scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
Journal ArticleDOI
14 Jul 2016-Cell
TL;DR: It is demonstrated that PM20D1 is a bidirectional enzyme in vitro, catalyzing both the condensation of fatty acids and amino acids to generate N-acyl amino acids and also the reverse hydrolytic reaction.

182 citations

Journal ArticleDOI
TL;DR: It is concluded that damage-response proteins are an unusually rapidly degraded family and that ClpXP has substantial capacity to process the influx of newly synthesized substrates while maintaining the ability to degrade its other substrates in an environmentally responsive manner.

181 citations

Journal ArticleDOI
06 Jul 2012-Cell
TL;DR: This study reports that Skp2 is acetylated by p300 at K68 and K71, which is a process that can be antagonized by the SIRT3 deacetylase, and identifies an acetylation-dependent regulatory mechanism governingSkp2 oncogenic function and provides insight into how cytoplasmic Skp 2 controls cellular migration.

181 citations

Journal ArticleDOI
TL;DR: This work describes an alternative proteasome activator from Saccharomyces cerevisiae, Blm10, which is found predominantly within hybrid Blm 10–CP–RP complexes, representing a distinct pool of mature proteasomes.
Abstract: Proteasome activity is fine-tuned by associating the proteolytic core particle (CP) with stimulatory and inhibitory complexes. Although several mammalian regulatory complexes are known, knowledge of yeast proteasome regulators is limited to the 19-subunit regulatory particle (RP), which confers ubiquitin-dependence on proteasomes. Here we describe an alternative proteasome activator from Saccharomyces cerevisiae, Blm10. Synthetic interactions between blm10Delta and other mutations that impair proteasome function show that Blm10 functions together with proteasomes in vivo. This large, internally repetitive protein is found predominantly within hybrid Blm10-CP-RP complexes, representing a distinct pool of mature proteasomes. EM studies show that Blm10 has a highly elongated, curved structure. The near-circular profile of Blm10 adapts it to the end of the CP cylinder, where it is properly positioned to activate the CP by opening the axial channel into its proteolytic chamber.

181 citations

Journal ArticleDOI
TL;DR: It is proposed that IFT27 separates from IFT-B inside cilia to promote ARL6 activation, BBSome coat assembly, and subsequent ciliary exit, mirroring the process by which BBSome mediates cargo entry into cilia.

181 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: 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.
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.

32,980 citations

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

Journal ArticleDOI
23 Jan 2009-Cell
TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.

18,036 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: 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.
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.

12,469 citations