Zenodo checkpoint

Author: CharlyEmpereurmot

Manual.pdf

@@ -1,12 +1,11 @@
-# SwarmCG (modifed version for CG lipids FF)
+# SwarmCG (modified version for CG lipids FF)
 
 ### Contents
 
 - [Overview](#overview)
 - [System Requirements](#system-requirements)
 - [Installation Guide](#installation-guide)
 - [Demo](#demo)
-- [Results & Reproduction](#results-&-reproduction)
 - [License](./LICENSE)
 - [Issues](https://github.com/GMPavanLab/SwarmCGM/issues)
 - [Citation](#citation)
@@ -88,7 +87,7 @@ For a quick demonstration of the software, one can execute the following command
 python3 optimize_lipids.py -cfg minimalistic_demo.yaml
 ```
 
-This minimalistic example should execute within 18-24 hours on the "testing purpose" hardware described, and will require 2 calculation slots of 4 cores each (no GPU).
+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.
 
@@ -103,38 +102,10 @@ These values can be even further decreased for the purpose of testing the consis
 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](https://yaml.org/) config files to simplify this process and help keeping track of the hyper-parameters used for an optimization run. 
 
-Please refer to [the manual](https://github.com/GMPavanLab/SwarmCGM/tree/main/Manual.pdf) for a breakdown of each parameter and step-by-step guidance on how to parametrize an optimization procedure.
-Additional example config files are provided below and can also be related to the content and explanations provided in the paper.
-
-# Results & Reproduction
-
-All the optimized CG models produced in the paper, and their associated FFs, can be found in folder [results](https://github.com/GMPavanLab/SwarmCGM/tree/main/results/).
-The config files cited in the 3 next subsections are available in the 3 corresponding subdirectories.
-
-### Example 1: Optimisation of Martini-based CG models of PC lipids in explicit solvent
-
-For this demonstration, we use reference data from 7 different phosphatidylcholine (PC) lipids and create optimized CG models of high resolution, in the framework of Martini and in explicit solvent.
-
-```
-python3 optimize_lipids.py -cfg example1.yaml
-```
-
-### Example 2: Optimisation of Martini-based CG models of lipids in implicit solvent
-
-For this demonstration, we use reference data from 4 different phosphatidylcholine (PC) lipids and create optimized CG models of high resolution, in the framework of Martini and in implicit solvent.
-
-```
-python3 optimize_lipids.py -cfg example2.yaml
-```
-
-### Example 3: Conception of custom low-resolution CG lipid models in implicit solvent
-
-For this demonstration, we use reference data from 4 different phosphatidylcholine (PC) lipids and create optimized CG models of low resolution, ab-initio and in implicit solvent.
-
-```
-python3 optimize_lipids.py -cfg example3.yaml
-```
+Please refer to [the demo YAML config file](https://github.com/GMPavanLab/SwarmCGM/tree/main/minimalistic_demo.yaml) 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.
 
 # Citation
 
-The preprint for SwarmCG can be found [on ArXiv](arxiv.org/abs/2107.01012).
+The testing of this modified version of SwarmCG, for building lipid models at different resolutions, is [published in JCP](https://aip.scitation.org/doi/abs/10.1063/5.0079044).
+The preprint version is available [on ArXiv](arxiv.org/abs/2107.01012).

README.md

@@ -0,0 +1,40 @@
+[moleculetype]
+; molname      nrexcl
+  DLPC         1
+
+
+[atoms]
+; id  type  resnr   residu  atom  cgnr  charge
+
+   1   K0    1       DLPC    K0    1     1.0
+   2   K1    1       DLPC    K1    2     -1.0
+
+   3   MB    1       DLPC    MBA   3     0
+   4   SB    1       DLPC    SBA   4     0
+
+   5   MB    1       DLPC    MBB   5     0
+   6   SB    1       DLPC    SBB   6     0
+
+
+[bonds]
+;  i    j   funct   length  force.c.
+
+   1    2       1        0        0     ; B1
+
+   2    3       1        0        0     ; B2
+   3    4       1        0        0     ; B8
+
+   2    5       1        0        0     ; B3
+   5    6       1        0        0     ; B8
+
+
+[angles]
+;  i    j    k   funct   length  force.c.
+
+   1    2    3       2        0        0     ; A1
+   1    2    5       2        0        0     ; A4
+;   3    2    5       2        0        0     ; A5
+
+   2    3    4       2        0        0     ; A14
+
+   2    5    6       2        0        0     ; A15

REF_MAPPINGS_LIPIDS/CG_DLPC_8BEADS_DRY.itp

@@ -0,0 +1,46 @@
+[moleculetype]
+; molname      nrexcl
+  DOPC         1
+
+
+[atoms]
+; id  type  resnr   residu  atom  cgnr  charge
+
+   1   K0    1       DOPC    K0    1     1.0
+   2   K1    1       DOPC    K1    2     -1.0
+
+   3   MB    1       DOPC    MBA   3     0
+   4   UB    1       DOPC    UBA   4     0
+   5   SB    1       DOPC    SBA   5     0
+
+   6   MB    1       DOPC    MBB   6     0
+   7   UB    1       DOPC    UBB   7     0
+   8   SB    1       DOPC    SBB   8     0
+
+
+[bonds]
+;  i    j   funct   length  force.c.
+
+   1    2       1        0        0     ; B1
+
+   2    3       1        0        0     ; B2
+   3    4       1        0        0     ; B4
+   4    5       1        0        0     ; B5
+
+   2    6       1        0        0     ; B3
+   6    7       1        0        0     ; B4
+   7    8       1        0        0     ; B5
+
+
+[angles]
+;  i    j    k   funct   length  force.c.
+
+   1    2    3       2        0        0     ; A1
+   1    2    6       2        0        0     ; A4
+;   3    2    6       2        0        0     ; A5
+
+   2    3    4       2        0        0     ; A8
+   3    4    5       2        0        0     ; A9
+
+   2    6    7       2        0        0     ; A12
+   6    7    8       2        0        0     ; A9

REF_MAPPINGS_LIPIDS/CG_DLPC_MARTINI3REMAP_WET.itp

@@ -0,0 +1,46 @@
+[moleculetype]
+; molname      nrexcl
+  DPPC         1
+
+
+[atoms]
+; id  type  resnr   residu  atom  cgnr  charge
+
+   1   K0    1       DPPC    K0    1     1.0
+   2   K1    1       DPPC    K1    2     -1.0
+
+   3   MS    1       DPPC    MSA   3     0
+   4   SB    1       DPPC    SBA   4     0
+   5   SB    1       DPPC    SBA   5     0
+
+   6   MS    1       DPPC    MSB   6     0
+   7   SB    1       DPPC    SBB   7     0
+   8   SB    1       DPPC    SBB   8     0
+
+
+[bonds]
+;  i    j   funct   length  force.c.
+
+   1    2       1        0        0     ; B1
+
+   2    3       1        0        0     ; B11
+   3    4       1        0        0     ; B6
+   4    5       1        0        0     ; B7
+
+   2    6       1        0        0     ; B10
+   6    7       1        0        0     ; B6
+   7    8       1        0        0     ; B7
+
+
+[angles]
+;  i    j    k   funct   length  force.c.
+
+   1    2    3       2        0        0     ; A6
+   1    2    6       2        0        0     ; A2
+;   3    2    6       2        0        0     ; A7
+
+   2    3    4       2        0        0     ; A13
+   3    4    5       2        0        0     ; A11
+
+   2    6    7       2        0        0     ; A10
+   6    7    8       2        0        0     ; A11