Structure-Based Kinase Profiling To Understand the Polypharmacological Behavior of Therapeutic Molecules

Area of Science:

• Computational chemistry
• Pharmacology
• Drug discovery

Background:

• Many drugs exert effects by interacting with multiple cellular targets (polypharmacology).
• Identifying these molecular targets is crucial for understanding drug efficacy and safety.
• Experimental methods for target identification are costly and time-consuming.

Purpose of the Study:

• To develop and validate a computational pipeline for predicting drug targets, specifically kinases.
• To assess the pipeline's performance using FDA-approved drugs and a novel compound, mahanine.
• To elucidate the polypharmacological profile of mahanine through structure-based kinase profiling.

Main Methods:

• Employed a reverse virtual screening computational pipeline with consensus scoring strategies.
• Performed structure-based kinase profiling on 12 FDA-approved drugs.
• Validated the pipeline by predicting and experimentally confirming kinase targets for mahanine.

Main Results:

• The pipeline achieved high accuracy (average AU-ROC > 0.85) in predicting kinase targets for approved drugs.
• Mahanine was identified to modulate multiple kinases, including mTOR and VEGFR2, with validated inhibitory effects (IC50 values).
• Drug-protein interaction fingerprints revealed that target selectivity is governed by kinase conformations and binding interactions.

Conclusions:

• The developed computational pipeline is effective for structure-based kinase profiling and predicting drug targets.
• This approach aids in understanding the polypharmacological actions of therapeutic molecules.
• The findings support the use of computational methods in rational drug discovery and target identification.