Dataset Information

The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium.

ABSTRACT: We describe the core Protein Production Platform of the Northeast Structural Genomics Consortium (NESG) and outline the strategies used for producing high-quality protein samples. The platform is centered on the cloning, expression and purification of 6X-His-tagged proteins using T7-based Escherichia coli systems. The 6X-His tag allows for similar purification procedures for most targets and implementation of high-throughput (HTP) parallel methods. In most cases, the 6X-His-tagged proteins are sufficiently purified (>97% homogeneity) using a HTP two-step purification protocol for most structural studies. Using this platform, the open reading frames of over 16,000 different targeted proteins (or domains) have been cloned as>26,000 constructs. Over the past 10 years, more than 16,000 of these expressed protein, and more than 4400 proteins (or domains) have been purified to homogeneity in tens of milligram quantities (see Summary Statistics, http://nesg.org/statistics.html). Using these samples, the NESG has deposited more than 900 new protein structures to the Protein Data Bank (PDB). The methods described here are effective in producing eukaryotic and prokaryotic protein samples in E. coli. This paper summarizes some of the updates made to the protein production pipeline in the last 5 years, corresponding to phase 2 of the NIGMS Protein Structure Initiative (PSI-2) project. The NESG Protein Production Platform is suitable for implementation in a large individual laboratory or by a small group of collaborating investigators. These advanced automated and/or parallel cloning, expression, purification, and biophysical screening technologies are of broad value to the structural biology, functional proteomics, and structural genomics communities.

SUBMITTER: Xiao R

PROVIDER: S-EPMC4110633 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium.

Xiao Rong R Anderson Stephen S Aramini James J Belote Rachel R Buchwald William A WA Ciccosanti Colleen C Conover Ken K Everett John K JK Hamilton Keith K Huang Yuanpeng Janet YJ Janjua Haleema H Jiang Mei M Kornhaber Gregory J GJ Lee Dong Yup DY Locke Jessica Y JY Ma Li-Chung LC Maglaqui Melissa M Mao Lei L Mitra Saheli S Patel Dayaban D Rossi Paolo P Sahdev Seema S Sharma Seema S Shastry Ritu R Swapna G V T GV Tong Saichu N SN Wang Dongyan D Wang Huang H Zhao Li L Montelione Gaetano T GT Acton Thomas B TB

Journal of structural biology 20100803 1

We describe the core Protein Production Platform of the Northeast Structural Genomics Consortium (NESG) and outline the strategies used for producing high-quality protein samples. The platform is centered on the cloning, expression and purification of 6X-His-tagged proteins using T7-based Escherichia coli systems. The 6X-His tag allows for similar purification procedures for most targets and implementation of high-throughput (HTP) parallel methods. In most cases, the 6X-His-tagged proteins are s ...[more]

PMID: 20688167

Similar Datasets

Project description:BACKGROUND:The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. RESULTS:To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. CONCLUSION:DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.

Project description:Structural genomic projects envision almost routine protein structure determinations, which are currently imaginable only for small proteins with molecular weights below 25,000 Da. For larger proteins, structural insight can be obtained by breaking them into small segments of amino acid sequences that can fold into native structures, even when isolated from the rest of the protein. Such segments are autonomously folding units (AFU) and have sizes suitable for fast structural analyses. Here, we propose to expand an intuitive procedure often employed for identifying biologically important domains to an automatic method for detecting putative folded protein fragments. The procedure is based on the recognition that large proteins can be regarded as a combination of independent domains conserved among diverse organisms. We thus have developed a program that reorganizes the output of BLAST searches and detects regions with a large number of similar sequences. To automate the detection process, it is reduced to a simple geometrical problem of recognizing rectangular shaped elevations in a graph that plots the number of similar sequences at each residue of a query sequence. We used our program to quantitatively corroborate the premise that segments with conserved sequences correspond to domains that fold into native structures. We applied our program to a test data set composed of 99 amino acid sequences containing 150 segments with structures listed in the Protein Data Bank, and thus known to fold into native structures. Overall, the fragments identified by our program have an almost 50% probability of forming a native structure, and comparable results are observed with sequences containing domain linkers classified in SCOP. Furthermore, we verified that our program identifies AFU in libraries from various organisms, and we found a significant number of AFU candidates for structural analysis, covering an estimated 5 to 20% of the genomic databases. Altogether, these results argue that methods based on sequence similarity can be useful for dissecting large proteins into small autonomously folding domains, and such methods may provide an efficient support to structural genomics projects.

Dataset Information

The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium.

Publications

The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets