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MOTIVATION: Propagating functional annotations to sequence-similar, presumably homologous proteins lies at the heart of the bioinformatics industry. Correct propagation is crucially dependent on the accurate identification of subtle sequence motifs that are conserved in evolution. The evolutionary signal can be difficult to detect because functional sites may consist of non-contiguous residues while segments in-between may be mutated without affecting fold or function. RESULTS: Here, we report a novel graph clustering algorithm in which all known protein sequences simultaneously self-organize into hypothetical multiple sequence alignments. This eliminates noise so that non-contiguous sequence motifs can be tracked down between extremely distant homologues. The novel data structure enables fast sequence database searching methods which are superior to profile-profile comparison at recognizing distant homologues. This study will boost the leverage of structural and functional genomics and opens up new avenues for data mining a complete set of functional signature motifs. AVAILABILITY: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original publication




Journal article



Publication Date





2361 - 2367


Algorithms, Amino Acid Sequence, Databases, Protein, Evolution, Molecular, Information Storage and Retrieval, Molecular Sequence Data, Sensitivity and Specificity, Sequence Alignment, Sequence Analysis, Protein, Sequence Homology, Amino Acid