jEcho

jEcho predicts protein posttranslational modification (PTM) sites in silico to identify residue-level modification positions and support experimental prioritization.

Key Features:

• Evolved weight vector: Captures the variant contributions of amino acid positions flanking potential PTM sites.
• Evolutionary optimization: Uses an evolutionary algorithm to optimize the weight vector for improved prediction.
• Weighted nearest neighbor algorithm: Incorporates the optimized weight vector into a nearest neighbor classifier for PTM site prediction.
• PTM site prediction: Predicts residue-level posttranslational modification sites.
• Implementation: Implemented in Java.
• Performance: Reported superior performance compared to existing algorithms for PTM site prediction.

Scientific Applications:

• Residue-level PTM identification: In silico identification of PTM residues to complement experimental assays.
• Experimental prioritization: Prioritizes candidate modification sites for targeted experimental validation.
• Protein biology studies: Supports investigations into protein regulation and the role of flanking positions in PTM determination.

Methodology:

jEcho models flanking-position contributions via an evolved weight vector, optimizes that vector with an evolutionary algorithm, and applies a weighted nearest neighbor classifier; the implementation is in Java.