【作者】 薛宇；

【导师】 姚雪彪；

【作者基本信息】 中国科学技术大学 ， 细胞生物学， 2006， 博士

【摘要】 在本文中，我对过去五年来在蛋白质翻译后修饰组的生物信息学方面所取得的进展作一简要的介绍。我的主要研究工作为蛋白质翻译后修饰及细胞信号通路的生物信息学。各种功能分子例如磷酸基团、脂基团等通过与蛋白质上特殊的氨基酸形成共价键的方式结合，能够显著的改变该蛋白质的生化特性，并在许多的细胞生理过程中发挥着重要的作用。尽管目前已有超过350种的蛋白质翻译后修饰被发现，由于实验研究手段欠佳和数据的零散不齐，仅有很少的几种得到了较为系统的描述。通过实验的方法来鉴定蛋白质的翻译后修饰位点常常需要耗费大量的精力，并且酶反应的优化时一个极为耗时的过程，从而严重的制约了相关性研究的进展速度。通过计算预测的方法进行预先的分析，能够大大削减可供参考的靶位的数目，对于进一步的体内或体外的实验验证有指导和帮助的意义。 之前有研究者通过计算的手段来研究一些翻译后修饰，例如，磷酸化、糖基化、硫酸盐化和肉豆蔻酸化等。然而，最近实验生物学的发展指出了这些工具的预测准确性有待提高。为此，我们开发了更为精确的计算模型并使用了更为有效的算法来推进该领域的研究。除了磷酸化，我们还考虑了其他的一些较新的翻译后修饰，例如SUMO化、棕榈酰化以及赖氨酸／精氨酸甲基化等。我们开发设计了一些简单易用的在线工具。例如，我们使用GPS和贝叶斯决策理论算法，分别构建了预测蛋白质磷酸化位点的GPS和PPSP网站。我们使用了CSS算法来预测蛋白质棕榈酰化位点。并且，我们使用支持向量学习机的算法，开发设计了在线工具MeMo来预测蛋白质的赖氨酸／精氨酸甲基化的位点。另外，我们使用GPS和Motifx的方法构建了预测SUMO位点的SUMOsp网站。我们还对SUMO化的底物做了功能分步性的分析。更多的相关工作正在整理发表过程中。 我们还开展了一些有趣的与细胞生物物学密切相关的工作，包括鉴定驱动蛋白质超家族以及构建中体、中心体和动点的整合数据库。我坚信我们的工作会将蛋白质翻译后修饰的研究推向一个新的高度。更多还原

【Abstract】 In the Ph.D. thesis, I give a brief introduction of my scientific progress on bioinformatics during the past five years. My major interest is bioinformatics of post-translational modifications (PTMs) of protein and cell signaling pathways. By covalently attaching to individual amino acids various functional molecules such as phosphates, lipids, or proteins, post-translational modifications alter a protein’s biochemical nature significantly, and play key roles in a wide variety of cellular processes. Although more than 350 types of PTMs have been discovered, only a few of them have been well-characterized due to the lack of sufficient data for analyses. Experimental identification of proteins’ PTM sites is labor-intensive and often limited by the availability and optimization of enzymatic reaction. In silico prediction could be a promising strategy to conduct preliminary analyses and greatly reduce the number of potential targets that need further in vivo or in vitro confirmation.Previously, several types of PTMs have been investigated using computational approaches, e.g. phosphorylation, glycosylation, sulfation and myristoylation, etc. However, the prediction performances of these programs still remain to be improved. We focused on developing more rigorous computational models and employing more efficient algorithms to enhance the research of PTMs. Besides the well-know PTM of phosphorylation, we also considered several other new PTMs, including sumoylation, palmitoylation and Lysine/Arginine methylation, etc. We developed several easy-to-use online web tools. For example, we construct GPS and PPSP for phosphorylation site prediction, based on GPS and Bayesian Decision Theory algorithms, respectively. And we deployed the CSS approach to predict the palmitoylation site. Also, we developed the online tool of MeMo to prediction Lysine/Arginine methylation site, with SVMs algorithm. Moreover, we construct an online tool of SUMOsp to predict sumoylation site, with GPS and Motifx approaches. We also surveyed the the functional diversity of SUMO substrates. And more analyses will be available in the future.更多还原