University, Diss., 2005--Düsseldorf.

Univ., Diss., 2006--Frankfurt (Main) / Zsfassung in engl. und dt. Sprache

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 118-127).

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

Studies undertaken to investigate the binding of low molecular mass analogues of polyphenolic vegetable tannins to collagen have prompted the development of a new method to investigate protein-ligand interactions. This method, the continuous-flow dynamic dialysis method (CFDD), differs from conventional dialysis procedures used for protein-ligand binding studies. In this method, the ligand concentration in the diffusate is monitored automatically at successive closely spaced time intervals while being continuously eluted from the dialysis cell. The primary data obtained by this method consists of a series of spectrophotometric absorbance measurements representing the ligand concentration in the sink compartment of a dialysis cell. This primary data is recorded by means of a data logging device onto a punched paper tape for subsequent computer processing. Two original methods are presented for analysing the primary data to extract the protein-ligand binding isotherm. The first of these is a direct analysis which relies on Fick's first law of diffusion. In this method it is necessary to establish, by means of a control experiment, a value for the ligand permeation constant. This is used in a subsequent analysis to establish a relationship between the measured rate of diffusion of the ligand from a protein-ligand mixture and the concentration of unbound ligand which is in equilibrium with the protein-ligand complex. The protein-ligand binding isotherm is obtained from parametric equations which give the quantity of ligand bound to the protein and the concentration of unbound ligand in the sample compartment as functions of time. The second method, which is more general, utilizes the same primary data but is based on establishing a system transfer function to characterise the dialysis and eluting processes. This analysis depends on the linearity of the system and utilizes numerical laplace transforms of the primary data sets obtained from control and protein-ligand dialyses. Laplace transforms are used to effect a deconvolution of the transfer function from the primary data and yield the concentration of ligand in equilibrium with the protein-ligand complex. This procedure yields, simultaneously, both the total ligand concentration and the concentration of unbound ligand in the protein compartment of the dialysis cell. These quantities are used to establish the binding isotherm for the protein ligand system. Numerical inversion of the laplace transforms in this analysis is effected by their reduction to Fourier series. The experimental reliability of the continuous-flow dynamic dialysis method, and validity of the two analytical methods used to derive a binding isotherm from dialysis data are evaluated from studies of the binding of phenol red to bovine serum albumin (BSA) at 15⁰, 20⁰ and 25⁰ C, as well as from simulated binding curves generated by the numerical solution of the differential equations used to describe the dialysis and elution process in terms of a two-site Scatchard binding model. The method is used to investigate the binding to collagen of a series of low molecular mass phenolic compounds which can be isolated from Wattle and Quebracho vegetable tannin extracts. These compounds can be considered as monomeric precursor analogues of the polymeric vegetable tannins. The binding of these ligands to collagen is shown to be characterised by high capacity, low affinity binding in which the uptake of ligand by the protein increases linearly with increasing ligand concentration. Collagen exhibits no indication of site saturation for these ligands over the experimentally accessible concentration ranges investigated.

Infectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. We developed DeepViral, a deep learning based method that predicts protein– protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.

This thesis focuses on the two research projects which have applied machine learning techniques to the protein-related topics. The first project is to use protein sequences and the interaction graph to address the protein-protein interaction prediction problem. The second project is to leverage the sequences of protein loops within and beyond homologs to predict the protein loop structures. In the protein-protein interaction prediction project, we applied the pretrained language models, which were trained on large sets of protein sequences, as one of the protein feature extraction methods. Another feature extraction method is the graph learning on the protein interaction graph. The graph learning embeddings and the language model embeddings were fed into classification models to predict if two proteins are interacting or not. We trained and tested our methods on the S. cerevisiae dataset and the human dataset. Our results are comparable to or better than other state-of-art methods, with the advantages that our method is faster at the sample preparation step and has a larger application scope for requiring only protein sequences. We also did experiments with datasets from different similarity cutoffs between the train and test set of the human dataset, and our method has shown an effective prediction ability even with a strict similarity cutoff. In the protein loop prediction project, we utilized the attention-based encoder-decoder language models to predict the protein loop inter-residue distances from the protein loop sequences. We fed the model with the loop sequences and received arrays of numbers representing the distances between each C_α pair in the loops. We utilized two different strategies to reconstruct the loops from the predicted distances. One was firstly to calculate the C_α coordinates from the predicted distances, and then apply a fast full-atom reconstruction method starting from C_α coordinates to build the local loop structures. Our local loop structure prediction results of this method are very competitive with low local RMSDs, especially with the lowest standard deviations. The second method was to integrate the predicted inter-residue distances as constraints to the de novo loop prediction method PLOP (Jacobson et al. 2004). We tested the loop reconstruction process on the 8-res and 12-res loop benchmark sets. This method has the best performance compared to other state-of-art methods, and the incorporation of such machine learning step decreased the computing time of the standalone PLOP program.

Due to the increasing resistance against the currently used antimalarial drugs, novel chemotherapeutic agents that target new metabolic pathways for the treatment of malarial infections are urgently needed. One approach to the drug discovery process is to use interaction analysis to find proteins that are involved in a specific metabolic pathway that has been identified as a drug target. Protein-protein interactions in such a pathway can be preferential targets since a) there is often greater structural variability in protein-protein interfaces, which can lead to more effective differentiation between the parasite and host proteins; and b) the important amino acids in a protein-protein interface are often conserved and even one amino acid mutation can lead to the dissociation of the complex, implying that resistance should be slower to appear. Since polyamines and their biosynthetic enzymes occur in increased concentrations in rapidly proliferating cells, the inhibition of polyamine metabolism is a rational approach for the development of antiparasitic drugs. Polyamine synthesis in P. falciparum is uniquely facilitated by a single open reading frame that encodes both rate-limiting enzymes in the pathway, namely ornithine decarboxylase (ODC) and S-adenosylmethionine decarboxylase (AdoMetDC). The AdoMetDC/ODC domains are assembled in a heterotetrameric bifunctional protein complex of ~330 kDa. Inhibition of both decarboxylase activities is curative of murine malaria and indicates the viability of such strategies in malaria control. It was hypothesized that protein ligands to this enzyme can be utilized in targeting the polyamine biosynthetic pathway in a novel approach. The bifunctional PfAdoMetDC/ODC was recombinantly expressed with a C-terminal Strep-tag-II to allow affinity purification. Subsequent gel electrophoresis analysis showed the presence of 3 contaminating proteins (~60 kDa, ~70 kDa and ~112 kDa) that co-elute with the ~330 kDa AdoMetDC/ODC. Efforts to purify the bifunctional protein to homogeneity included subcloning into a double-tagged vector for tandem affinity purification as well as size-exclusion HPLC. SDS-PAGE analysis of these indicated that separation of the four proteins was not successful, implicating the presence of strong protein-protein interactions. Western blot analysis showed that the ~112 kDa and ~70 kDa peptides were recombinantly produced with a C-terminal Strep-tag, indicating their heterologous origin. The ~60 kDa fragment was however not recognised by the tag-specific antibodies. This implies that this fragment is of E. coli origin. MS-analysis of the contaminating bands showed that the ~112 kDa peptide is an N-terminally truncated form of the full-length protein, the ~70 kDa peptide is a mixture of N-terminally truncated recombinant protein and E. coli DnaK and the ~60 kDa peptide is E. coli GroEL. A P. falciparum cDNA phage display library was used to identify peptide ligands to PfAdoMetDC/ODC. Of the peptides isolated through the biopanning process, only one was shown to occur in vivo. It could however not be conclusively shown that the isolated peptides bind to PfAdoMetDC/ODC and not to the co-eluting E. coli proteins. It is thought that while it is extremely likely that interacting protein partners to PfAdoMetDC/DOC exist, the available technologies are not sufficient to lead to the identification of such partners. / Dissertation (MSc (Biochemistry))--University of Pretoria, 2008. / Biochemistry / unrestricted