TY - JOUR
T1 - Bartender
T2 - Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics
AU - Pereira, Gilberto P
AU - Alessandri, Riccardo
AU - Domínguez, Moisés
AU - Araya-Osorio, Rocío
AU - Grünewald, Linus
AU - Borges-Araújo, Luís
AU - Wu, Sangwook
AU - Marrink, Siewert J
AU - Souza, Paulo C T
AU - Mera-Adasme, Raul
PY - 2024/7/9
Y1 - 2024/7/9
N2 - Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.
AB - Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.
U2 - 10.1021/acs.jctc.4c00275
DO - 10.1021/acs.jctc.4c00275
M3 - Article
C2 - 38924075
SN - 1549-9618
VL - 20
SP - 5763
EP - 5773
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 13
ER -