Protein-protein interactions (PPIs) across the cell envelope of Gram-negative bacteria are critical for mediating signal transduction pathways that underpin cellular homeostasis. The Ton and Tol Pal systems are two conserved, ancestrally related protein networks that are also required for bacterial pathogenesis. Both Ton and Tol-Pal traverse the periplasm to effect different functions at the outer membrane (OM). Tol-Pal is composed of a homologous complex of three inner membrane proteins, TolQ-TolR-TolA (linked to proton motive force) and two additional periplasmic proteins TolB and Pal. The physiological role of the Tol-Pal system is to stabilise the OM, however the mechanism involved is unknown. TolA is however known to form a crucial protein-protein interaction via its C-terminus with the disordered N-terminus of TolB. Prior to this thesis, determination of the molecular features underlying a protein-protein complex between TolA and an endogenous binding partner TolB had never been accomplished. In this work, I describe the first structure comprising the TolA-TolB complex from Gram negative bacteria. The structure of this complex was determined from Pseudomonas aeruginosa by solution NMR spectroscopy. I determined the interaction between P. aeruginosa TolA and a TolB N terminal peptide to be relatively weak using fluorescence anisotropy. I found that TolB interacts with TolA through an analogous mechanism to that seen in TonB-dependent transporters. Based on these studies and bioinformatics analyses, I hypothesize that the evolutionary resilience of the Tol-Pal system to external pressures is contingent on the preservation of the TolA-TolB interface. Structure-based mutations within the TolA-TolB complex were also evaluated for their effect on in vivo function of the Tol-Pal complex and impact on complex formation in vitro. Taken together, the results demonstrate that protein networks which transduce energy to the OM through PMF-dependent systems in bacterial cells appear to follow a common β-strand augmentation mechanism.

Aspergillus flavusis a filamentous fungusthat causes an ear and kernel rot in maize (Zea mays L.). It produces a toxic secondary metabolite, aflatoxin, on the colonized maize kernels. Aflatoxin is a carcinogen to humans and animals. The toxin is also an immunosuppressant and causes aspergillosis in immune compromised individuals. Therefore, the presence of aflatoxin in food is strictly regulated by governmental agencies. Contaminated food leads to severe loss in profit and in marketable yield. There has been extensive research to investigate resistance of maize toA. flavus. Certain lines of maize exhibit increased resistance to A. flavus and aflatoxin accumulation compared to others and correlated with that are proteins and metabolites that differ in abundance in those lines. Among them are members of the cupin superfamily of proteins and products of special nitrogen metabolism (derived from glutamate). The goal here was to identify networks underlying disease resistance indifferent maize genotypes through the identification of protein-protein interactions and the analysis of transcript abundance profiles realting to cupins and glutamate. The outcome will be an understanding of host resistance to A. flavussufficient to develop methods to prevent pre-harvest contamination by aflatoxin. A protein abundant in resistant maize was identified as a cupin and named ZmCUP1. The cDNA isolation, expression in E. coliand characterization of the protein encoded by the mRNA, Zmcup1, lead to the discovery that the ZmCUP1 protein had anti fungal properties and oxalate decarboxylase activity (EC 4.1.1.2). Another part of the project aimed at understanding the involvement of a transgene that encoded bacterial NADPH-glutamate dehydrogenase (GDHA; EC 4.2.3.1) that reduced aflatoxin accumulation by half. A maize partial predicted protein to protein interactome was built and used to identify potential interactions between proteins expressed differentially in lines of maize resistant to A. flavus. These interactions were characterized in-silico and one specific interaction, between Zmcup1 and a maize zinc finger protein was characterized in vitro.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第17599号 / 工博第3758号 / 新制||工||1573(附属図書館) / 30365 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 濵地 格, 教授 梅田 眞郷, 教授 杉野目 道紀 / 学位規則第4条第1項該当

>Magister Scientiae - MSc / Retinoblastoma Binding Protein 6 (RBBP6) is a 200 kDa human protein known to play an essential role in mRNA 3’-end processing, as well as functioning as an E3 ligase to catalyze ubiquitination and suppression of p53 and other cancer-associated proteins. A RBBP6 knockout mouse model previously suggested that RBBP6 cooperates with MDM2 in polyubiquitinating p53, but is not able to ubiquitinate p53 without the assistance of MDM2. However, unpublished studies from our laboratory suggest that the N-terminal 335 residues of RBBP6, known as R3, are able to ubiquitinate p53 in full in vitro assays, and that the isolated RING finger of RBBP6 is able to catalyse ubiquitination of itself, a phenomenon known as auto-ubiquitination. It is, however, possible that other domains within RBBP6, in particular the ubiquitin-like DWNN domain situated near to the RING finger, may modulate the autoubiquitination and substrate-ubiquitination potentials of the complete protein. / 2022

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β-Glucosidase zymograms of certain maize genotypes (nulls) do not show any activity bands after electrophoresis. We have shown that a chimeric lectin called β-glucosidase aggregating factor (BGAF) is responsible for the absence of β-glucosidase activity bands on zymograms. BGAF specifically binds to maize β-glucosidase isozymes Glu1 and Glu2 and forms large, insoluble complexes. Furthermore, we have previously shown that the N-terminal (Glu⁵⁰-Val¹⁴⁵) and the C-terminal (Phe⁴⁶⁶-Ala⁵¹²) regions contain residues that make up the BGAF binding site on maize Glu1. However, sequence comparison between sorghum β-glucosidases (dhurrinases, Dhr1 and Dhr2), to which BGAF does not bind, and maize β-glucosidases, and an examination of the 3-D structure of Glu1 suggested that the BGAF binding site on Glu1 is much smaller than predicted previously. To define more precisely the BGAF binding site, we constructed additional chimeric β-glucosidases. The results showed that a region spanning 11 amino acids (Ile⁷²-Thr⁸²) on Glu1 is essential and sufficient for BGAF binding, whereas the extreme N-terminal region Ser¹-Thr²⁹, together with C-terminal region Phe⁴⁶⁶-Ala⁵¹², affects the size of Glu1-BGAF complexes. To determine the importance of each region for binding, we determined the dissociation constants (K_d) of chimeric β-glucosidase-BGAF interactions. The results demonstrated that the extreme N-terminal and C-terminal regions are important but not essential for binding. To confirm the importance of Ile⁷²-Thr⁸² on Glu1 for BGAF binding, we constructed chimeric Dhr2 (C-11, Dhr2 whose Val⁷²-Glu⁸² region was replaced with the Ile⁷²-Thr⁸² region of Glu1). C-11 binds to BGAF, indicating that the Ile⁷²-Thr⁸² region is indeed a major interaction site on Glu1 involved in BGAF binding. We also constructed mutant β-glucosidases to identify and define the contribution of individual amino acids in the above three regions to BGAF binding. In the N-terminal region (Ile⁷²-Thr⁸²), critical region for BGAF binding, Glu1 mutants K81E and T82Y failed to bind BGAF in the gel-shift assay and their frontal affinity chromatography (FAC) profiles were essentially similar to that of sorghum β-glucosidase (dhurrinase 2, Dhr2), a non-binder, indicating that these two amino acids within Ile⁷²-Thr⁸² region are essential for BGAF binding. In the extreme N-terminal (Ser¹-Thr²⁹) and C-terminal (Phe⁴⁶⁶-Ala⁵¹²) regions, N481E [substitution of asparagine-481 with glutamic acid (as in Dhr)] showed lower affinity for BGAF, whereas none of the single amino acid substitutions in the Ser¹-Thr²⁹ region showed any effect on BGAF binding indicating that these regions play a minor role. To further confirm the importance of lysine-81 and threonine-82 for BGAF binding, we produced a number of Dhr2 mutants, and the results showed that all four unique amino acids (isoleucine-72, asparagine-75, lysine-81, and threonine-82) of Glu1 in the peptide span Ile⁷²-Thr⁸² are required to impart BGAF binding ability to Dhr2. The sequence comparison among plant β-glucosidases supports the hypothesis that BGAF binding is specific to maize β-glucosidases because only maize β-glucosidases have threonine at position 82. / Ph. D.

The world has proven unprepared for deadly viral outbreaks. Designing antiviral drugs and strategies requires a firm understanding of the interactions taken place between the proteins of the virus and human proteins. The current computational models for predicting these interactions consider only single viruses for which extensive prior knowledge is available. The two prediction frameworks in this dissertation, DeNovo and DeNovo-Human, make it possible for the first time to predict the interactions between any viral protein and human proteins. They further helped to answer critical questions about the Zika virus. DeNovo utilizes concepts from virology, bioinformatics, and machine learning to make predictions for novel viruses possible. It pools protein-protein interactions (PPIs) from different viruses sharing the same host. It further introduces taxonomic partitioning to make the reported performance reflect the situation of predicting for a novel virus. DeNovo avoids the expected low accuracy of such a prediction by introducing a negative sampling scheme that is based on sequence similarity. DeNovo achieved accuracy up to 81% and 86% when predicting for a new viral species and a new viral family, respectively. This result is comparable to the best achieved previously in single virus-host and intra-species PPI prediction cases. DeNovo predicts PPIs of a novel virus without requiring known PPIs for it, but with a limitation on the number of human proteins it can make predictions against. The second framework, DeNovo-Human, relaxes this limitation by forcing in-network prediction and random sampling while keeping the pooling technique of DeNovo. The accuracy and AUC are both promising ($>85%$, and $>91%$ respectively). DeNovo-Human facilitates predicting the virus-human PPI network. To demonstrate how the two frameworks can enrich our knowledge about virus behavior, I use them to answer interesting questions about the Zika virus. The research questions examine how the Zika virus enters human cells, fights the innate immune system, and causes microcephaly. The answers obtained are well supported by recently published Zika virus studies. / Ph. D.