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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode

Gautier Moroy^1,2, Olivier Sperandio^1,2, Shakti Rielland^1,2

¹Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico, INSERM UMR-S 973, Paris, France.

Future Medicinal Chemistry

|November 25, 2015

Summary

New computational methods efficiently generate receptor conformational ensembles (RCEs) for drug discovery. These fast protocols, combining molecular dynamics and normal mode analysis, improve virtual screening by accounting for protein flexibility.

Keywords:

CDK2 DHFR molecular dynamics simulations normal mode analysis protein conformational ensemble virtual screening

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Area of Science:

Computational chemistry
Structural biology
Pharmacology

Background:

Receptor conformational ensembles (RCEs) are crucial for accurate ligand docking and virtual screening.
Traditional methods for generating RCEs can be computationally intensive.
Understanding protein flexibility is key to effective drug design.

Purpose of the Study:

To develop and validate novel, fast computational protocols for generating RCEs.
To integrate conformational pocket classifications into RCE generation.
To enhance the efficiency of virtual screening through improved RCEs.

Main Methods:

Fast molecular dynamics (MD)-based simulations.
Fast normal mode analysis (NMA)-based simulations.
Conformational pocket classification integrated with MD and NMA.

Main Results:

Protocols were successfully applied to dihydrofolate reductase (local flexibility) and CDK2 (collective movements).
Generated RCEs effectively distinguished known ligands from decoys for both targets.
Demonstrated the efficiency of the new protocols in capturing relevant protein dynamics.

Conclusions:

The developed protocols offer an efficient approach to generating RCEs.
The choice of simulation protocol should consider the specific type of protein flexibility.
These methods can advance ligand docking and virtual screening in drug discovery.