Quick & Dirty tutorial

While it is recommended to read the whole tutorials before getting started with ML-LPA, we present on this page a quick and dirty example on how ML-LPA can be used to analyse a set of simulation files.

In the following example, we will use two simulations (one at low temperature, gel, and one at high temperature, fluid) to train ML-LPA to recognise the phases of DPPC molecules. We will then use the generated models to analyse the unknown phase composition of a DPPC bilayers which also includes some DOPC and cholesterol (named CHL1) molecules; the latter having no phase transition in the studied temperature range. Finally the system will be tessellated to analyse the local environment of the molecules in the bilayer.

Codelines

Please refer to the comments in the code to see the purpose of each function and step. Sections below describes the different functions used and where to find more information on them.

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import mllpa

## STEP 1 - TRAINING

# Load the simulations files to be trained on
gel_system = mllpa.openSystem('gel.gro', 'gel.tpr', 'DPPC')
fluid_system = mllpa.openSystem('fluid.gro', 'fluid.tpr', 'DPPC')

# Train the models and save them in a variable AND in a file
models = mllpa.generateModel([gel_system, fluid_system], phases=['gel', 'fluid'], save_model=True, file_path='test_dppc.lpm')

## STEP 2 - PREDICTION

# Load the simulation file with the unknown composition in DPPC and with DOPC molecules as well
unknown_system_dppc = mllpa.openSystem('unknown.gro', 'unknown.tpr', 'DPPC')
unknown_system_dopc = mllpa.openSystem('unknown.gro', 'unknown.tpr', 'DOPC')
unknown_system_chol = mllpa.openSystem('unknown.gro', 'unknown.tpr', 'CHL1')

# Predict the phase of the lipids that have been trained on
unknown_system_dppc.getPhases(models)

# Assign a given phase to lipids that does not undergo phase transition
unknown_system_dopc.setPhases("fluid")
unknown_system_chol.setPhases("cholesterol") # We add here a label instead of a phase

## STEP 3 - TESSELLATION

# Do the tessellation and analyse the local environment - but exclude cholesterols from the ghost generation
unknown_tessellation = mllpa.doVoro([unknown_system_dppc, unknown_system_dopc, unknown_system_chol], geometry='bilayer_3d', exclude_ghost=[2], read_neighbors=True)

## STEP 4 - OUTPUT

# Save the file
mllpa.saveVoro(unknown_tessellation, file_path="unknown_tessellation.csv")

Details on the example

Training

In the training part of the codes, two functions are being used:

Prediction

In the prediction part of the codes, one function and two methods are being used:

Tessellation

In the tessellation part of the codes, two functions are being used:

Output

In the output part of the codes, one function is used: