Chapter title |
Rapid Prediction of Multi-dimensional NMR Data Sets Using FANDAS. - PubMed - NCBI
|
---|---|
Chapter number | 6 |
Book title |
Protein NMR
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7386-6_6 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7385-9, 978-1-4939-7386-6
|
Authors |
Siddarth Narasimhan, Deni Mance, Cecilia Pinto, Markus Weingarth, Alexandre M. J. J. Bonvin, Marc Baldus |
Abstract |
Solid-state NMR (ssNMR) can provide structural information at the most detailed level and, at the same time, is applicable in highly heterogeneous and complex molecular environments. In the last few years, ssNMR has made significant progress in uncovering structure and dynamics of proteins in their native cellular environments [1-4]. Additionally, ssNMR has proven to be useful in studying large biomolecular complexes as well as membrane proteins at the atomic level [5]. In such studies, innovative labeling schemes have become a powerful approach to tackle spectral crowding. In fact, selecting the appropriate isotope-labeling schemes and a careful choice of the ssNMR experiments to be conducted are critical for applications of ssNMR in complex biomolecular systems. Previously, we have introduced a software tool called FANDAS (Fast Analysis of multidimensional NMR DAta Sets) that supports such investigations from the early stages of sample preparation to the final data analysis [6]. Here, we present a new version of FANDAS, called FANDAS 2.0, with improved user interface and extended labeling scheme options allowing the user to rapidly predict and analyze ssNMR data sets for a given protein-based application. It provides flexible options for advanced users to customize the program for tailored applications. In addition, the list of ssNMR experiments that can be predicted now includes proton ((1)H) detected pulse sequences. FANDAS 2.0, written in Python, is freely available through a user-friendly web interface at http://milou.science.uu.nl/services/FANDAS . |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 23% |
Student > Master | 2 | 15% |
Student > Ph. D. Student | 1 | 8% |
Other | 1 | 8% |
Professor > Associate Professor | 1 | 8% |
Other | 0 | 0% |
Unknown | 5 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 15% |
Chemistry | 2 | 15% |
Physics and Astronomy | 1 | 8% |
Agricultural and Biological Sciences | 1 | 8% |
Unknown | 7 | 54% |