AlphaUnfold: Probing Potential Unfolding and Structural Fragility in AlphaFold3 Models via Short-Time High-Pressure MD

Abstract We developed AlphaUnfold, an automated pipeline that couples AF3 predictions with short-time (5 ns) high-pressure Molecular Dynamics (MD) using NAMD3. By subjecting models to baric stress, AlphaUnfold acts as a dynamic “stress-test” to identify structural fragility and potential unfolding. Testing a diverse set of proteins revealed a significant inverse correlation between average pLDDT and Root Mean Square Deviation (RMSD) after MD, indicating that lower confidence translates to rapid structural drift. Furthermore, domains with low local pLDDT consistently exhibited high Root Mean Square Fluctuation (RMSF), a behavior also observed in 200 ns simulations under standard pressure, pinpointing specific metastable areas. AlphaUnfold thus provides a viable, computationally efficient framework for assessing the biophysical robustness of AI-generated models, offering an “experimental-like” validation that ensures more reliable downstream applications in structural biology. Motivation AlphaFold3 (AF3) provides high-accuracy protein models characterized by the Predicted Local Distance Difference Test (pLDDT). However, these static predictions may harbor “not well-forged” regions lacking thermodynamic resilience. There is a critical need for rapid computational protocols to validate structural integrity beyond static confidence scores. Availability GitHub: https://github.com/pegados/pipeline_AlphaUnfold Supplementary information Supplementary data are available at http://biodados.icb.ufmg.br/alphaunfold Contact e-mail fabio, silva-jrp.miguel{at}ufmg.br Competing Interest Statement The authors have declared no competing interest.