General Information
| Description | Complete package for reduced dimensionality in protein NMR spectroscopy |
| Homepage | http://pine.nmrfam.wisc.edu/adapt-nmr-enhancer |
Publications
ADAPT-NMR Enhancer: complete package for reduced dimensionality in protein NMR spectroscopy
Summary:
ADAPT-nuclear magnetic resonance (ADAPT-NMR) offers an automated approach to the concurrent acquisition and processing of protein NMR data with the goal of complete backbone and side chain assignments. What the approach lacks is a useful graphical interface for reviewing results and for searching for missing peaks that may have prevented assignments or led to incorrect assignments. Because most of the data ADAPT-NMR collects are 2D tilted planes used to find peaks in 3D spectra, it would be helpful to have a tool that reconstructs the 3D spectra. The software package reported here, ADAPT-NMR Enhancer, supports the visualization of both 2D tilted planes and reconstructed 3D peaks on each tilted plane. ADAPT-NMR Enhancer can be used interactively with ADAPT-NMR to automatically assign selected peaks, or it can be used to produce PINE-SPARKY-like graphical dialogs that support atom-by-atom and peak-by-peak assignment strategies. Results can be exported in various formats, including XEASY proton file (.prot), PINE pre-assignment file (.str), PINE probabilistic output file, SPARKY peak list file (.list) and TALOS+ input file (.tab). As an example, we show how ADAPT-NMR Enhancer was used to extend the automated data collection and assignment results for the protein Aedes aegypti sterol carrier protein 2.
Availability:
The program, in the form of binary code along with tutorials and reference manuals, is available at http://pine.nmrfam.wisc.edu/adapt-nmr-enhancer.
Contact:
whlee@nmrfam.wisc.edu or markley@nmrfam.wisc.edu
Related Tools
WaVPeak
Published February 2012
Picking NMR peaks through wavelet-based smoothing and volume-based filtering
MS-REDUCE
Published January 2016
An ultrafast technique for reduction of big mass spectrometry data for high-throughput processing
BiRen
Published February 2017
Predicting enhancers with a deep-learning-based model using the DNA sequence alone
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
