicly available database MHCPEP. The model can be used to predict MHC binding regions in genomes of a pathogen such as HIV, which may facilitate, for example, a rational development of vaccines.

Degenerate nature of T-cell recognition of peptide-MHC complexes

It is generally accepted that a high level of antigen specificity is required for T-cell activation in order to avoid potentially pathogenic immune responses to self antigens. On the other hand, T cells need to be widely crossreactive in order for a comprehensive response to a diverse spectrum of foreign antigens. We investigate how the immune system satisfies these two albeit conflicting requirements. A simple probabilistic model is extended in order to incorporate several known mechanisms of the immune system.

References:

Hemmer B, Gran B, Zhao Y, Marques A, Pascal J, Tzou A, Kondo T, Cortese I, Bielekova B, Straus S, McFarland HF, Houghten R, Simon R, Pinilla C, and Martin R. Identifiction of candidate T cell epitopes and molecular mimics in chronic Lyme disease. Nature Medicine 5:1375-82,1999.

Pinilla, C., Rubio-Godoy, V., Dutoit, V., Guillaume, P., Simon, R., Zhao, Y., Houghten, R. A., Cerottini, J., Romero, P, and Valmori, D. Combinatorial peptide libraries as an alternative approach to the identification of ligands for tumor reactive cytolytic T lymphocytes. Cancer Research 61:5153-5160, 2001.

Martin, R., Gran, B., Zhao, Y., Markovic-Plese, S., Bielekova, B., Marques, A., Sung, M., Hemmer, B., Simon, R., MaFarlan, H. F., and Pinilla, C. Molecular mimicry and antigen-specific T-cell response in multiple sclerosis and chronic CNS lyme disease. J. Autoimmunity 16:187-192, 2001.

Zhao, Y., Gran, B., Pinilla, C., Markovic-Plese, S., Hemmer, B., Tzou, A., Whitney, L. W., Biddison, W. E., Martin, R., and Simon, R. Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic T-cell receptors and MHC-peptide ligands. J. Immunol. 167:2130-3141, 2001.

Zhao, Y., Grovel, L., and Simon, R. TEST: a web-based T-cell Epitope Search Tool.Proceedings of the 14th IEEE symposium on computer-based medical systems, p.493- 497, 2001.

Rubio-Godoy, V., Pinilla, C., Dutoit, V., Borras, E., Simon, R., Zhao, Y., Cerottini, J., Romero, P, Houghten, R. A., and Valmori, D. Towards PS-SCL based identification of CD8+ tumor reactive T cell ligands: a comparative analysis of PS-SCLs recognition by a single tumor-reactive CD8+CTL clone1. Cancer Research, 2002, In press.

Sung, M., Zhao, Y., Martin, R., and Simon, R. T-cell epitope prediction with Combinatorial Peptide Libraries. J. of Computational Biology, 2002, In press.

Please send comments and suggestions to brb@brb.nci.nih.gov

Updated on Nov. 2, 2015