Automatic data-driven approaches are increasingly used to develop accurate molecular models. But the parameters of such automatically-optimized models are typically untransferable. Using a multi-reference approach in combination with an automatic optimization engine (SwarmCG), here we show that it is possible to optimize coarse-grained (CG) lipid models that are also transferable, thereby generating optimized lipid force fields.
The parameters of the CG lipid models are iteratively and simultaneously optimized against experimental data (top-down reference) and higher-resolution simulations (bottom-up reference). Including different types of lipid bilayers in the training set guarantees the transferability of the optimized force field parameters. Tested against state-of-the-art CG lipid force fields, we demonstrate that SwarmCG can systematically improve their parameters, enhancing the agreement with the experiments even for lipid types not included in the training set. The approach is general and can be used to improve existing CG lipid force fields, as well as to develop new custom ones.
This code was recently applied as part of efforts for the further enhancement of lipid models in Martini 3. Proof-of-concept and testing of different CG resolutions for building alternative Martini lipid models is soon to be published in JCIM and a preprint version is available on ChemRxiv. Resulting models obtained for phosphatidylcholine (PC) lipids are available here.
The code provided herein is designed for running on High Performance Computing (HPC) resources, but can just as well be executed on a classic desktop computer for testing purposes, or even for obtaining optimized CG lipid models given enough execution time. Due to the embarrassingly parallel nature of the tasks performed by SwarmCG, best performances are achieved on HPC resources, in terms of required wall-clock time for obtaining well-optimized CG models.
The execution wall-clock times are directly dependent on:
- the number of different lipids included in the training set
- the number of temperatures at which each of these lipids will be simulated
- the number of parameters to be optimized in the FF, according to user requirements
- the simulation times used in MD equilibration and production runs used for FF evaluation
- the number of MD simulations that can run in parallel on the computational infrastructure in use
- the wall-clock time required to perform each MD simulation using a given system
Tested operating systems:
- Ubuntu 18.04
- SUSE Linux Enterprise Server 15 SP1
For testing purposes, the following specs should be sufficient:
- RAM: 4+ GB
- CPU: 8+ cores, 2.0+ GHz/core
- GPU: optional
For optimal performance, SwarmCG should be executed on HPC resources. This enables calibrating accurate CG lipid FFs via the usage of many different lipid bilayers in the training set, which allows to fully take advantage of the transferability constraint imposed to the parameters of the FF (i.e. the FF parameters are tested at each iteration via multiple CG simulations of lipid bilayers).
Supported HPC resource managers:
- SLURM (distributes 1 simulation per node, within the user-defined number of nodes made available)
Standalone files can be directly copied as provided here.
Users must install the following python packages prior to executing the code:
pip/pip3 install numpy scipy matplotlib MDAnalysis pyemd fst-pso pyyaml
If you are having troubles installing or using this software, please drop us an Issue.
Tested python & package versions:
- python: 3.6.12, 3.8.5
- numpy: 1.1.16, 1.19.2
- scipy: 1.4.1, 1.5.2
- matplotlib: 3.3.2
- MDAnalysis: 1.0.0, 1.0.1
- pyemd: 0.5.1
- fst-pso: 1.7.15
- pyyaml: 5.4.1
For a quick demonstration of the software, one can execute the following command:
python3 optimize_lipids.py -cfg minimalistic_demo.yaml
This minimalistic demo should execute within 18-24 hours on the "testing purpose" hardware described, and will require 2 calculation slots of 4 cores each (no GPU). It will be possible to see that the FF score decreases during optimization, and that the 2 different lipid bilayers have decreasing % deviation on their area per lipid (APL) and Dʜʜ thickness with respect to experimental data. The OT-B and OT-NB metrics will also decrease, indicating that the FF parameters reproduce the spatial features described in the (mapped) AA reference trajectories.
However, this will not result in realistically optimal lipid models, due to:
- the small size of the training set (limited transferability constraint: only 2 different lipids)
- the short MD simulation times employed (limited sampling: only 3 ns of equilibration and 20 ns of production)
- the low number of particles used in the swarm (limited exploration of the FF parameters: only 3 particles)
which are necessary limits to be introduced here for quick (laptop/desktop) demonstration purposes. These values can be even further decreased for the purpose of testing the consistency of your own input configuration files for a specific optimization run.
As the input of SwarmCG requires a preliminary CG mapping choice (i.e. defining the positions and types of the CG beads, bonds and angles used for building the molecular models which parameters will be optimized), several parameters allow to define precisely which parameters should be optimized, or which other ones stay at given fixed values, according to your requirements. To this end, SwarmCG makes use of YAML config files to simplify this process and help keeping track of the hyper-parameters used for an optimization run.
Please refer to the demo YAML config file for a breakdown of each parameter and step-by-step guidance on how to parametrize an optimization procedure. These parameters can also be related to the content and explanations provided in the paper.
The testing of this modified version of SwarmCG, for building lipid models at different resolutions, is published in JCP and a preprint version is available on ArXiv.
Further experiments in the context of the refinement of the Martini 3 lipid models, are soon to be published in JCIM and a preprint version is available on ChemRxiv.