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.

Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS

Abstract: A need exists for technologies that permit the direct quantification of differences in protein and posttranslationally modified protein expression levels. Here we present a strategy for the absolute quantification (termed AQUA) of proteins and their modification states. Peptides are synthesized with incorporated stable isotopes as ideal internal standards to mimic native peptides formed by proteolysis. These synthetic peptides can also be prepared with covalent modifications (e.g., phosphorylation, methylation, acetylation, etc.) that are chemically identical to naturally occurring posttranslational modifications. Such AQUA internal standard peptides are then used to precisely and quantitatively measure the absolute levels of proteins and posttranslationally modified proteins after proteolysis by using a selected reaction monitoring analysis in a tandem mass spectrometer. In the present work, the AQUA strategy was used to (i) quantify low abundance yeast proteins involved in gene silencing, (ii) quantitatively determine the cell cycle-dependent phosphorylation of Ser-1126 of human separase protein, and (iii) identify kinases capable of phosphorylating Ser-1501 of separase in an in vitro kinase assay. The methods described here represent focused, alternative approaches for studying the dynamically changing proteome.

Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.

Abstract: Highly complex protein mixtures can be directly analyzed after proteolysis by liquid chromatography coupled with tandem mass spectrometry (LC−MS/MS). In this paper, we have utilized the combination of strong cation exchange (SCX) and reversed-phase (RP) chromatography to achieve two-dimensional separation prior to MS/MS. One milligram of whole yeast protein was proteolyzed and separated by SCX chromatography (2.1 mm i.d.) with fraction collection every minute during an 80-min elution. Eighty fractions were reduced in volume and then re-injected via an autosampler in an automated fashion using a vented-column (100 μm i.d.) approach for RP-LC−MS/MS analysis. More than 162 000 MS/MS spectra were collected with 26 815 matched to yeast peptides (7537 unique peptides). A total of 1504 yeast proteins were unambiguously identified in this single analysis. We present a comparison of this experiment with a previously published yeast proteome analysis by Yates and colleagues (Washburn, M. P.; Wolters, D.; Yates, J. ...

A proteomics approach to understanding protein ubiquitination

Abstract: There is a growing need for techniques that can identify and characterize protein modifications on a large or global scale. We report here a proteomics approach to enrich, recover, and identify ubiquitin conjugates from Saccharomyces cerevisiae lysate. Ubiquitin conjugates from a strain expressing 6xHis-tagged ubiquitin were isolated, proteolyzed with trypsin and analyzed by multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for amino acid sequence determination. We identified 1,075 proteins from the sample. In addition, we detected 110 precise ubiquitination sites present in 72 ubiquitin-protein conjugates. Finally, ubiquitin itself was found to be modified at seven lysine residues providing evidence for unexpected diversity in polyubiquitin chain topology in vivo. The methodology described here provides a general tool for the large-scale analysis and characterization of protein ubiquitination.

A quantitative atlas of mitotic phosphorylation.

Abstract: The eukaryotic cell division cycle is characterized by a sequence of orderly and highly regulated events resulting in the duplication and separation of all cellular material into two newly formed daughter cells. Protein phosphorylation by cyclin-dependent kinases (CDKs) drives this cycle. To gain further insight into how phosphorylation regulates the cell cycle, we sought to identify proteins whose phosphorylation is cell cycle regulated. Using stable isotope labeling along with a two-step strategy for phosphopeptide enrichment and high mass accuracy mass spectrometry, we examined protein phosphorylation in a human cell line arrested in the G1 and mitotic phases of the cell cycle. We report the identification of >14,000 different phosphorylation events, more than half of which, to our knowledge, have not been described in the literature, along with relative quantitative data for the majority of these sites. We observed >1,000 proteins with increased phosphorylation in mitosis including many known cell cycle regulators. The majority of sites on regulated phosphopeptides lie in [S/T]P motifs, the minimum required sequence for CDKs, suggesting that many of the proteins may be CDK substrates. Analysis of non-proline site-containing phosphopeptides identified two unique motifs that suggest there are at least two undiscovered mitotic kinases.

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.