DETECT--a density estimation tool for enzyme classification and its application to Plasmodium falciparum.
BACKGROUND
A major challenge in genomics is the accurate annotation of component genes. Enzymes are typically predicted using homology-based search methods, where the membership of a protein to an enzyme family is based on single-sequence comparisons. As such, these methods are often error-prone and lack useful measures of reliability for the prediction.


RESULTS
Here, we present DETECT, a probabilistic method for enzyme prediction that accounts for the sequence diversity across enzyme families. By comparing the global alignment scores of an unknown protein to those of all known enzymes, an integrated likelihood score can be readily calculated, ranking the reaction classes relevant for that protein. Comparisons to BLAST reveal significant improvements in enzyme annotation accuracy. Applied to Plasmodium falciparum, we identify potential annotation errors and predict novel enzymes of therapeutic interest.


BACKGROUND
A standalone application is available from the website: http://www.compsysbio.org/projects/DETECT/
Program in Molecular Structure and Function, Hospital for Sick Children, 15-704 MaRS TMDT East, 101 College Street, Toronto, ON M5G 1L7, Canada.
Stacy S
SS
Hung
James
J
Wasmuth
Christopher
C
Sanford
John
J
Parkinson
D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000145:classification* / Q000235:genetics; D023281:Genomics / Q000379:methods*; D010963:Plasmodium falciparum / Q000235:genetics* / Q000378:metabolism; D011506:Proteins / Q000235:genetics; D016415:Sequence Alignment; D012984:Software
D004798:Enzymes; D011506:Proteins