Title |
A probabilistic approach for validating protein NMR chemical shift assignments
|
---|---|
Published in |
Journal of Biomolecular NMR, May 2010
|
DOI | 10.1007/s10858-010-9407-y |
Pubmed ID | |
Authors |
Bowei Wang, Yunjun Wang, David S. Wishart |
Abstract |
It has been estimated that more than 20% of the proteins in the BMRB are improperly referenced and that about 1% of all chemical shift assignments are mis-assigned. These statistics also reflect the likelihood that any newly assigned protein will have shift assignment or shift referencing errors. The relatively high frequency of these errors continues to be a concern for the biomolecular NMR community. While several programs do exist to detect and/or correct chemical shift mis-referencing or chemical shift mis-assignments, most can only do one, or the other. The one program (SHIFTCOR) that is capable of handling both chemical shift mis-referencing and mis-assignments, requires the 3D structure coordinates of the target protein. Given that chemical shift mis-assignments and chemical shift re-referencing issues should ideally be addressed prior to 3D structure determination, there is a clear need to develop a structure-independent approach. Here, we present a new structure-independent protocol, which is based on using residue-specific and secondary structure-specific chemical shift distributions calculated over small (3-6 residue) fragments to identify mis-assigned resonances. The method is also able to identify and re-reference mis-referenced chemical shift assignments. Comparisons against existing re-referencing or mis-assignment detection programs show that the method is as good or superior to existing approaches. The protocol described here has been implemented into a freely available Java program called "Probabilistic Approach for protein Nmr Assignment Validation (PANAV)" and as a web server ( http://redpoll.pharmacy.ualberta.ca/PANAV ) which can be used to validate and/or correct as well as re-reference assigned protein chemical shifts. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
France | 1 | 1% |
Brazil | 1 | 1% |
New Zealand | 1 | 1% |
India | 1 | 1% |
Russia | 1 | 1% |
Belgium | 1 | 1% |
Unknown | 67 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 24 | 32% |
Researcher | 20 | 27% |
Professor > Associate Professor | 5 | 7% |
Professor | 4 | 5% |
Student > Master | 4 | 5% |
Other | 8 | 11% |
Unknown | 10 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 21 | 28% |
Biochemistry, Genetics and Molecular Biology | 17 | 23% |
Chemistry | 16 | 21% |
Computer Science | 7 | 9% |
Mathematics | 1 | 1% |
Other | 3 | 4% |
Unknown | 10 | 13% |