AUTOMATED MOTIF DETERMINATION IN ANTIGENIC PEPTIDES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 多肽存在于与主要组织相容性复合体(MHC)结合的细胞表面。这些复合体被T细胞询问以确定免疫反应。多肽序列的谱系由多肽与单个MHC(I类或II类和特定等位基因)的结合常数以及细胞处理机制(分解代谢酶、伴侣)决定。确定这些多肽的序列基序对于寻找触发自身免疫性疾病的多肽和蛋白质以及开发疫苗具有重要意义。 基序的确定包括使用纳米高效液相色谱和串联质谱仪从含有特定MHC的细胞系中分离和测序大量的多肽。人工对长长的多肽列表进行分类，提出基序假设，并使用突变多肽的结合研究进行测试。如果有的话，可以通过研究络合物的晶体结构来提炼基序。这一过程可能非常耗时耗力。此外，结合研究没有考虑其他细胞选择机制的影响。 提出了一种寻找序列基序的新方法。它的基础是使用分离的多肽序列自动找到它。使用计算机程序，在寻找平均序列时最大化所有肽的SW局部比对(具有仿射间隙)。该平均序列用于寻找多肽与复合体的结合核心长度，并将每个多肽序列与复合体中蛋白质的正确结合口袋进行比对。对每个口袋中氨基酸含量的统计分析揭示了这一主题。替代矩阵和空位惩罚等比对参数的影响正在调查中。此外，还研究了一种聚类算法在剔除离群点和揭示多个主题方面的作用。

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.