Protein Language Models Identify Early Folding Residues as Critical Hotspots for Pathogenic Variants and Cancer...

Identifying the specific residues that initiate protein folding is critical for understanding protein stability and the mechanisms of misfolding-related diseases. However, the systematic, proteome-wide identification of these early folding residues (EFRs) has been hampered by both experimental and computational limitations. Here, we present LMEFold, a deep learning framework using protein language models (PLMs) to accurately predict EFRs from amino acid sequence alone. LMEFold outperforms existing sequence-based predictors on the Start2Fold benchmark (ROC AUC = 0.831), and its predicted scores show consistent associations with independent HDX-NMR protection-factor measurements, providing complementary biophysical evidence. Applying LMEFold to annotate over 6.6 million variants across diverse genomic datasets, we find that pathogenic mutations curated in ClinVar are significantly enriched within EFRs (~17%) compared to millions of population variants from large-scale cohorts including the UKB and gnomAD databases (~10%, P < 0.001). Critically, in the MSK-MET pan-cancer clinical cohort, EFR mutations are associated with severe functional disruption and significantly reduced overall patient survival. Structural analysis of hotspot cancer mutations, such as TP53 p.Y163C, suggests a mechanism whereby these variants destabilize protein structure by disrupting the early folding core. Our work establishes EFRs as critical sites of pathogenic vulnerability and provides a powerful framework for the rapid identification of folding-centric prognostic markers using sequence only.