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Identification of novel restriction endonuclease-like fold families among hypothetical proteins.


ABSTRACT: Restriction endonucleases and other nucleic acid cleaving enzymes form a large and extremely diverse superfamily that display little sequence similarity despite retaining a common core fold responsible for cleavage. The lack of significant sequence similarity between protein families makes homology inference a challenging task and hinders new family identification with traditional sequence-based approaches. Using the consensus fold recognition method Meta-BASIC that combines sequence profiles with predicted protein secondary structure, we identify nine new restriction endonuclease-like fold families among previously uncharacterized proteins and predict these proteins to cleave nucleic acid substrates. Application of transitive searches combined with gene neighborhood analysis allow us to confidently link these unknown families to a number of known restriction endonuclease-like structures and thus assign folds to the uncharacterized proteins. Finally, our method identifies a novel restriction endonuclease-like domain in the C-terminus of RecC that is not detected with structure-based searches of the existing PDB database.

SUBMITTER: Kinch LN 

PROVIDER: S-EPMC1157100 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

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Identification of novel restriction endonuclease-like fold families among hypothetical proteins.

Kinch Lisa N LN   Ginalski Krzysztof K   Rychlewski Leszek L   Grishin Nick V NV  

Nucleic acids research 20050622 11


Restriction endonucleases and other nucleic acid cleaving enzymes form a large and extremely diverse superfamily that display little sequence similarity despite retaining a common core fold responsible for cleavage. The lack of significant sequence similarity between protein families makes homology inference a challenging task and hinders new family identification with traditional sequence-based approaches. Using the consensus fold recognition method Meta-BASIC that combines sequence profiles wi  ...[more]

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