After two decades
of continued development of the Martini
coarse-grained
force field (CG FF), further refinment of the already rather accurate
Martini lipid models has become a demanding task that could benefit
from integrative data-driven methods. Automatic approaches are increasingly
used in the development of accurate molecular models, but they typically
make use of specifically designed interaction potentials that transfer
poorly to molecular systems or conditions different than those used
for model calibration. As a proof of concept, here, we employSwarmCG, an automatic multiobjective optimization approach
facilitating the development of lipid force fields, to refine specifically
the bonded interaction parameters in building blocks of lipid models
within the framework of the general Martini CG FF. As targets of the
optimization procedure, we employ both experimental observables (top-down
references: area per lipid and bilayer thickness) and all-atom molecular
dynamics simulations (bottom-up reference), which respectively inform
on the supra-molecular structure of the lipid bilayer systems and
on their submolecular dynamics. In our training sets, we simulate
at different temperatures in the liquid and gel phases up to 11 homogeneous
lamellar bilayers composed of phosphatidylcholine lipids spanning
various tail lengths and degrees of (un)saturation. We explore different
CG representations of the molecules and evaluate improvementsa posterioriusing additional simulation temperatures and
a portion of the phase diagram of a DOPC/DPPC mixture. Successfully
optimizing up to ∼80 model parameters within still limited
computational budgets, we show that this protocol allows the obtainment
of improved transferable Martini lipid models. In particular, the
results of this study demonstrate how a fine-tuning of the representation
and parameters of the models may improve their accuracy and how automatic
approaches, such asSwarmCG, may be very useful to
this end.