![]() |
Dartmouth College Computer Science Technical Report series |
CS home TR home TR search TR listserv |
By author: | A B C D E F G H I J K L M N O P Q R S T U V W X Y Z | |
By number: | 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986 |
Abstract:
One goal of the structural genomics initiative is the identification of new
protein folds. Sequence-based structural homology prediction
methods are an important means for prioritizing unknown proteins
for structure determination. However, 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, called HD, for detecting 3D
structural homologies from sparse, unassigned protein NMR
data. Our method identifies 3D models in a protein structural
database whose geometries best fit the unassigned experimental NMR
data. HD does not use, 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 homology modelling. The algorithm runs in $O(pn^{5/2}
\log {(cn)} + p \log p)$ time, where $p$ is the number of proteins
in the database, $n$ is the number of residues in the target
protein and $c$ is the maximum edge weight in an integer-weighted
bipartite graph. Our experiments on real NMR data from 3
different proteins against a database of 4,500 representative
folds demonstrate that the method identifies closely related
protein folds, including sub-domains of larger proteins, with as
little as 10-30\% sequence homology between the target protein (or
sub-domain) and the computed model. In particular, we report no
false-negatives or false-positives despite significant percentages
of missing experimental data.
Note:
A revised version of this paper will appear in the IEEE
Computational Systems Bioinformatics Conference (CSB),
Stanford CA. (August, 2004).
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Christopher J. Langmead and
Bruce R. Donald,
"High-Throughput 3D Homology Detection via NMR Resonance Assignment."
Dartmouth Computer Science Technical Report TR2004-487,
September 2003.
Notify me about new tech reports.
To receive paper copy of a report, by mail, send your address and the TR number to reports AT cs.dartmouth.edu
Copyright notice: The documents contained in this server are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Technical reports collection maintained by David Kotz.