AUTOMATED MOTIF DETERMINATION IN ANTIGENIC PEPTIDES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 在与主要组织相容性复合物（MHC）结合的细胞表面上发现肽。这些复合物被T细胞询问以确定免疫应答。肽序列的库由肽与单个MHC（I类或II类和特定等位基因）的结合常数和细胞加工机制（分解代谢酶、伴侣）决定。确定这些肽的序列基序对发现触发自身免疫性疾病的肽和蛋白质以及开发疫苗具有意义。 基序测定涉及使用纳米HPLC和串联质谱法从携带特定MHC的细胞系中分离和测序大量肽。人工排序的肽的长列表，基序假设的建议和测试使用突变的肽的结合研究。如果可以的话，可以通过研究复合物的晶体结构来细化基序。这个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模体的新方法。它基于使用分离的肽序列自动找到它。使用计算机程序，所有肽的SW局部比对（具有仿射空位）在找到平均序列时被最大化。该平均序列用于发现肽与复合物的结合核心长度，并将每个肽序列与复合物中蛋白质的正确结合口袋进行比对。每个口袋的氨基酸含量的统计分析揭示了基序。比对参数如置换矩阵和空位罚分的影响正在研究中。此外，效用的聚类算法，以消除离群值，揭示多个图案进行了研究。

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.