Automated prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-12

AUTHORS

Xiao-Ping Xu, David A. Case

ABSTRACT

A database of peptide chemical shifts, computed at the density functional level, has been used to develop an algorithm for prediction of 15N and 13C shifts in proteins from their structure; the method is incorporated into a program called SHIFTS (version 4.0). The database was built from the calculated chemical shift patterns of 1335 peptides whose backbone torsion angles are limited to areas of the Ramachandran map around helical and sheet configurations. For each tripeptide in these regions of regular secondary structure (which constitute about 40% of residues in globular proteins) SHIFTS also consults the database for information about sidechain torsion angle effects for the residue of interest and for the preceding residue, and estimates hydrogen bonding effects through an empirical formula that is also based on density functional calculations on peptides. The program optionally searches for alternate side-chain torsion angles that could significantly improve agreement between calculated and observed shifts. The application of the program on 20 proteins shows good consistency with experimental data, with correlation coefficients of 0.92, 0.98, 0.99 and 0.90 and r.m.s. deviations of 1.94, 0.97, 1.05, and 1.08 ppm for 15N, 13Calpha, 13Cbeta and 13C', respectively. Reference shifts fit to protein data are in good agreement with 'random-coil' values derived from experimental measurements on peptides. This prediction algorithm should be helpful in NMR assignment, crystal and solution structure comparison, and structure refinement. More... »

PAGES

321-333

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1013324104681

DOI

http://dx.doi.org/10.1023/a:1013324104681

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036619595

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/11824752


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