@TechReport{Dartmouth:TR2003-439,
author = {Chris J. Langmead and Bruce R. Donald},
title = {{3D-Structural Homology Detection via Unassigned Residual Dipolar Couplings}},
institution = {Dartmouth College, Computer Science},
address = {Hanover, NH},
number = {TR2003-439},
year = {2003},
month = {January},
URL = {ftp://ftp.cs.dartmouth.edu/TR/TR2003-439.pdf},
comment = {
A revised and expanded version of this TR will appear as a refereed
paper at the
IEEE Computer Society Bioinformatics Conference
,
Stanford, California (August, 2003),
},
abstract = {
Recognition of a protein's fold provides valuable
information about its function. While many sequence-based
homology prediction methods exist, an important challenge remains:
two highly dissimilar sequences can have similar folds --- how can
we detect this rapidly, in the context of structural genomics?
High-throughput NMR experiments, coupled with novel algorithms for
data analysis, can address this challenge. We report an automated
procedure for detecting 3D-structural homologies from sparse,
unassigned protein NMR data.
Our method identifies the 3D-structural models in a protein
structural database whose geometries best fit the unassigned
experimental NMR data. It does not use sequence information and is
thus not limited by sequence homology. The method can also be
used to confirm or refute structural predictions made by other
techniques such as protein threading or sequence homology. The
algorithm runs in O(pnk3) time, where p is the number of
proteins in the database, n is the number of residues in the
target protein, and k is the resolution of a rotation search.
The method requires only uniform 15N-labelling of the protein
and processes unassigned 1H-15N residual dipolar couplings,
which can be acquired in a couple of hours. Our experiments on NMR
data from 5 different proteins demonstrate that the method
identifies closely related protein folds, despite low-sequence
homology between the target protein and the computed
model.
}
}