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Bioinformatics Vol. 19 no. 18 2003
pages 2442-2447
© 2003 Oxford University Press

Accelerating screening of 3D protein data with a graph theoretical approach

Cornelius Frömmel 1, Christoph Gille 1,*, Andrean Goede 1, Clemens Gröpl 2, Stefan Hougardy 2, Till Nierhoff 2, Robert Preißner 1 and Martin Thimm 2

1 Institut für Biochemie, Charité, Monbijoustr. 2, Berlin, D-10117, Germany and 2 Institut für Informatik, Humboldt-Universität zu Berlin, Berlin, D-10099, Germany

Received on March 14, 2003 ; revised on June 11, 2003 ; accepted on June 20, 2003

Motivation: The Dictionary of Interfaces in Proteins (DIP) is a database collecting the 3D structure of interacting parts of proteins that are called patches. It serves as a repository, in which patches similar to given query patches can be found. The computation of the similarity of two patches is time consuming and traversing the entire DIP requires some hours. In this work we address the question of how the patches similar to a given query can be identified by scanning only a small part of DIP. The answer to this question requires the investigation of the distribution of the similarity of patches.

Results: The score values describing the similarity of two patches can roughly be divided into three ranges that correspond to different levels of spatial similarity. Interestingly, the two iso-score lines separating the three classes can be determined by two different approaches. Applying a concept of the theory of random graphs reveals significant structural properties of the data in DIP. These can be used to accelerate scanning the DIP for patches similar to a given query. Searches for very similar patches could be accelerated by a factor of more than 25. Patches with a medium similarity could be found 10 times faster than by brute-force search.

Contact: christoph.gille{at}charite.de

* To whom correspondence should be addressed.


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