RemoteHomologues: Identifying clusters of remote homologues

Detecting proteins which share a common ancestor is an important step in understanding protein structure and function. Multi-domain proteins normally cause problems due to spurious similarities they induce; with a simple graph-based approach based on the concept of asymmetric similarity we were able to clearly outperform PSI-Blast.

For further information contact Alexander Schliep (alexander@schlieplab.org). This project is connected to the following projects: Proclust.

Team

Members: Alexander Schliep, Alexander Schliep. Collaborators: Dietmar Schomburg (University of Cologne, Institute for Biochemistry), Alexander Schönhuth (Centrum Wiskunde & Informatica).

Publications

Pipenbacher et al.. ProClust: improved clustering of protein sequences with an extended graph-based approach. Bioinformatics 2002, 18 Suppl 2, S182–S191.

Bolten et al.. Clustering protein sequences-structure prediction by transitive homology. Bioinformatics 2001, 17:10, 935–41.

Pipenbacher. Evaluation and extension of a graph based clustering approach for the detection of remote homologs. Master's Thesis, University of Cologne, 2001.

Bolten et al.. Strongly Connected Components can Predict Protein Structure. In Electron. Notes Discret. Math., 8, 10–13, 2001. Extended Abstract, 1st Cologne-Twente Workshop on Graphs and Combinatorial Optimization.

Bolten. A graph based clustering method for the detection of remote homolog protein sequences. Master's Thesis, University of Cologne, 2000. In German..