Machine-Learning-GuidedDesign of Antifreezing Peptides

Antifreeze peptides (AFPTs) offer a potentially nontoxic, sequence-programmable alternative to conventional cryoprotectants for preserving biological materials, yet poorly defined sequence–activity relationships continue to limit rational design. Natural AFPTs are often weak, scarce, or context-dependent, and existing design strategies rely on incremental motif tuning with low hit rates and limited interpretability. Here, we present an unsupervised machine-learning framework that leverages hybrid high-dimensional peptide representations to discover high-performance AFPT families without requiring 3D structures or large labeled data sets. We curated the largest annotated AFPT benchmark to date (n = 719) and embedded sequences in a feature space combining physicochemical descriptors with protein language model (PLM) embeddings. Unsupervised clustering resolved distinct active families within the sequence landscape, quantitatively validated by a subset of 107 peptides with measured single-crystal ice-growth rates. Mechanistic interrogation uncovered a dual-signal architecture not previously codified at the peptide level: (i) a regularly spaced polar ice-binding face encoded by primary-sequence motifs, coupled with (ii) a rigid, glycine-depleted scaffold captured only by latent PLM features. A logistic regression classifier trained on the minimal 10-feature set achieved near-perfect separability of active versus inactive families (AUC = 0.98), confirming the generality of the dual-signal rule. Guided by this interpretable blueprint, we designed 14 de novo peptides─10 dual-signal positive designs and 4 negative controls─and validated them alongside 3 literature-reported benchmarks through multiple orthogonal assays. As predicted, negative controls showed minimal activity across all metrics, whereas dual-signal designs exhibited strong ice recrystallization inhibition (IRI activity up to ∼55%), substantial temperature (down to −3.45 °C), and high red-blood-cell post-thaw recovery (85–92%). This work establishes a generalizable, presynthesis prioritization framework for antifreeze peptide engineering and demonstrates how unsupervised hybrid-feature learning can reveal actionable biophysical design rules from sequence data alone.