Author
Dorothea Fiedler
Other affiliations: University of California, San Francisco, Howard Hughes Medical Institute, University of Washington ...read more
Bio: Dorothea Fiedler is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Inositol & Kinase. The author has an hindex of 33, co-authored 85 publications receiving 5000 citations. Previous affiliations of Dorothea Fiedler include University of California, San Francisco & Howard Hughes Medical Institute.
Topics: Inositol, Kinase, Phosphorylation, Medicine, Inositol phosphate
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a chiral self-assembled M4L6 supramolecular tetrahedron can encapsulate a variety of cationic guests with varying degrees of stereoselectivity.
Abstract: Supramolecular chemistry represents a way to mimic enzyme reactivity by using specially designed container molecules. We have shown that a chiral self-assembled M4L6 supramolecular tetrahedron can encapsulate a variety of cationic guests with varying degrees of stereoselectivity. Reactive iridium guests can be encapsulated, and the C−H bond activation of aldehydes occurs with the host cavity controlling the ability of substrates to interact with the metal center based upon size and shape. In addition, the host container can act as a catalyst by itself. By restricting reaction space and preorganizing the substrates into reactive conformations, it accelerates the sigmatropic rearrangement of enammonium cations.
864 citations
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TL;DR: Using an approach called differential epistasis mapping, widespread changes in genetic interaction are discovered among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage, uncovering many gene functions that go undetected in static conditions.
Abstract: Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.
464 citations
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TL;DR: An epistatic miniarray profile comprised of 100,000 pairwise, quantitative genetic interactions, including virtually all protein and small-molecule kinases and phosphatases as well as key cellular regulators is generated, finding an enrichment of positive genetic interactions between kinases, phosphatase, and their substrates.
278 citations
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TL;DR: It is concluded that ATP-competitive Akt inhibitors impart regulatory phosphorylation of their target kinase Akt providing new insights into both natural regulation of Akt activation andAkt inhibitors entering the clinic.
Abstract: The kinase Akt plays a central role as a regulator of multiple growth factor input signals, thus making it an attractive anticancer drug target. A-443654 is an ATP-competitive Akt inhibitor. Unexpectedly, treatment of cells with A-443654 causes paradoxical hyperphosphorylation of Akt at its two regulatory sites (Thr308 and Ser473). We explored whether inhibitor-induced hyperphosphorylation of Akt by A-443654 is a consequence of disrupted feedback regulation at a pathway level or whether it is a direct consequence of inhibitor binding to the ATP binding site of Akt. Catalytically inactive mutants of Akt revealed that binding of an inhibitor to the ATP site of Akt is sufficient to directly cause hyperphosphorylation of the kinase in the absence of any pathway feedback effects. We conclude that ATP-competitive Akt inhibitors impart regulatory phosphorylation of their target kinase Akt. These results provide new insights into both natural regulation of Akt activation and Akt inhibitors entering the clinic.
274 citations
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TL;DR: The utilization of a supramolecular metal–ligand assembly that is capable of catalyzing a unimolecular rearrangement is reported, and by inclusion into a sizeand shape-constrained reaction space these rearrangements are accelerated by up to three orders of magnitude compared to their background rates.
Abstract: Chemists have long envied the ability of enzymes to manipulate reaction energetics and specificity through steric confinement and precise functional-group interactions. The enormous rate accelerations that enzymes achieve at modest temperatures may be attributed to their high degree of complexity, and the synthetic chemist is hard pressed to create such well-constructed catalytic scaffolds. Yet in this regard, the utilization of supramolecular chemistry may have an advantage: supramolecular self-assembly facilitates the creation of large, complex structures from relatively simple precursors. Based on reversible weak interactions, such as hydrogen bonding or metal–ligand interactions, synthetic chemists have generated an array of self-assembled structures, diverse in architecture and composition. Some of these synthetic structures bear an internal cavity, and their interior can provide a new and very specific chemical environment, distinctly different from the exterior surroundings. The development of container-like molecules into chemically useful structures is an attractive goal, and their utilization as catalytic reaction chambers can parallel the enzyme function. The rate for a bimolecular Diels–Alder reaction, for example, was reported to be significantly accelerated in the presence of a supramolecular host, owing to the increase of effective concentrations of the two substrates when bound within the same capsule. Major challenges are a) to develop supramolecular systems capable of catalyzing unimolecular reactions, and b) to circumvent catalyst inhibition, a problem that frequently occurs when the cavity binds the reaction product more strongly than the substrate. We report herein the utilization of a supramolecular metal–ligand assembly that is capable of catalyzing a unimolecular rearrangement. Simply by inclusion into a sizeand shape-constrained reaction space these rearrangements are accelerated by up to three orders of magnitude compared to their background rates. Furthermore, the chemical properties of the reacting system provide an effective means of preventing product inhibition, which facilitates catalyst turnover. Raymond and co-workers have composed supramolecular tetrahedral structures of M4L6 stoichiometry through selfassembly of simple metal and ligand components. 11] In these assemblies the metal atoms are located at the vertices of the tetrahedron and six bis-bidentate catechol amide ligands span the edges (Figure 1). The tris-bidentate chelation of the metal centers renders them chiral (D or L), and the mechanical coupling through the rigid ligands results in the formation of exclusively homochiral assemblies (i.e. D,D,D,D or L,L,L,L). By virtue of the 12 overall charge, the assemblies are water soluble, yet they contain a flexible
243 citations
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
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TL;DR: In this paper, the coding exons of the family of 518 protein kinases were sequenced in 210 cancers of diverse histological types to explore the nature of the information that will be derived from cancer genome sequencing.
Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore
PL02-05
All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.
2,737 citations