AUTOMATED MOTIF DETERMINATION IN ANTIGENIC PEPTIDES

点击翻译按钮获取中文摘要

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Peptides are found on the surface of cells bound to the Major Histocompatibility Complex (MHC). These complexes are interrogated by T-cells to determine an immune response. The repertoire of the peptides sequences is determined by the binding constant of the peptide to the individual MHC (class I or II and the particular allele) and by the cell processing mechanisms (catabolic enzymes, chaperons). Determination of a sequence motif to these peptides has implications in finding peptides and proteins that trigger autoimmune diseases and in developing vaccins. Motif determination involves isolating and sequencing large number of peptides using nano-HPLC and tandem mass spectrometry from a cell line bearing a particular MHC. The long lists of peptides are sorted manually, motif hypothesises are suggested and tested using binding studies of mutated peptides. The motif can be refined by studying crystal structures of the complex, if it is available. This process can be very time and labor consuming. Also, binding studies don''t take into account the influence of other cell selection mechanisms. A new method for finding a sequence motif is suggested. It is based on finding it automatically using the isolated peptides sequences. Using a computer program the SW local alignment (with affine gap) of all peptides is maximized in finding of an average sequence. This average sequence is used in finding the binding core length of the peptide to the complex and to align each peptide sequence with the correct binding pockets of the protein in the complex. Statistical analysis of the amino acid content of each pocket reveals the motif. The influence of alignment parameters such as substitution matrixes and gap penalties is under investigation. Also the utility of a clustering algorithm to eliminate outliers and reveal multiple motifs is studied.