氧自由基曾被認為是有氧代謝过程的無用副產物。然而，在正常條件下，氧自由基也可以修飾蛋白質的結構與功能，同時能在多種細胞代謝過程中作為重要的信號分子。半胱氨酸的巰基極易被氧自由基、氮自由基以及其它的親電子分子所氧化，且其可逆的氧化反應對於與氧化還原相關的調控與信號傳遞是極其重要的。雖然僅有特定的一小部分半胱氨酸可以被氧化修飾，我們至今對於影響半胱氨酸對氧化還原敏感性的决定因素所知甚少。對於蛋白质半胱氨酸氧化修饰的比較分析及預測，不仅可以提高我們對半胱氨酸氧化還原敏感性的决定因素的認識，同時也能對後續关于重要的氧化還原敏感蛋白的實驗驗證起指導作用。 / 本研究中，本人主要做了如下三部分的工作：第一部分， RedoxDB數據庫的構建。这也是首個針對已被实验验证的蛋白氧化還原修飾的數據庫。第二部分，氧化還原敏感的半胱氨酸位點的特征分析及預測。本人分析了基於序列的各種可能與半胱氨酸氧化還原敏感性相關的特征，並發現其中三個特征可用於預測氧化還原敏感的半胱氨酸位點。基於這三個特征，我開發了RSCysPred 一个基於支持向量機且只依赖蛋白序列的預測氧化還原敏感的半胱氨酸位點的新方法。第三部分，可逆二硫鍵的分析。可逆二硫鍵的形成是蛋白質可逆氧化還原修飾的最主要的類型之一。我从RedoxDB中获取相关数据，然后針對功能位點標簽以及靜電特征等方面的特征，對可逆二硫鍵與結構二硫鍵進行了詳盡的比較分析。結果表明相比結構二硫鍵，可逆二硫鍵具有一些显著不同的特征。进一步的分析显示這些特征可用於可逆二硫鍵的預測。 / Reactive oxygen species (ROS) has been regarded as unwanted by-product of aerobic metabolism. However, under normal conditions, ROS can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation by ROS/RNS and other electrophilic molecules, and their reversible oxidation is of critical roles for redox regulation and signaling. Despite the fact that only a small fraction of cysteine residues undergone oxidative modification, the determinants of cysteine redox-sensitivity is poorly understood to date. Comparative analyses and computational prediction of protein cysteine oxidative modification will not only enhance our understanding about the determinants of cysteine redox-sensitivity, it may also facilitate further experimental investigation of important redox proteins. / This thesis includes the following three parts of work. Part I: Construction of RedoxDB - a curated database of protein oxidative modification. It is the first database for experimentally verified protein oxidative modification events. Part II: Feature analysis and computational prediction of redox-sensitive cysteines. I analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three features for efficient computational prediction of redox-sensitive cysteine. Based on these features, I developed RSCysPred, a SVM-based tool for predicting redox-sensitive cysteine using primary protein sequence only. Part III: Study on reversible disulfide formation, which is the major type of protein oxidative modification. By using data retrieved from RedoxDB, I performed extensive comparison between reversible disulfide-bonded cysteines and structural disulfide-bonded cysteines with regard to the functional site signatures and electrostatic properties. The results support that reversible disulfides show several remarkable differences compared with structural disulfides. Further analyses indicated that these features could be used for efficient prediction of reversible disulfide. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Sun, Mingan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 77-89). / Abstract also in Chinese. / Thesis/Assessment Committee --- p.I / Statement: --- p.I / Abstract of thesis entitled --- p.II / 摘要 --- p.IV / Acknowledgements --- p.V / Table of contents --- p.VI / List of Tables --- p.IX / List of Figures --- p.X / List of Abbreviations --- p.XI / CHAPTER 1 --- p.1 / General Introduction --- p.1 / Chapter 1.1 --- Generation of ROS and oxidative stress --- p.2 / Chapter 1.2 --- Protein cysteine oxidative modification --- p.3 / Chapter 1.2.1 --- Protein oxidative modification --- p.3 / Chapter 1.2.2 --- Special properties of cysteine --- p.3 / Chapter 1.2.3 --- Reversible cysteine oxidation and its function --- p.4 / Chapter 1.2.4 --- Specificity of cysteine oxidative modification --- p.5 / Chapter 1.3 --- Experimental approaches for detecting protein oxidative modification --- p.6 / Chapter 1.4 --- Computational prediction of protein oxidative modification --- p.7 / Chapter 1.5 --- Project Objectives --- p.8 / CHAPTER 2 --- p.11 / RedoxDB: a Curated Database of Experimentally Verified Protein Oxidative Modification --- p.11 / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Database concept --- p.13 / Chapter 2.3 --- Database interface and tools --- p.15 / Chapter 2.4 --- Discussion --- p.16 / CHAPTER 3 --- p.23 / Computational Prediction of Redox-Sensitive Cysteines Using Sequential Distance and Other Sequence-based Features --- p.23 / Chapter 3.1 --- Introduction --- p.24 / Chapter 3.2 --- Methods and Materials --- p.26 / Chapter 3.2.1 --- Datasets --- p.26 / Chapter 3.2.2 --- Feature extraction --- p.28 / Chapter 3.2.3 --- Support vector machines (SVMs) implementation and parameter optimization --- p.29 / Chapter 3.2.4 --- Performance assessment --- p.30 / Chapter 3.2.5 --- Statistical analyses --- p.30 / Chapter 3.3.6 --- Webserver implementation --- p.30 / Chapter 3.3 --- Results --- p.31 / Chapter 3.3.1 --- Performance evaluation by 10-fold cross-validation --- p.31 / Chapter 3.3.2 --- Evaluation of the most efficient features --- p.33 / Chapter 3.3.3 --- Comparison with current structure-based method --- p.34 / Chapter 3.3.4 --- Performance evaluation using OSCTdb --- p.36 / Chapter 3.4 --- Discussion --- p.37 / CHAPTER 4 --- p.49 / Comparative Analysis of Reversible and Structural Disulfides to Reveal Their Distinct Characteristics --- p.49 / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Materials and methods --- p.52 / Chapter 4.2.1 --- Datasets --- p.52 / Chapter 4.2.2 --- Functional site signatures --- p.53 / Chapter 4.2.3 --- Electrostatic properties --- p.55 / Chapter 4.2.4 --- Support vector machines (SVMs) implementation and parameter optimization --- p.56 / Chapter 4.2.5 --- Performance assessment --- p.56 / Chapter 4.2.6 --- Statistical analyses --- p.56 / Chapter 4.3 --- Results --- p.56 / Chapter 4.3.1 --- General features of the functional site signatures for reversible-SS Cys and structural-SS Cys --- p.56 / Chapter 4.3.2 --- Differences in amino acid composition are related to several physical-chemical properties --- p.58 / Chapter 4.3.3 --- Electrostatic characteristics of disulfide-bonded Cys --- p.60 / Chapter 4.3.4 --- Comparison of S-S distance --- p.61 / Chapter 4.3.5 --- Predictive power of newly identified features --- p.62 / Chapter 4.4 --- Discussion --- p.63 / Chapter 4.4.1 --- Reversible disulfides show distinct characteristics compared with structural disulfides --- p.63 / Chapter 4.4.2 --- Chances and challenges for computational prediction of reversible disulfides --- p.64 / CHAPTER 5 --- p.79 / Conclusions and Perspectives --- p.79 / Chapter 5.1 --- Contributions and conclusions from this thesis research --- p.80 / Chapter 5.2 --- Future perspectives --- p.81 / Reference --- p.82 / List of Publications --- p.95

The past few decades have witnessed a revolution in recombinant DNA and nucleic acid sequencing technologies. Recently however, technologies capable of massively high-throughout, genome-wide data collection, combined with computational and statistical tools for data mining, integration and modeling have enabled the construction of predictive networks that capture cellular regulatory states, paving the way for ‘Systems biology’. Consequently, protein interactions can be captured in the context of a cellular interaction network and emergent ‘system’ properties arrived at, that may not have been possible by conventional biology. The ability to generate data from multiple, non-redundant experimental sources is one of the important facets to systems biology. Towards this end, we have established a novel platform called ‘spotted cell microarrays’ for conducting image-based genetic screens. We have subsequently used spotted cell microarrays for studying multidimensional phenotypes in yeast under different regulatory states. In particular, we studied the response to mating pheromone using a cell microarray comprised of the yeast non-essential deletion library and analyzed morphology changes to identify novel genes that were involved in mating. An important aspect of the mating response pathway is large-scale spatiotemporal changes to the proteome, an aspect of proteomics, still largely obscure. In our next study, we used an imaging screen and a computational approach to predict and validate the complement of proteins that polarize and change localization towards the mating projection tip. By adopting such hybrid approaches, we have been able to, not only study proteins involved in specific pathways, but also their behavior in a systemic context, leading to a broader comprehension of cell function. Lastly, we have performed a novel metabolic starvation-based screen using the GFP-tagged collection to study proteome dynamics in response to nutrient limitation and are currently in the process of rationalizing our observations through follow-up experiments. We believe this study to have implications in evolutionarily conserved cellular mechanisms such as protein turnover, quiescence and aging. Our technique has therefore been applied towards addressing several interesting aspects of yeast cellular physiology and behavior and is now being extended to mammalian cells. / text

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.