231 research outputs found
Pattern matching and pattern discovery algorithms for protein topologies
We describe algorithms for pattern matching and pattern
learning in TOPS diagrams (formal descriptions of protein topologies).
These problems can be reduced to checking for subgraph isomorphism
and finding maximal common subgraphs in a restricted class of ordered
graphs. We have developed a subgraph isomorphism algorithm for
ordered graphs, which performs well on the given set of data. The
maximal common subgraph problem then is solved by repeated
subgraph extension and checking for isomorphisms. Despite the
apparent inefficiency such approach gives an algorithm with time
complexity proportional to the number of graphs in the input set and is
still practical on the given set of data. As a result we obtain fast
methods which can be used for building a database of protein
topological motifs, and for the comparison of a given protein of known
secondary structure against a motif database
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A computer system to perform structure comparison using TOPS representations of protein structure
We describe the design and implementation of a fast topology–based method
for protein structure comparison. The approach uses the TOPS topological representation
of protein structure, aligning two structures using a common discovered
pattern and generating measure of distance derived from an insert score. Heavy
use is made of a constraint-based pattern matching algorithm for TOPS diagrams
that we have designed and described elsewhere Gilbert et al. (1999). The comparison
system is maintained at the European Bioinformatics Institute and is available
over the Web via the at tops.ebi.ac.uk/tops. Users submit a structure description in
Protein Data Bank (PDB) format and can compare it with structures in the entire
PDB or a representative subset of protein domains, receiving the results by email
Domain discovery method for topological profile searches in protein structures
We describe a method for automated domain discovery for topological profile searches in protein
structures. The method is used in a system TOPStructure for fast prediction of CATH classification
for protein structures (given as PDB files). It is important for profile searches in multi-domain
proteins, for which the profile method by itself tends to perform poorly. We also present an
O(C(n)k +nk2) time algorithm for this problem, compared to the O(C(n)k +(nk)2) time used by
a trivial algorithm (where n is the length of the structure, k is the number of profiles and C(n) is the
time needed to check for a presence of a given motif in a structure of length n). This method has
been developed and is currently used for TOPS representations of protein structures and prediction
of CATH classification, but may be applied to other graph-based representations of protein or RNA
structures and/or other prediction problems. A protein structure prediction system incorporating
the domain discovery method is available at http://bioinf.mii.lu.lv/tops/
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Topology-based protein structure comparison using a pattern discovery technique
Tableau-based protein substructure search using quadratic programming
<p>Abstract</p> <p>Background</p> <p>Searching for proteins that contain similar substructures is an important task in structural biology. The exact solution of most formulations of this problem, including a recently published method based on tableaux, is too slow for practical use in scanning a large database.</p> <p>Results</p> <p>We developed an improved method for detecting substructural similarities in proteins using tableaux. Tableaux are compared efficiently by solving the quadratic program (QP) corresponding to the quadratic integer program (QIP) formulation of the extraction of maximally-similar tableaux. We compare the accuracy of the method in classifying protein folds with some existing techniques.</p> <p>Conclusion</p> <p>We find that including constraints based on the separation of secondary structure elements increases the accuracy of protein structure search using maximally-similar subtableau extraction, to a level where it has comparable or superior accuracy to existing techniques. We demonstrate that our implementation is able to search a structural database in a matter of hours on a standard PC.</p
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Fast and accurate protein substructure searching with simulated annealing and GPUs
<p>Abstract</p> <p>Background</p> <p>Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.</p> <p>Results</p> <p>We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).</p> <p>Conclusions</p> <p>The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.</p
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