DETECT

DETECT predicts and probabilistically classifies enzymes by integrating global alignment scores into a likelihood model to improve enzyme annotation and account for sequence diversity.

Key Features:

• Probabilistic Methodology: Employs a probabilistic framework that integrates likelihood scores to account for sequence diversity across enzyme families.
• Global Alignment Scores: Calculates integrated likelihoods by comparing global alignment scores of an unknown protein against those of known enzymes.
• Reaction Class Ranking: Ranks reaction classes relevant to a query protein based on integrated likelihood scores derived from global alignments.
• Improved Annotation Accuracy: Demonstrates improved enzyme annotation accuracy relative to homology-only methods such as BLAST, enabling identification of annotation errors and prediction of novel enzymes, including applications to Plasmodium falciparum.

Scientific Applications:

• Enzyme Classification: Provides probabilistic assignment of proteins to enzyme families using integrated alignment-derived likelihoods.
• Genomic Annotation: Improves the reliability of enzyme annotations within genomic datasets by providing per-prediction reliability measures.
• Therapeutic Discovery: Facilitates identification of novel enzymes and potential therapeutic targets, exemplified by analyses of Plasmodium falciparum.

Methodology:

Integrates global alignment scores into a probabilistic likelihood model by comparing query protein alignments to those of known enzymes to compute integrated likelihood scores, rank reaction classes, and provide prediction reliability.