Printing protein arrays from DNA arrays

He, Mingyue; Stoevesandt, Oda; Palmer, Elizabeth; Khan, F. Nawaz; Ericsson, Olle; Taussig, Michael J.

doi:10.1038/nmeth.1178

Cited by 146 publications

(124 citation statements)

References 14 publications

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“…With conventional protein microarrays it is necessary to purify thousands of in vivo expressed proteins and to spot these purified proteins on a solid surface (5-8). In contrast, in situ synthesis protein microarray technologies simplify protein microarray fabrication by circumventing the steps of in vivo protein expression and purification (9,10,14,15). This streamlining facilitates an increase in the number of target genes that can be assayed, allowing for thousands of protein-encoding plasmids to be spotted at lower cost and in less time.…”

mentioning

confidence: 99%

Mapping transcription factor interactome networks using HaloTag protein arrays

Yazaki

Galli

Kim

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literaturecurated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.protein arrays | interactome | hormone | systems biology | Arabidopsis thalianaA major objective in the postgenomic era is to assign detailed molecular function(s) to the many protein-coding genes that remain uncharacterized even in model organisms such as the reference plant Arabidopsis thaliana (1). It is estimated that Arabidopsis contains more than 2,000 transcription factors and transcriptional regulators (hereafter "TFs"), most of which are uncharacterized (2). TFs function as key players in plant hormone signal transduction pathways and are responsible for directing the widespread changes in gene expression that are essential for the regulation of growth and development in plants (3, 4). The TFs within these transcriptional regulatory networks do not work independently but rather undergo complex interactions with other proteins (2). Understanding how these TFs interact with other proteins will ultimately lead to a greater comprehension of biological systems.For determination of physical protein-protein interactions (PPIs), protein microarrays (5-10) are complementary to other PPI technologies such as the yeast two-hybrid system (Y2H) (11) and protein complex purification coupled with mass spectrometry (AP-MS) (12, 13). With conventional protein microarrays it is necessary to purify thousands of in vivo expressed proteins and to spot these purified proteins on a solid surface (5-8). In contrast, in situ synthesis protein microarray technologies simplify protein microarray fabrication by circumventing the steps of in vivo protein expression and purification (9,10,14,15). This streamlining facilitates an increase in the number of target genes that can be assayed, allowing for thousands of protein-encoding plasmids to be spotted at lower cost and in less time. Such ...

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confidence: 99%

Mapping transcription factor interactome networks using HaloTag protein arrays

Yazaki

Galli

Kim

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…This innovative technique was developed by He et al (2008a). DAPA makes it possible to repeatedly use the same DNA template slide for printing multiple protein arrays.…”

Section: Dapamentioning

confidence: 99%

Emerging technology of in situ cell free expression protein microarrays

et al. 2012

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Recently, in situ protein microarrays have been developed for large scale analysis and high throughput studies of proteins. In situ protein microarrays produce proteins directly on the solid surface from pre-arrayed DNA or RNA. The advances in in situ protein microarrays are exemplified by the ease of cDNA cloning and cell free protein expression. These technologies can evaluate, validate and monitor protein in a cost effective manner and address the issue of a high quality protein supply to use in the array. Here we review the importance of recently employed methods: PISA (protein in situ array), DAPA (DNA array to protein array), NAPPA (nucleic acid programmable protein array) and TUS-TER microarrays and the role of these methods in proteomics.

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“…The analysis of biological data becomes the bottleneck of current biological research, and its processing, mining and analysis and understanding of the increasingly urgent requirements. At present, there are some problems in the analysis of biological data (He and Stoevesandt, 2008). For example, the algorithm model used in data analysis is becoming more and more complicated, and the results obtained by the black box algorithm used in data analysis are difficult to make biological interpretation.…”

Section: Introductionmentioning

confidence: 99%

Genes Expression Data Processing Algorithm Based on Hybrid Particle Swarm Optimization

2016

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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In order to improve the accuracy of structural and functional information of gene experiment data, and to better identify gene data related to function, a PSO-based hybrid algorithm(HPSO) was proposed and HPSO algorithm was applied to cluster analysis of gene expression data. And the problem of clustering analysis of gene expression data is reduced to an optimization problem, then a gene expression data processing algorithm based on HPSO is proposed, and an algorithm of gene expression data processing based on HPSO is proposed, and the HPSO algorithm was used to analyze the protein gene structure data, and a lot of useful rules were obtained. The experimental results show that the HPSO algorithm can cluster well, which opens up a new method for the analysis of gene expression data.

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Printing protein arrays from DNA arrays

Cited by 146 publications

References 14 publications

Mapping transcription factor interactome networks using HaloTag protein arrays

Mapping transcription factor interactome networks using HaloTag protein arrays

Emerging technology of in situ cell free expression protein microarrays

Genes Expression Data Processing Algorithm Based on Hybrid Particle Swarm Optimization

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