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Force constants for a new molecule
- ahq-ujn
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3 years 7 months ago #8702
by ahq-ujn
Force constants for a new molecule was created by ahq-ujn
Hi,
I am new to parametrize a new small molecule (with Martini 3.0 tutorials) and I have completed tutorial practice. I find it is very convenient for a newer to learn the parametrization with Martini 3.0 tutorial. However, I am confused where I can get/choose the force constant values of bonds, angles and dihedrals for a new small molecule, such as my aspirin, melatonin and curcumin. As I find there are various force constants assigned for bonds, dihedrals in the modified ENAP_take1.itp file of the Martini 3.0 tutorials, I wonder where are these values from.
It will be really helpful if experts could comment and guide me.
Thanks,
Hongqi Ai
I am new to parametrize a new small molecule (with Martini 3.0 tutorials) and I have completed tutorial practice. I find it is very convenient for a newer to learn the parametrization with Martini 3.0 tutorial. However, I am confused where I can get/choose the force constant values of bonds, angles and dihedrals for a new small molecule, such as my aspirin, melatonin and curcumin. As I find there are various force constants assigned for bonds, dihedrals in the modified ENAP_take1.itp file of the Martini 3.0 tutorials, I wonder where are these values from.
It will be really helpful if experts could comment and guide me.
Thanks,
Hongqi Ai
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- peterkroon
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3 years 7 months ago #8709
by peterkroon
Replied by peterkroon on topic Force constants for a new molecule
Hi Hongqi Ai,
There are several considerations:
1) Make sure the shape of the molecule at equilibrium matches the atomistic shape. This will give you bond lengths (usually directly from CoG mapping)
2) Make sure the CG molecule samples the same conformational space the as the AA molecule. For this you usually need AA reference simulations, and will give you angles, dihedrals and force constants. This is generally an iterative process where the CG parameters are refined until the distributions match the AA ones. See also e.g. pycgtools [1], and swarm-cg [2].
3) Compare macroscopic properties, such as partition free energies (and a few others). These properties usually come as result from the bead types, but can be affected by bonded interactions [3].
When parametrizing a new molecule from scratch, the order is usually: 3, 1, 2. So non-bonded parameters first, then bonds, then force constants and the other bonded parameters.
HTH
[1] J.A. Graham, J.W. Essex, S. Khalid, PyCGTOOL: Automated Generation of Coarse-Grained Molecular Dynamics Models from Atomistic Trajectories, J. Chem. Inf. Model. 57 (2017) 650–656. doi:10.1021/acs.jcim.7b00096.
[2] chemrxiv.org/articles/Swarm-CG_Automatic...ptimization/12613427
[3] R. Alessandri, P.C.T. Souza, S. Thallmair, M.N. Melo, A.H. de Vries, S.J. Marrink, Pitfalls of the Martini Model, J. Chem. Theory Comput. 15 (2019) 5448–5460. doi:10.1021/acs.jctc.9b00473.
There are several considerations:
1) Make sure the shape of the molecule at equilibrium matches the atomistic shape. This will give you bond lengths (usually directly from CoG mapping)
2) Make sure the CG molecule samples the same conformational space the as the AA molecule. For this you usually need AA reference simulations, and will give you angles, dihedrals and force constants. This is generally an iterative process where the CG parameters are refined until the distributions match the AA ones. See also e.g. pycgtools [1], and swarm-cg [2].
3) Compare macroscopic properties, such as partition free energies (and a few others). These properties usually come as result from the bead types, but can be affected by bonded interactions [3].
When parametrizing a new molecule from scratch, the order is usually: 3, 1, 2. So non-bonded parameters first, then bonds, then force constants and the other bonded parameters.
HTH
[1] J.A. Graham, J.W. Essex, S. Khalid, PyCGTOOL: Automated Generation of Coarse-Grained Molecular Dynamics Models from Atomistic Trajectories, J. Chem. Inf. Model. 57 (2017) 650–656. doi:10.1021/acs.jcim.7b00096.
[2] chemrxiv.org/articles/Swarm-CG_Automatic...ptimization/12613427
[3] R. Alessandri, P.C.T. Souza, S. Thallmair, M.N. Melo, A.H. de Vries, S.J. Marrink, Pitfalls of the Martini Model, J. Chem. Theory Comput. 15 (2019) 5448–5460. doi:10.1021/acs.jctc.9b00473.
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