Knowledge of protein-protein interactions (PPIs) is essential to understanding regulatory processes in a cell. High-throughput experimental methods have made significant contributions to PPI determination, but they are known to have many false positives and fail to identify a signification portion of bona fide interactions. The same is true for the many computational tools that have been developed. Significantly, although protein structures provide atomic details of PPIs, they have had relatively little impact in large-scale PPI predictions and there has been only limited overlap between structural and systems biology. Here in this thesis, I present our progress in combining structural biology and systems biology in the context of studies analyzing, coarse-grained modeling and prediction of protein-protein interactions. I first report a comprehensive analysis of the degree to which the location of a protein interface is conserved in sets of proteins that share different levels of similarities. Our results show that while, in general, the interface conservation is most significant among close neighbors, it is still significant even for remote structural neighbors. Based on this finding, we designed PredUs, a method to predict protein interface simply by "mapping" the interface information from its structural neighbors (i.e., "templates") to the target structure. We developed the PredUs web server to predict protein interfaces using this "template-based" method and a support vector machine (SVM) to further improve predictions. The PredUs webserver outperforms other state-of-the-art methods that are typically based on amino acid properties in terms of both prediction precision and recall. Meanwhile, PredUs runs very fast and can be used to study protein interfaces in a high throughput fashion. Maybe more importantly, it is not sensitive to local conformational changes and small errors in structures and thus can be applied to predict interface of protein homology models, when experimental structures are not available. I then describe a novel structural modeling method that uses geometric relationships between protein structures, including both PDB structures and homology models, to accurately predict PPIs on a genome-wide scale. We applied the method with considerable success to both the yeast and the human genomes. We found that the accuracy and the coverage of our structure-based prediction compare favorably with the methods derived from sequence and functional clues, e.g. sequence similarity, co-expression, phylogenetic similarity, etc. Results further improve when using a naive Bayesian classifier to combine structural information with non-structural clues (PREPPI), yielding predictions of comparable quality to high-throughput experiments. Our data further suggests that PREPPI predictions are substantially complementary to those by experimental methods thus providing a way to dissect interactions that would be hard to identify on a purely high-throughput experimental basis. We have for the first time designed a "template-based" method that predicts protein interface with high precision and recall. We have also for the first time used 3D structure as part of the repertoire of experimental and computational information and find a way to accurately infer PPIs on a large scale. The success of PredUs and PREPPI can be attributed to the exploitation of both the information contained in imperfect models and the remote structure-function relationships between proteins that have been usually considered to be unrelated. Our results constitute a significant paradigm shift in both structural and systems biology and suggest that they can be integrated to an extent that has not been possible in the past.

This thesis focuses on an important aspect of protein functionality – protein-protein interactions (PPI). Three physical chemistry techniques for or derived from protein-protein interaction investigation are discussed. First, in Chapter 2, we demonstrate a new fluorescent imaging technique that creates high-order nonlinear signals by harnessing the frustrated fluorescence resonance energy transfer (FRET) – energy transfer between certain proteins close in proximity which is commonly used in PPI studies. In Chapter 3, we combine fluorescence resonance energy transfer (FRET) and bioluminescence resonance energy transfer (BRET), two most commonly used approaches to monitor protein-protein interactions in vivo, to create a novel hybrid strategy, bioluminescence assisted switching and fluorescence imaging (BASFI), which integrates the advantages of FRET and BRET. We demonstrate BASFI with Dronpa-RLuc8 fusion constructs and drug-inducible intermolecular FKBP-FRB protein-protein interactions in live cells with high sensitivity, resolution, and specificity. Finally, in Chapter 4, we propose a systematic free energy perturbation (FEP) protocol to computationally calculate the binding affinities between proteins. We demonstrate our protocol with the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class and analyze antibody residues’ contributions to the binding which further provides insights for antibody design.

P-glycoprotein (P-gp or ABCB1) is a membrane-bound active transporter belonging to the ABC protein superfamily. It is responsible for xenobioIc efflux and also contributes to multidrug resistance in diverse diseases including cancer and epilepsy. P-gp has been increasingly recognised as a potential target for future therapeutics. Although the protein has been studied for decades, understanding of the P-gp transport mechanism is still incomplete. Two P-gp orthologues, mouse (m) and human (h), were therefore expressed in yeasts and purified in the presence of the detergent, n-Dodecyl-β-D- Maltoside (DDM). Purified proteins were examined for aggregation and monodispersity via dynamic light scattering (DLS) and their thermal stability was determined by an assay using a thiol-specific dye (CPM). ATPase activity, measured in a detergent environment, showed that the proteins were active with a basal activity of 60 ± 4 and 35 ± 3 nmol/min/mg for mP-gp and hP-gp, respectively. Crystallisation trials were conducted in the presence of nucleotide. In meso crystallisation using commercial monoolein pre- dispensed plates yielded hexagonal crystal-like objects however they failed to diffract X- rays. P-gp samples were also subjected to cryo-EM where mP-gp in the post-hydrolytic (ADP-bound, vanadate-trapped) state provided the highest resolution dataset that led to a reconstruction of 3D density map at the resolution of 7.9 Å which showed an inward- facing conformation. Rigid-body model fitting unveiled densities that were not accounted for by the fitted model illustrating new features such as bound ADP, extended NBD1- TMD2 linker and alternative allocrite-binding sites. Ultimately, the knowledge of P-gp conformation alteration was enhanced and a refined alternating access mechanism of P- gp was proposed based upon information derived from this study.

Research into the relationship between the structure and function of proteins has been ongoing now for several decades. More recently, there has been an explosion in the investigation of the dynamic properties of proteins, and how their dynamic propensity relates to their function. This new direction in protein research requires new techniques to analyse protein dynamics, since most traditional techniques are biased towards a fixed tertiary structure. Mass spectrometry (MS) is emerging as a powerful tool to probe protein dynamics since it can provide information on interconverting conformations and has no preference towards the folded state. Furthermore, its low sample consumption, rapid data acquisition and low data processing positions MS as an attractive tool in protein structure research. The hybrid technique of ion mobility-mass spectrometry provides further insight into the range of conformations adopted by proteins and protein complexes, by providing information on the size in terms of rotationally averaged collision cross section. The work presented in this thesis considers proteins with a range of structural characteristics. We use ion mobility mass spectrometry to investigate proteins of different extents of disorder, protein complexes with dynamic entities and a system that undergoes structural rearrangement upon ligand binding. First, a framework of mass spectrometry experiments is described which allows identification of the extent of structure and disorder within proteins. This framework is tested on a range of different systems throughout the thesis. Differences in the gas-phase properties of two conformationally dynamic proteins which behave similarly in solution are investigated and from this research we postulate a new ionisation mechanism for partially folded proteins. The dynamic propensity of C-terminal p27 is investigated and compared to two permutants which allows us to delineate how the location of charged residues in a primary sequence affects the structure of a protein. We monitor the 'folding-upon-binding' behaviour of p27 upon association with its binding partners, and how this differs with the order of charged residues in the linear sequence. Finally, we describe the structural rearrangement of Fdc1 upon the binding of its cofactor; a prenylated FMN molecule. This thesis demonstrates the suitability of ion mobility-mass spectrometry for the investigation of dynamic properties of proteins and protein complexes.

Leung Tak Yuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 57-63). / Abstracts in English and Chinese. / Acknowledgments --- p.i / Abstract --- p.ii / Abbreviations --- p.iii / Abbreviations of amino acids --- p.iv / Abbreviations of nucleotides --- p.iv / Naming system for TRP mutants --- p.v / Chapter Chapter 1 --- I ntroduction / Chapter 1.1 --- Hyperthermophile and hyperthermophilic proteins --- p.1 / Chapter 1.2 --- Hyperthermophilic proteina are highly similar to their mesophilic homologues --- p.2 / Chapter 1.3 --- Hyperthermophilic proteins and free energy of stabilization --- p.3 / Chapter 1.4 --- Mechanisms of protein stabilization --- p.4 / Chapter 1.5 --- The difference in protein stability between mesophilic protein and hyperthermophilic protein --- p.4 / Chapter 1.6 --- Ribosomal protein L30e from T. celer can be used as a model system to study thermostability --- p.9 / Chapter 1.7 --- Protein engineering of TRP --- p.10 / Chapter 1.8 --- Purpose of the present study --- p.12 / Chapter Chapter 2 --- Materials and Methods / Chapter 2.1 --- Bacterial strains --- p.13 / Chapter 2.2 --- Plasmids --- p.13 / Chapter 2.3 --- Bacterial culture media and solutions --- p.13 / Chapter 2.4 --- Antibiotic solutions --- p.13 / Chapter 2.5 --- Restriction endonucleases and other enzymes --- p.14 / Chapter 2.6 --- M9ZB medium --- p.14 / Chapter 2.7 --- SDS-PAGE --- p.14 / Chapter 2.8 --- Alkaline phosphatase buffer --- p.15 / Chapter 2.9 --- DNA agarose gel --- p.15 / Chapter 2.10 --- "Gel loading buffer, DNA" --- p.16 / Chapter 2.11 --- "Ethidium bromide (EtBr), lOmg/ml" --- p.16 / Chapter 2.12 --- Constructing mutant TRP genes --- p.16 / Chapter 2.12.1 --- Polymerase Chain Reaction (PCR) --- p.17 / Chapter 2.12.2 --- Gel electrophoresis --- p.19 / Chapter 2.12.3 --- DNA purification from agarose gel --- p.19 / Chapter 2.12.4 --- "Construction of R39A, R39M, K46A, K46M, E47A, E50A, R54A, R54M" --- p.19 / Chapter 2.12.5 --- "Construction of double mutant R39A/E62A, R39M/E62A" --- p.20 / Chapter 2.13 --- Sub-cloning --- p.21 / Chapter 2.13.1 --- Restriction digestion --- p.22 / Chapter 2.13.2 --- Ligation vector with mutant TRP gene insert --- p.22 / Chapter 2.13.3 --- Amplifying vector carrying mutant TRP gene insert --- p.22 / Chapter 2.13.4 --- Mini-preparation of DNA --- p.22 / Chapter 2.13.5 --- Preparations of competent cells --- p.23 / Chapter 2.13.6 --- Transformation of Escherichia coli --- p.24 / Chapter 2.13.7 --- Screening tests --- p.25 / Chapter 2.14 --- Over expression of mutant TRP --- p.26 / Chapter 2.14.1 --- Transformation --- p.26 / Chapter 2.14.2 --- Expression --- p.26 / Chapter 2.14.3 --- Cell harvesting --- p.27 / Chapter 2.14.4 --- Expression checking --- p.27 / Chapter 2.14.5 --- SDS-PAGE --- p.27 / Chapter 2.14.6 --- Staining the acrylamide gel --- p.28 / Chapter 2.15 --- Purification of mutant TRP protein --- p.28 / Chapter 2.15.1 --- Cells lysis --- p.28 / Chapter 2.15.2 --- Chromatography --- p.29 / Chapter 2.15.3 --- Concentrating TRP as protein stock --- p.31 / Chapter 2.16 --- Protein stability --- p.32 / Chapter 2.16.1 --- Chemical stability --- p.33 / Chapter 2.16.2 --- Thermal stability --- p.34 / Chapter Chapter 3 --- Results / Chapter 3.1 --- Construction of mutant TRP genes --- p.36 / Chapter 3.1.1 --- PCR mutagenesis --- p.36 / Chapter 3.1.2 --- Sub-cloning of mutant TRP gene to express vector pET8c --- p.37 / Chapter 3.2 --- Expression and purification of mutant TRP --- p.38 / Chapter 3.3 --- Protein stability --- p.39 / Chapter 3.3.1 --- Free energy of unfolding --- p.39 / Chapter 3.3.2 --- Thermal stability --- p.43 / Chapter Chapter 4 --- Discussion / Chapter 4.1 --- "Effect of R39, K46, E62, E64" --- p.47 / Chapter 4.2 --- Double mutation at R39 and E62 --- p.50 / Chapter 4.3 --- Effect of R54 --- p.51 / Chapter 4.4 --- Effect of E47 and E50 --- p.53 / Chapter 4.5 --- Conclusion --- p.54 / References --- p.57 / Appendix --- p.64

The main aim of this thesis was to discover and design new ligands for difficult, under-explored and clinically relevant protein targets. A number of protein-protein interaction complexes (PPIs) are introduced as the target focus for the methods employed and developed herein. This thesis is separated into two sections to independently address both peptides and small molecules as screening agents. The project examines both approaches through comprehensive library design strategies and screening by NMR spectroscopic methods. ATAD2 is the first PPI investigated and was expressed and purified in good yield and was also isotopically labelled with Nitrogen-15 for enhanced sensitivity and orthogonal ligand and protein-observed NMR methods. A known pentapeptide was synthesised by solid-phase peptide synthesis (SPPS) using Fmoc chemistry for target validation and tool compound development. A one-bead one-compound (OBOC) tripeptide library was synthesised by SPPS in good yield and purity, determined using single-bead labelling techniques with a fluorescent dye (TMR) and HPLC analysis. This library contained 3072 unique tripeptides with 12 central non-natural, lysine derivatives flanked by 16 natural L amino acids. The library screening technique was based on using a fluorescently labelled protein and Confocal Nanoscanning to detect binding. However, fluorescent labelling of ATAD2 was unsuccessful due to difficult protein handling conditions, therefore this library was not screened. The advent of small molecule, high affinity inhibitors of this target protein generated by GSK shifted focus to a different PPI target, the ubiquitin conjugating enzyme, UbE2L3. A novel 'on-protein peptide building' approach was introduced with the aim of screening a library of fluorinated dipeptides and extending the most potent via the 'N' and 'C' terminus to increase the affinity. A proof-of-concept tetrapeptide to survivin was synthesised by SPPS by incorporation of a non-natural, fluorinated amino acid in the known tetrapeptide sequence. This fluorinated derivative showed target binding activity by 19F NMR spectroscopy. The tripeptide and dipeptide truncates were synthesised by SPPS and binding was still observable by 19F NMR. This method was extended to screening a library of synthesised fluorinated dipeptides by 19F NMR against UbE2L3. A single dipeptide was identified with low affinity and the dipeptide was extended C and N terminally by SPPS to increase affinity. However, there were no tripeptides identified for this protein using this method. The proof of concept tetrapeptide was a success, therefore further protein targets are required to conclusively assess the viability of the approach. Fragment based screening is then introduced as a second approach to novel ligand discovery. Coupled with cheminformatics analysis and in silico library design, we created an in-house fluorinated fragment library consisting of 109 fluorinated fragments using three parallel methods. Compounds were purchased and quality checked by LCMS, HPLC and 19F-NMR. These fragment libraries were screened in a 19F NMR assay against the UbE2L3 and NusE/NusB protein targets. In a primary mixture screen, two fragment hits were identified against the NusE/NusB PPI and there were no fragment hits identified against the UbE2L3 protein. The two fragments against NusE/NusB were validated using orthogonal ligand-binding NMR methods. A mini-series, consisting of six commercially available analogues, were purchased and two fragment analogues showed increased affinity and were active against E. coli in a bacterial inhibition assay. The dissociation constants of the six active compounds were determined by 15N-HSQC NMR titration experiments and shown to be in 100-500 μM range. The binding sites of each compound were also determined by 15N-HSQC chemical shift mapping. These fragment hits represent a novel chemical scaffold identified against the NusE/NusB PPI and demonstrate the potential druggability of this new, complex target. The use of fluorine as a sensor for binding detection is evaluated by incorporating into both peptides and fragments. Through the use of novel library design strategies, a campaign to discover novel ligands of difficult protein targets is presented.

SR蛋白在前體信使核糖核酸(pre-mRNA)的組成性剪接和選擇性剪接中扮演者重要的角色，在這個過程中它需要被SR蛋白激酶(SRPK) 燐酸化才能正常行使功能。經典的SR蛋白是由N端一到二個RNA識別基序(RRM) 以及C端一串精氨酸－絲氨酸(RS) 二肽所構成。SR蛋白的燐酸化調控它的亞細胞定位以及生理功能。此外，SR 蛋白激酶1(SRPK1) 和SR蛋白原型ASF/SF2的復合物結構顯示底物的結合需要第二個非標準的RRM結構域以及在N端可以被燐酸化的RS結構域，但是，第一個標準的RRM結構域對於SR 蛋白激酶1的結合卻是可以或缺的。 / 在這裡，我們展示了SR蛋白激酶2(SRPK2) 結合並且燐酸化SRp20的RS結構域，SRp20是另外一個只包含一個RNA識別基序(RRM) 的SR蛋白。與ASF/SF2相似的是，SRp20中的標準RNA識別基序對於SRPK2的結合並不是必要的。與此同時，我們發現錨定槽對於底物的識別作用在SRPK2中也是保守的，因為，錨定槽中四個關鍵氨基酸的突變會削弱它對SRp20的結合。 / 此外，現在認為SRPK2的功能已經不限於對前體信使核糖核酸(pre-mRNA) 的剪接調控。最近發現，SRPK2也可以燐酸化Tau蛋白並且介導阿爾茨海默疾病中的認知性缺陷。組成性的激活是SR蛋白激酶中的一個固有特性，然而人們對於它的調控機制還不是很清楚。因此, 為了更好的瞭解SRPK2，我們采用酵母雙雜交的方法並且最終發現一個新的SRPK2相互作用蛋白: ZNF187。 / ZNF187是一個可以結合血清反應元件(SRE) 的轉綠因子。我們的研究發現，它可以正向調控SRE的轉錄激活。然而，SRPK2在EGF的刺激下卻起着抑制的效果，其中EGF的刺激會促使SRPK2進入細胞核。進一步證實，通過RNAi干擾的方法敲掉SRPK2可以增加ZNF187誘導的SRE活性。在共轉染實驗中，SRPK2可以把ZNF187誘導的SRE活性逆轉到本底水平。對於可以和EGF刺激的SRPK2有着相似細胞定位的缺失或者突變研究發現，它們都可以產生相一致的抑制現象。於此相反，對於和SRPK2有着不同細胞定位的突變，它卻不能產生抑制效果。因此，我們認為在EGF的刺激下，SRPK2進入細胞核並且負向的調控ZNF187激活的SRE。令人驚訝的是，如果細胞在FBS的刺激下，SRPK2卻上調SRE活性，並且它可以協同增加ZNF187對於SRE的誘導。這些結果表明SRPK2對於ZNF187誘導的SRE轉綠調控是刺激物依賴的。 / SR proteins are critical players in regulating both constitutive and alternative pre-mRNA splicing, during which the phosphorylation by SR Protein Kinases (SRPKs) is required. Classical SR proteins contain one or two RNA Recognition Motifs (RRM) in their N terminus and a stretch of Arginine-Serine (RS) dipeptides in C terminus. Phosphorylation status of SR proteins regulates their subcellular localization as well as physiological function. In addition, complex structure of SRPK1 with ASF/SF2, a prototype of SR protein, shows that substrate binding requires non-canonicalRRM2 domain and RS domain, which can be extensivelyphosphorylated. However, the canonical RRM1 domain is dispensable for such interaction. / Here we show that SRPK2 binds and phosphorylates SRp20, a classical single RRM domain-containing SR protein, at its RS domain. Similarly with ASF/SF2, the canonical RRM domain of SRp20 is dispensable for interacting with SRPK2. Meanwhile, we also find that a docking groove that iscritical for substrate binding in SRPK1 is also conserved in SRPK2, since mutations on four key residues in docking groove impair its binding affinity with SRp20. / In addition, SRPK2 is now known to function more then regulating mRNA splicing, such as cell proliferation and cell apoptosis. Recently, SRPK2 is also shown to be a kinase phosphorylating Tau and mediate the cognitive defects in Alzheimer’s disease (AD). Besides, an intrinsic character of SRPKs lies in that they are constitutively active, but the regulation mechanism is not well understood. Therefore, in order to obtain a better recognition about SRPK2, we applied yeast two-hybrid assay and eventually anew interaction partner called ZNF187 was identified. / ZNF187 is a transcriptional factor that binds with Serum Response Element (SRE). Our studies showed that it isa positive regulator of SRE activity. However, SRPK2 showed inhibiting effect on SRE activation with the treatment of EGF, which could induce its nucleus entry, when co-transfected, it reversed the stimulating effect on SRE by ZNF187 to basal level. Furthermore, knockdown of SRPK2 by RNAi would enhance ZNF187-stimuated SRE activation. Studies on truncation and mutations that have the similar effect with EGF-induced subcellular localization of SRPK2 also generated the same inhibiting phenomenon. In contrast, mutant that has distinct localization with SRPK2 wild type failed to exert suppression. Therefore, we conclude that with the treatment of EGF, SRPK2 moves into nucleus and negatively regulates ZNF187-stimulated transactivation of SRE. Surprisingly, when cells were treated with FBS, SRPK2 showed stimulation on SRE activity and it synergized ZNF187-stimulated effect on SRE, indicating that transcriptional regulation of SRPK2 on ZNF187-stimulated SRE activity is stimuli-dependent. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Shang, Yong. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 113-137). / Abstracts also in Chinese.

Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant.

Li, Man Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 109-119). / Abstracts in English and Chinese. / Thesis Committee --- p.I / Statement --- p.II / Abstract --- p.III / 摘要 --- p.V / Acknowledgements --- p.VI / Abbreviations --- p.VIII / Abbreviations of Chemicals --- p.X / List of Tables --- p.XI / List of Figures --- p.XII / Table of Contents --- p.XIII / Chapter Chapter 1 --- Literature Review --- p.1 / Chapter 1.1 --- General amino acid control in yeast --- p.1 / Chapter 1.2 --- Mammalian eIF2α kinases --- p.7 / Chapter 1.2.1 --- Heme-regulated inhibitor kinase (EIF2AK1/HRI) --- p.7 / Chapter 1.2.2 --- Protein kinase dsRNA-dependent (EIF2AK2/PKR) --- p.8 / Chapter 1.2.3 --- PKR-like ER kinase (EIF2AK3/PERK) --- p.9 / Chapter 1.2.4 --- General control non-repressible 2 (EIF2AK4/GCN2) --- p.10 / Chapter 1.2.5 --- Activating transcription factor 4 (ATF4) --- p.11 / Chapter 1.3 --- Plant General Amino Acid Control --- p.12 / Chapter 1.3.1 --- Studies of the homolog of GCN2 in Arabidopsis thaliana --- p.12 / Chapter 1.3.2 --- Studies of the homolog of other eIF2a kinase in plant --- p.14 / Chapter 1.3.3 --- Studies of the homolog of other GAAC components --- p.14 / Chapter 1.4 --- Previous works in our lab --- p.15 / Chapter 1.5 --- Hypothesis and Objectives --- p.17 / Chapter Chapter 2 --- Materials and Methods / Chapter 2.1 --- Materials --- p.18 / Chapter 2.1.1 --- "Bacterial cultures, plant materials and vectors" --- p.18 / Chapter 2.1.2 --- Primers --- p.21 / Chapter 2.1.3 --- Commercial kits --- p.25 / Chapter 2.1.4 --- "Buffer, solution, gel and medium" --- p.25 / Chapter 2.1.5 --- "Chemicals, reagents and consumables" --- p.25 / Chapter 2.1.6 --- Enzymes --- p.25 / Chapter 2.1.7 --- Antibodies --- p.25 / Chapter 2.1.8 --- Equipments and facilities --- p.25 / Chapter 2.2 --- Methods --- p.26 / Chapter 2.2.1 --- Growth conditions of Arabidopsis thaliana --- p.26 / Chapter 2.2.1.1 --- Surface sterilize of Arabidopsis thaliana seed --- p.26 / Chapter 2.2.1.2 --- Growing of Arabidopsis thaliana --- p.26 / Chapter 2.2.1.3 --- Treatment of Arabidopsis seedling --- p.26 / Chapter 2.2.2 --- Basic molecular techniques --- p.27 / Chapter 2.2.2.1 --- Liquid culture of Escherichia coli --- p.27 / Chapter 2.2.2.2 --- Preparation of plasmid DNA --- p.27 / Chapter 2.2.2.3 --- Restriction digestion --- p.27 / Chapter 2.2.2.4 --- DNA purification --- p.28 / Chapter 2.2.2.5 --- DNA gel electrophoresis --- p.28 / Chapter 2.2.2.6 --- DNA ligation --- p.29 / Chapter 2.2.2.7 --- CaCl2 mediated E. coli transformation --- p.29 / Chapter 2.2.2.8 --- Preparation of DNA fragment for cloning --- p.29 / Chapter 2.2.2.9 --- PCR reaction for screening positive E. coli transformants --- p.30 / Chapter 2.2.2.10 --- DNA sequencing --- p.30 / Chapter 2.2.2.11 --- RNA extraction from plant tissue with tRNA --- p.31 / Chapter 2.2.2.12 --- Extraction of RNA without tRNA --- p.31 / Chapter 2.2.2.13 --- cDNA synthesis --- p.32 / Chapter 2.2.2.14 --- SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) --- p.33 / Chapter 2.2.2.15 --- Western blotting --- p.33 / Chapter 2.2.3 --- Sub-cloning of AtGCN2 --- p.34 / Chapter 2.2.3.1 --- Sub-cloning full length AtGCN2 into pMAL-c2 --- p.36 / Chapter 2.2.3.2 --- Sub-cloning of the N-terminal sequence of AtGCN2 into pMAL-c2 --- p.38 / Chapter 2.2.3.3 --- Sub-cloning of the C-terminal sequence of AtGCN2 into pMAL-c2 --- p.38 / Chapter 2.2.4 --- Cloning of the eIF2α candidates for the in vitro assay --- p.41 / Chapter 2.2.4.1 --- Cloning of At2g40290 (putative eIF2α candidate) --- p.41 / Chapter 2.2.4.2 --- Cloning of At5g05470 (putative eIF2α candidate) into pBlueScript KS II + --- p.43 / Chapter 2.2.4.3 --- Sub-cloning of At5g05470 into pGEX-4T-1 --- p.43 / Chapter 2.2.4 --- Expression and purification of fusion proteins --- p.45 / Chapter 2.2.5 --- Expression of fusion proteins in E. coli --- p.45 / Chapter 2.2.5.2 --- Extraction of E. coli soluble proteins --- p.45 / Chapter 2.2.5.3 --- Purification of GST tagged fusion protein --- p.46 / Chapter 2.2.5.4 --- Purification of MBP tagged fusion protein --- p.46 / Chapter 2.2.5.5 --- Concentration of purified fusion proteins --- p.46 / Chapter 2.2.5.6 --- MS/MS verification of purified fusion proteins --- p.47 / Chapter 2.2.6 --- Gel mobility shift assay --- p.47 / Chapter 2.2.6.1 --- Synthesis of short biotinylated RNA --- p.47 / Chapter 2.2.6.2 --- Ligation of short biotinylated RNA with tRNA --- p.48 / Chapter 2.2.6.3 --- Gel mobility shift assay --- p.48 / Chapter 2.2.6.4 --- Blotting of the sample on to nitrocellulose membrane --- p.48 / Chapter 2.2.6.5 --- Detection of the tRNA on the membrane --- p.49 / Chapter 2.2.6.6 --- Detection of the MBP fusion proteins on the membrane --- p.49 / Chapter 2.2.7 --- In vitro kinase assay of AtGCN2 --- p.49 / Chapter 2.2.8 --- In vitro translation inhibition assay --- p.50 / Chapter 2.2.8.1 --- In vitro transcription of HA mRNA --- p.50 / Chapter 2.2.8.2 --- In vitro translation --- p.51 / Chapter 2.2.8.3 --- Detection of the protein dot blot --- p.51 / Chapter 2.2.9 --- Gene expression analysis by real time PCR --- p.52 / Chapter 2.2.10 --- Total seed nitrogen analysis --- p.53 / Chapter Chapter 3 --- Results / Chapter 3.1 --- Blast search results suggested that AtGCN2 may be the sole eIF2α kinase in Arabidopsis thaliana --- p.54 / Chapter 3.2 --- Existence of two eIF2α candidates in Arabidopsis thaliana genome --- p.59 / Chapter 3.3 --- Fusion proteins were successfully expressed and purified --- p.63 / Chapter 3.4 --- C-terminal of AtGCN2 has a higher affinity toward tRNA than rRNA --- p.67 / Chapter 3.5 --- Both eIF2α candidates can be phosphorylated by full length AtGCN2 in vitro --- p.70 / Chapter 3.6 --- AtGCN2 can inhibit translation in vitro --- p.72 / Chapter 3.7 --- Overexpression of AtGCN2 did not affect expression of selected genes --- p.74 / Chapter 3.8 --- Overexpression of AtGCN2 did not affect seed nitrogen content and C:N ratio under normal growth conditions --- p.83 / Chapter Chapter 4 --- Discussion --- p.85 / Chapter 4.1 --- Existing evidence supported that AtGCN2 is the sole eIF2α kinase in Arabidopsis thaliana --- p.85 / Chapter 4.2 --- Kinase activities of AtGCN2 and its two substrates in Arabidopsis --- p.86 / Chapter 4.3 --- C-terminal binds tRNA in the gel mobility shift assay --- p.88 / Chapter 4.4 --- Overexpression of AtGCN2 did not affect gene expression of the transgenic lines under nitrogen starvation and azerserine treatment --- p.90 / Chapter 4.5 --- Overexpression of AtGCN2 did not alter the seed nitrogen content --- p.91 / Chapter 4.6 --- Existence of GCN4 and ATF4 in plant --- p.92 / Chapter 4.7 --- Alternative model without GCN4 and ATF4 homolog --- p.93 / Chapter 4.8 --- Possible application of the in vitro kinase assay --- p.94 / Chapter 4.9 --- Possible application of the in vitro translation inhibition analysis platform in future study --- p.95 / Chapter Chapter 5 --- Conclusion and Future Prospective --- p.97 / Appendices / Appendix I Commercial kits used in this project --- p.98 / "Appendix II Buffer, solution, gel and medium" --- p.99 / "Appendix III Chemicals, reagents and consumables" --- p.102 / Appendix IV Enzymes --- p.103 / Appendix V Antibodies --- p.104 / Appendix VI Equipments and facilities --- p.105 / Appendix VII Supplementary Data --- p.106 / Appendix VIII Amplification efficiency of real time primers --- p.108 / References --- p.109

Post-translational modification (PTM) of proteins by enzymes such as methyltransferases, kinases and deacetylases play a crucial role in the regulation of many metabolic pathways. Determining the substrate scope of these enzymes is essential when studying their biological role. However, the combinatorial nature of possible protein substrate sequences makes experimental screening assays intractable. To predict new substrates for proteins, various computational approaches have been developed. Our method relies on crystallographic data and a novel multistate computational protein design algorithm. We previously used our method to successfully predict four new substrates for SMYD2 (Lanouette S & Davey J.A., 2015), doubling the number of known targets for this PTM enzyme that has been difficult to characterize using other methods. This was possible by first extracting a specificity profile of Smyd2 using our algorithm and subsequently screening a peptide library for matching sequences. However, our method did not yield successful results when attempting to reproduce specificity profiles of other proteins (64% accuracy on average). Different protein environments have demonstrated limitations in the methodology and lead us to further develop the algorithm on a more thorough dataset. Using our new optimized method, specificity profile predictions increase by roughly 20% (84% accuracy on average), independent of the structural template used. The algorithm was then used to blindly predict a specificity profile for the methyltransferase Smyd3, an enzyme for which limited data is currently available. A library of 2550 peptides was screened with the predicted profile, yielding 123 matching sequences. We randomly chose 64 for experimental validation (SPOT peptide array) of methylation by Smyd3 and found 45 methylated and 19 non-methylated peptides (70% success rate). Finally, we released to the community a web version of the algorithm, which can be accessed as http://viper.science.uottawa.ca.