From Homology Models to a Set of Predictive Binding Pockets-a 5-HT_1A Receptor Case Study

Area of Science:

• Pharmacology
• Computational Chemistry
• Structural Biology

Background:

• The serotonin 1A receptor (5-HT_1A) is crucial for drug design but lacks X-ray crystallographic data.
• This structural gap hinders understanding of ligand binding and the development of new therapeutics.

Purpose of the Study:

• To develop and validate a ligand-guided computational approach for modeling the 5-HT_1A receptor.
• To identify novel active compounds for the 5-HT_1A receptor using virtual screening.

Main Methods:

• Utilized the ALiBERO ligand-guided approach based on Internal Coordinate Mechanics (ICM) docking.
• Incorporated receptor flexibility using Normal Mode Analysis and Monte Carlo sampling.
• Performed retrospective and prospective virtual screening using optimized protein models.

Main Results:

• The ALiBERO method effectively discriminated active from inactive compounds.
• Investigated the impact of different protein templates and compound selection on screening accuracy.
• Identified two novel, chemically distinct active compounds in a prospective virtual screening campaign.

Conclusions:

• The ligand-guided approach provides valuable insights into 5-HT_1A receptor pharmacology despite structural data limitations.
• This method is effective for virtual screening and discovering new drug candidates for the 5-HT_1A receptor.