Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

Jakob Schneider, Ksenia Korshunova, Francesco Musiani, Mercedes Alfonso-Prieto, Alejandro Giorgetti, Paolo Carloni
2018 Biochemical and Biophysical Research Communications - BBRC  
Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular
more » ... ained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code. Research highlights • Multiscale methods can provide insights into membrane proteins. • Structural information on human G-protein coupled receptors is very limited. • Docking becomes challenging when only low-resolution homology models are available. • Our molecular mechanics/coarse grained approach improves ligand pose predictions. Hamiltonian Adaptive Resolution Scheme CHARMM36 [87] and the PRIMO [49, 50, 88] force fields are combined to describe the MM and CG parts of the protein, respectively, and the membrane is represented with the heterogeneous dielectric generalized Born (HDGB) model [89, 90] . However, so far this method has not been extensively tested for membrane proteins.
doi:10.1016/j.bbrc.2018.01.160 pmid:29409902 fatcat:wxl25mf5yrafbcu4pjuouqh4ey