Topological Learning Approach to Characterizing Biological Membranes

Area of Science:

• Computational biology
• Biophysics
• Materials science

Background:

• Biological membranes are crucial for cellular functions, with lipid behavior influenced by temperature and molecular interactions.
• Understanding lipid organization and phase behavior is key to deciphering membrane dynamics and function.

Approach:

• Developed a persistent homology-based method to quantify lipid tail organization and structural features from static coordinates.
• Utilized algebraic topology and machine learning, specifically an attention-based neural network, trained on molecular dynamics simulations of DPPC membranes.
• Generated topological fingerprints using sphere filtrations and simplicial complexes to capture enduring structural features.

Key Points:

• The method provides local and contextual information on lipid tail organization.
• Successfully infers temperature-dependent structural information and assigns effective temperatures to membrane regions.
• Demonstrates the ability to capture local structural effects of lipids interacting with sterols and proteins.

Conclusions:

• This topological learning approach, implemented in the MembTDA tool, accurately predicts lipid effective temperatures from static coordinates across various spatial resolutions.
• Offers a novel way to analyze complex lipid structures in biological membranes computationally.
• Provides a valuable tool for researchers studying membrane biophysics and cellular signaling.