Discovering Naturally Occurring Antifreeze Peptides from Microbiome by Integrating Protein Language Models and Molecular Dynamics Simulation

Abstract

Antifreeze peptides (AFPs) inhibit ice crystal growth and recrystallization, and are promising components of cryoprotective formulations for cell, tissue, and food preservation, as well as anti-icing surface coatings. However, the discovery of new AFPs has been hindered by their sequence diversity and the limited scalability of experimental screening. In this study, we identify novel AFP candidates from a microbiome-derived sequence library using ensemble machine learning and molecular dynamics (MD) simulations. We developed an ensemble classifier composed of 10 adapter-tuned protein-language models and a random forest meta-learner. After training on a curated dataset of 73,766 sequences, we applied this ensemble to 56,008 amino acid sequences from an Arctic microbiome library to identify AFP candidates. Structural prediction yields a diverse range of conformations for six selected candidates, including α-helices, coils, and combinations of both. To evaluate their functional relevance, atomistic MD simulations were conducted to assess conformational stability and solvent interactions under freezing conditions. One candidate shows persistent helicity, surface amphipathicity, and an organized hydration pattern consistent with known ice-binding helices, marking it a promising functional candidate. These findings expand the known landscape of AFPs and highlight a scalable strategy for discovering functional peptides from complex biological sources.