231 research outputs found

    Pattern matching and pattern discovery algorithms for protein topologies

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    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

    Domain discovery method for topological profile searches in protein structures

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    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/

    Tableau-based protein substructure search using quadratic programming

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    <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

    Interactive visualisation and exploration of biological data

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    International audienceno abstrac

    Fast and accurate protein substructure searching with simulated annealing and GPUs

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    <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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