GAPE: an improved genetic algorithm for pharmacophore elucidation.

Area of Science:

• Computational chemistry
• Structural bioinformatics
• Drug discovery

Background:

• Predicting drug-protein binding modes is crucial for rational drug design, especially when protein structures are unknown.
• Accurate pharmacophore elucidation is key to understanding molecular interactions and designing effective therapeutics.

Purpose of the Study:

• To introduce and evaluate GAPE (genetic algorithm for pharmacophore elucidation), an automated program for predicting binding modes.
• To assess GAPE's performance in elucidating pharmacophores from 2D structures without protein information.

Main Methods:

• GAPE utilizes a genetic algorithm to explore conformational space and align multiple compounds.
• The software was tested on 13 systems using ligands from the Protein Data Bank (PDB).
• Performance was evaluated using root-mean-square deviation (rmsd) criteria against crystallographic data.

Main Results:

• GAPE successfully approximated crystallographically observed binding modes in 8 out of 13 test systems.
• In successful predictions, at least 50% of input structures were within 2 Å rmsd of crystal coordinates.
• A notable success involved predicting the binding mode for 11 out of 12 ligands targeting P38 with 1.8 Å rmsd.

Conclusions:

• GAPE demonstrates significant potential for predicting drug-protein binding modes in the absence of structural data.
• The algorithm offers a valuable tool for computational drug design and pharmacophore elucidation.
• GAPE shows favorable comparisons to existing methods like GASP and Galahad.