Reduction of false positives in structure-based virtual screening when receptor plasticity is considered

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Structure-based virtual screening often yields false positives due to receptor plasticity.
• Influenza A nucleoprotein's oligomerization is crucial for RNA binding and presents a flexible target.

Purpose of the Study:

• To develop and validate a strategy for reducing false positives in virtual screening by accounting for receptor plasticity.
• To identify true drug candidates targeting the influenza A nucleoprotein.

Main Methods:

• Utilized binding energy landscape theory and molecular dynamics simulations to explore influenza A nucleoprotein flexibility.
• Performed molecular docking using multiple receptor conformations (crystal and simulated) against the Otava PrimScreen1 library.
• Employed GOLD software to target the tail-loop binding pocket and RNA binding site.

Main Results:

• A selection strategy based on the intersection of top-ranked ligands across all receptor models was implemented.
• This method effectively distinguished between high-affinity and low-affinity control molecules.
• Successfully reduced false positives when considering receptor plasticity.

Conclusions:

• The proposed method offers an applicable approach for enhancing the accuracy of virtual screening.
• This strategy improves the selection of true ligands from large molecule libraries for drug development.