Steven P. Gygi

Bio: Steven P. Gygi is an academic researcher from Harvard University. The author has contributed to research in topics: Phosphorylation & Proteome. The author has an hindex of 172, co-authored 704 publications receiving 129173 citations. Previous affiliations of Steven P. Gygi include University of Rochester Medical Center & Cell Signaling Technology.

Ribosome binding of a single copy of the SecY complex: implications for protein translocation

Abstract: The SecY complex associates with the ribosome to form a protein translocation channel in the bacterial plasma membrane. We have used cryo-electron microscopy and quantitative mass spectrometry to show that a nontranslating E. coli ribosome binds to a single SecY complex. The crystal structure of an archaeal SecY complex was then docked into the electron density maps. In the resulting model, two cytoplasmic loops of SecY extend into the exit tunnel near proteins L23, L29, and L24. The loop between transmembrane helices 8 and 9 interacts with helices H59 and H50 in the large subunit RNA, while the 6/7 loop interacts with H7. We also show that point mutations of basic residues within either loop abolish ribosome binding. We suggest that SecY binds to this primary site on the ribosome and subsequently captures and translocates the nascent chain.

An OBSL1-Cul7Fbxw8 Ubiquitin Ligase Signaling Mechanism Regulates Golgi Morphology and Dendrite Patterning

Abstract: The elaboration of dendrites in neurons requires secretory trafficking through the Golgi apparatus, but the mechanisms that govern Golgi function in neuronal morphogenesis in the brain have remained largely unexplored. Here, we report that the E3 ubiquitin ligase Cul7Fbxw8 localizes to the Golgi complex in mammalian brain neurons. Inhibition of Cul7Fbxw8 by independent approaches including Fbxw8 knockdown reveals that Cul7Fbxw8 is selectively required for the growth and elaboration of dendrites but not axons in primary neurons and in the developing rat cerebellum in vivo. Inhibition of Cul7Fbxw8 also dramatically impairs the morphology of the Golgi complex, leading to deficient secretory trafficking in neurons. Using an immunoprecipitation/mass spectrometry screening approach, we also uncover the cytoskeletal adaptor protein OBSL1 as a critical regulator of Cul7Fbxw8 in Golgi morphogenesis and dendrite elaboration. OBSL1 forms a physical complex with the scaffold protein Cul7 and thereby localizes Cul7 at the Golgi apparatus. Accordingly, OBSL1 is required for the morphogenesis of the Golgi apparatus and the elaboration of dendrites. Finally, we identify the Golgi protein Grasp65 as a novel and physiologically relevant substrate of Cul7Fbxw8 in the control of Golgi and dendrite morphogenesis in neurons. Collectively, these findings define a novel OBSL1-regulated Cul7Fbxw8 ubiquitin signaling mechanism that orchestrates the morphogenesis of the Golgi apparatus and patterning of dendrites, with fundamental implications for our understanding of brain development.

Structure-Based Evolution of Low Nanomolar O-GlcNAc Transferase Inhibitors

Abstract: Reversible glycosylation of nuclear and cytoplasmic proteins is an important regulatory mechanism across metazoans. One enzyme, O-linked N-acetylglucosamine transferase (OGT), is responsible for all nucleocytoplasmic glycosylation and there is a well-known need for potent, cell-permeable inhibitors to interrogate OGT function. Here we report the structure-based evolution of OGT inhibitors culminating in compounds with low nanomolar inhibitory potency and on-target cellular activity. In addition to disclosing useful OGT inhibitors, the structures we report provide insight into how to inhibit glycosyltransferases, a family of enzymes that has been notoriously refractory to inhibitor development.

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

Machine learning

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.).

The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

Abstract: Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments. Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement. Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.