Instruct-ERIC
Instruct-ERIC integrates high-end structural biology services and experimental data to support translational research in protein structure and function.
Key Features:
- Comprehensive Integration: Integrates experimental methods including NMR spectroscopy, solid-state NMR, quantitative in-cell NMR, cryo-electron microscopy (cryo-EM), cryo-FIB micromachining, and X-ray crystallography.
- Automation and Efficiency: Provides automated workflows and automated crystallography pipelines to process and increase throughput of complex datasets from high-throughput and fragment-based screening.
- Data Management and Analysis: Offers robust data management for storage and retrieval and supports advanced analytical tools for analysis of large experimental datasets.
Scientific Applications:
- Fragment-Based Lead Discovery: Supports fragment-based lead discovery using nano-differential scanning fluorimetry and NMR-based fragment screening with minimal sample requirements.
- Protein–Ligand Interaction Studies: Enables real-time quantitative in-cell NMR to monitor protein–ligand interactions within human cells.
- High-Resolution Structural Analysis: Facilitates high-resolution studies of proteins embedded in natural membranes using solid-state NMR.
- Cryo-EM and Cryo-FIB Micromachining: Supports cryo-FIB micromachining workflows for sample preparation and cryo-electron tomography data acquisition.
Methodology:
Automated workflows and automated crystallography pipelines (EMBL HTX) for data management and analysis of high-throughput and fragment screening datasets.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Windows, Linux
- Added:
- 5/24/2022
- Last Updated:
- 5/24/2022
Operations
Publications
Wienk H, Banci L, Daenke S, Pereiro E, Schwalbe H, Stuart DI, Weiss MS, Perrakis A. iNEXT-Discovery and Instruct-ERIC: Integrating High-End Services for Translational Research in Structural Biology. Journal of Visualized Experiments. 2021. doi:10.3791/63435. PMID:34866631.
DOI: 10.3791/63435
PMID: 34866631