Lipid Nanoparticle Database towards structure-function modeling and data-driven design for nucleic acid delivery

Area of Science:

• Biotechnology
• Materials Science
• Computational Chemistry

Background:

• Lipid nanoparticles (LNPs) are crucial nonviral nucleic acid delivery systems.
• Current LNP data is fragmented and lacks standardization, hindering systematic analysis.
• A centralized repository, akin to the Protein Data Bank, is needed for LNPs.

Purpose of the Study:

• To develop an integrated database and web tool for consolidating LNP structural and functional data.
• To standardize LNP featurization and enable molecular dynamics simulations.
• To facilitate data-driven design and improve LNP delivery performance prediction.

Main Methods:

• Developed the Lipid Nanoparticle Database (LNPDB) consolidating data for 19,528 LNPs.
• Standardized LNP featurization by encoding lipid composition, methods, and results.
• Generated CHARMM force field files for lipids to support molecular dynamics simulations.

Main Results:

• LNPDB provides a unified platform for LNP data analysis.
• Applications demonstrated improved deep learning models for predicting LNP delivery.
• Simulations identified bilayer stability and packing parameters correlating with delivery performance.

Conclusions:

• LNPDB offers a digital framework for LNP modeling and data-driven design.
• Standardized data and simulation capabilities will accelerate LNP development.
• The database supports continued contributions for future growth and discovery.