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A new method for modeling and solving the protein fold recognition problem [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 1998.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
12 pages : digital, PDF file
Additional Creators
United States. Department of Energy and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Computational recognition of native-like folds from a protein fold database is considered to be a promising alternative approach to the ab initio fold prediction. We present a new and effective method for protein fold recognition through optimally aligning (threading) an amino acid sequence and a protein fold (template). A protein fold, in our database, is represented as a series of core secondary structures, and the alignment quality is determined by three factors. They are (1) the fitness between each amino acid and the environment of its assigned (aligned) template position; (2) pairwise interaction preferences between amino acids that are spatially close; and (3) alignment gap penalties. Our threading algorithm constructs an optimum alignment between an amino acid sequence of size n and a protein fold template of size m in 0((m + n{sup 1+0.5C}-M log(n))n{sup C+1}) time and 0(nm + n{sup C+2}) space, where M is the number of core secondary structures in the fold, and C is a (small) nonnegative integer, determined by a mathematical property of the pairwise interactions in the fold. C is less than or equal to 3 for about 90% of the 296 unique folds in our database, when pairwise interactions are restricted to amino acids < 6Å apart (measured between their beta carbon atoms). An approximation scheme is developed for fold templates with C > 3, when threading requires too much memory and time to be practical on a typical workstation.
Report Numbers
E 1.99:ornl/cp--95147
E 1.99: conf-980313--
conf-980313--
ornl/cp--95147
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Other Subject(s)
Note
Published through SciTech Connect.
12/31/1998.
"ornl/cp--95147"
" conf-980313--"
"DE98001253"
2. annual international conference on computational molecular biology, New York, NY (United States), 22-25 Mar 1998.
Uberbacher, E.C.; Xu, Ying; Xu, Dong.
Lockheed Martin Energy Research, Inc., Oak Ridge, TN (United States)
Lockheed Martin Energy Systems, Inc., Oak Ridge, TN (United States)
Funding Information
AC05-96OR22464
AC05-84OR21400
View MARC record | catkey: 14351503