Finding Largest Well-Predicted Subset of Protein Structure Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Shuai Cheng Li , Dongbo Bu , Jinbo Xu , Ming Li

ABSTRACT

How to evaluate the quality of models is a basic problem for the field of protein structure prediction. Numerous evaluation criteria have been proposed, and one of the most intuitive criteria requires us to find a largest well-predicted subset — a maximum subset of the model which matches the native structure [12]. The problem is solvable in O(n 7) time, albeit too slow for practical usage. We present a (1 + ε)d distance approximation algorithm that runs in time O(n 3logn/ε 5) for general protein structures. In the case of globular proteins, this result can be enhanced to a randomized O(nlog2 n) time algorithm with probability at least 1 − O(1/n). In addition, we propose a (1 + ε)-approximation algorithm to compute the minimum distance to fit all the points of a model to its native structure in time O(n(loglogn + log1/ε)/ε 5). We have implemented our algorithms and results indicate our program finds much more matched pairs with less running time than TMScore, which is one of the most popular tools to assess the quality of predicted models. More... »

PAGES

44-55

References to SciGraph publications

  • 2004. A Combinatorial Shape Matching Algorithm for Rigid Protein Docking in COMBINATORIAL PATTERN MATCHING
  • 2003. Protein Structure Comparison: Algorithms and Applications in MATHEMATICAL METHODS FOR PROTEIN STRUCTURE ANALYSIS AND DESIGN
  • 2003-02-11. Computing Largest Common Point Sets under Approximate Congruence in ALGORITHMS - ESA 2000
  • 2006. An Efficient Approximation Algorithm for Point Pattern Matching Under Noise in LATIN 2006: THEORETICAL INFORMATICS
  • Book

    TITLE

    Combinatorial Pattern Matching

    ISBN

    978-3-540-69066-5
    978-3-540-69068-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-69068-9_7

    DOI

    http://dx.doi.org/10.1007/978-3-540-69068-9_7

    DIMENSIONS

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