Topological Learning Approach to Characterizing Biological Membranes

Area of Science:

• Cellular Biology
• Biophysics
• Computational Chemistry

Background:

• Biological membranes are crucial for cellular functions, with lipid behavior influenced by temperature.
• Lipid configurations and phase transitions are complex phenomena affecting membrane properties.
• Existing methods may not fully capture local lipid tail organization and its temperature dependence.

Purpose of the Study:

• To present a novel persistent homology (PH)-based method for quantifying lipid structural features in biological membranes.
• To infer temperature-dependent structural information and local lipid organization from static molecular coordinates.
• To develop a computational tool for analyzing lipid effective temperatures in membrane environments.

Main Methods:

• Utilized persistent homology (PH) and algebraic topology to analyze lipid configurations.
• Generated molecular dynamics trajectories of dipalmitoyl-phosphatidylcholine membranes at varying temperatures.
• Trained an attention-based neural network using PH-derived persistence data to predict effective temperatures.

Main Results:

• Developed a PH-based method to quantify local and contextual lipid tail organization.
• Successfully inferred effective temperature values for membrane regions from static lipid coordinates.
• Demonstrated the method's ability to capture local structural effects of lipids interacting with sterols and proteins.

Conclusions:

• The developed topological learning approach, MembTDA, accurately predicts lipid effective temperatures from static coordinates.
• This method provides valuable local structural insights into membrane lipid organization.
• MembTDA offers a novel computational tool for biophysical and cellular biology research.