Published NMR studies utilizing the BMRB database
The following pages present the abstracts of articles that used the BMRB database in their
studies. Also included are lists of the proteins that they used along with their accession
codes, and links to the Pubmed abstracts or entire articles where available.
An Empirical Correlation
between Secondary Structure Content and Averaged Chemical Shifts in Proteins
Chemical shift values of more than 200 proteins obtained from the Biological Magnetic
Resonance Bank are used to calculate averaged chemical shift (ACS) values, and secondary
structure content (SSC) is estimated from the corresponding three-dimensional
coordinates obtained from the Protein Data Bank. The results suggest that the
correlation between ACS and SSC can be used to estimate secondary structure content
and to monitor large-scale secondary structural changes in protein, as in folding
Chemical shift index
This 13C chemical shift database uses backbone 13C data from
the BMRB database to identify protein secondary structures.
Chemical shifts in denatured proteins
Chemical shifts in denatured proteins: resonance assignments for denatured
ubiquitin and comparisons with other denatured proteins.
Disulfide bond prediction
This article demonstrates that cys Ca and
Cb 13C chemical shift data can be
used to effectively predict disulfide bonding by following 3 basic ground rules.
A tool for automated sequential assignment of protein spectra from triple
Improved Technologies now Routinely
Provide Protein NMR Structures Useful for Molecular Replacement
This is a comprehensive investigation of the utility of protein NMR ensembles as
Molecular Replacement (MR) search models, using 25 pairs of X-ray and NMR structures
solved and refined using modern NMR methods.
BioMagResBank database with sets of experimental NMR constraints corresponding
to the structures of over 1400 biomolecules deposited in the Protein Data Bank
13C', 1HN and 15N chemical shifts.
PROSHIFT: Protein chemical shift prediction
using artificial neural networks
A neural network was trained to predict the 1H, 13C, and
15N chemical shifts of proteins using their three-dimensional
structure as well as experimental conditions as input parameters.
Protein structural class identification directly
from NMR spectra using averaged chemical shifts
This article explores the possibility of determining the structural classes of
proteins directly from their NMR spectra, prior to resonance assignment, using
averaged chemical shifts.
This program was written to aid in the assignment of 1H NMR spectra
by using neural network technology to locate groups of related amino acids, and
then to identify individual amino acids within those groups.
A database of uniformly referenced protein chemical shifts.
This program is an aid in assiging the 1H and 13C
NMR spectra. It does so by predicting the chemical shifts of unassigned
proteins through an analysis of assigned chemical shifts for proteins
with homologous sequences.
Sorting signals from protein NMR spectra: SPI, a Bayesian protocol for
uncovering spin systems.
A standardized protein NMR storage. A data dictionary and object-oriented
relational database for archiving protein NMR spectra.
Structure prediction of protein complexes
by an NMR-based protein docking algorithm
This paper discusses a variant of the protein-protein docking problem,
where the input consists of the tertiary structures of proteins A and
B plus an unassigned one-dimensional 1H-NMR spectrum of the complex AB.
This program uses 13Ca,
1Ha and 15N
chemical shifts assignments to predict protein backbone torsion angles
using a database of high resolution X-ray structures.
This program uses BMRB chemical shift data to automate the assignment of
1HN and 15N chemical shifts.
Toward direct protein conformation
This article is a case study of For-L-Val-NH2 conformations, comparing
ab initio calculated conformation values based on BMRB chemical shift data
to experimentally verified conformations.
Type I and II beta-Turns
A theoretical case study of type I and type II beta-Turns.