MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.

Area of Science:

• Computational chemistry
• Biophysics
• Materials science

Background:

• Understanding lipid membrane phase behavior is crucial for various biological and technological applications.
• Accurate analysis of lipid molecular states and their spatial organization is essential for characterizing membrane domains.
• Existing methods may lack the versatility to handle diverse simulation data and complex phase behaviors.

Purpose of the Study:

• To introduce Machine Learning-assisted Lipid Phase Analysis (MLLPA), a new Python module for analyzing lipid membrane phase domains.
• To provide a versatile tool capable of processing various simulation formats and molecular models.
• To enable detailed analysis of local molecular environments and multi-phase coexistence.

Main Methods:

• Utilizes machine learning algorithms to classify lipid molecular states within simulation trajectories.
• Employs Voronoi tessellations to decompose the simulation box and analyze local molecular environments.
• Supports multiple input file formats (GROMACS, LAMMPS) and molecular representations (all-atom, coarse-grain).

Main Results:

• MLLPA successfully labels individual lipid molecules based on their phase state.
• The module can analyze diverse membrane geometries, including bilayers and vesicles.
• It facilitates the analysis of multiple coexisting phases, enhancing the study of complex membrane systems.

Conclusions:

• MLLPA offers a powerful and flexible approach for analyzing lipid membrane phase behavior.
• The tool enhances the understanding of molecular organization and phase separation in lipid membranes.
• It is applicable to a wide range of molecular simulations and membrane systems.