TY - JOUR
T1 - Membrane Mixer
T2 - A Toolkit for Efficient Shuffling of Lipids in Heterogeneous Biological Membranes
AU - Licari, Giuseppe
AU - Dehghani-Ghahnaviyeh, Sepehr
AU - Tajkhorshid, Emad
N1 - This research is supported by National Institutes of Health grants R01-GM123455 and P41-GM104601 (to E.T.). Simulations in this study have been performed using allocations at National Science Foundation Supercomputing Centers (XSEDE grant number MCA06N060), and the Blue Waters Petascale Computing Facility of National Center for Supercomputing Applications at University of Illinois at Urbana–Champaign, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois.
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Molecular dynamics (MD) simulations of biological membranes have achieved such levels of sophistication that are commonly used to predict unresolved structures and various properties of lipids and to substantiate experimental data. While achieving sufficient sampling of lipid dynamics remains a major challenge, a commonly used method to improve lipid sampling, e.g., in terms of specific interactions with membrane-associated proteins, is to randomize the initial arrangement of lipid constituents in multiple replicas of simulations, without changing the overall lipid composition of the membrane of interest. Here, we introduce a method that can rapidly generate multiple replicas of lipid bilayers with different spatial and conformational configurations for any given lipid composition. The underlying algorithm, which allows one to shuffle lipids at any desired level, relies on the application of an external potential, here referred to as the “carving potential”, that removes clashes/entanglements before lipid positions are exchanged (shuffled), thereby minimizing the energy penalty due to abrupt lipid repositioning. The method is implemented as “Membrane Mixer Plugin (MMP) 1.0” in VMD, with a convenient graphical user interface that guides the user in setting various options and parameters. The plugin is fully automated and generates new membrane replicas more rapidly and conveniently than other analogous tools. The plugin and its capabilities introduced here can be extended to include additional features in future versions.
AB - Molecular dynamics (MD) simulations of biological membranes have achieved such levels of sophistication that are commonly used to predict unresolved structures and various properties of lipids and to substantiate experimental data. While achieving sufficient sampling of lipid dynamics remains a major challenge, a commonly used method to improve lipid sampling, e.g., in terms of specific interactions with membrane-associated proteins, is to randomize the initial arrangement of lipid constituents in multiple replicas of simulations, without changing the overall lipid composition of the membrane of interest. Here, we introduce a method that can rapidly generate multiple replicas of lipid bilayers with different spatial and conformational configurations for any given lipid composition. The underlying algorithm, which allows one to shuffle lipids at any desired level, relies on the application of an external potential, here referred to as the “carving potential”, that removes clashes/entanglements before lipid positions are exchanged (shuffled), thereby minimizing the energy penalty due to abrupt lipid repositioning. The method is implemented as “Membrane Mixer Plugin (MMP) 1.0” in VMD, with a convenient graphical user interface that guides the user in setting various options and parameters. The plugin is fully automated and generates new membrane replicas more rapidly and conveniently than other analogous tools. The plugin and its capabilities introduced here can be extended to include additional features in future versions.
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U2 - 10.1021/acs.jcim.1c01388
DO - 10.1021/acs.jcim.1c01388
M3 - Article
C2 - 35104125
AN - SCOPUS:85124313470
SN - 1549-9596
VL - 62
SP - 986
EP - 996
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 4
ER -