An improved relaxed complex scheme for receptor flexibility in computer-aided drug design

TLDR

Receptor flexibility is a major challenge in computational drug design, and the relaxed complex scheme (RCS) addresses this by integrating docking with molecular dynamics to capture both receptor and ligand dynamics. The authors review and extend the RCS for ligand binding to kinetoplastid RNA editing ligase 1 and a cytochrome c peroxidase mutant, and propose further methodological enhancements. RCS is enhanced by adding virtual screening, detailed binding effect analysis, and a reduced receptor ensemble to boost efficiency. The predictive accuracy of RCS depends on the receptor ensemble chosen, and solvent effects remain a limitation.

Abstract

The interactions among associating (macro)molecules are dynamic, which adds to the complexity of molecular recognition. While ligand flexibility is well accounted for in computational drug design, the effective inclusion of receptor flexibility remains an important challenge. The relaxed complex scheme (RCS) is a promising computational methodology that combines the advantages of docking algorithms with dynamic structural information provided by molecular dynamics (MD) simulations, therefore explicitly accounting for the flexibility of both the receptor and the docked ligands. Here, we briefly review the RCS and discuss new extensions and improvements of this methodology in the context of ligand binding to two example targets: kinetoplastid RNA editing ligase 1 and the W191G cavity mutant of cytochrome c peroxidase. The RCS improvements include its extension to virtual screening, more rigorous characterization of local and global binding effects, and methods to improve its computational efficiency by reducing the receptor ensemble to a representative set of configurations. The choice of receptor ensemble, its influence on the predictive power of RCS, and the current limitations for an accurate treatment of the solvent contributions are also briefly discussed. Finally, we outline potential methodological improvements that we anticipate will assist future development.