Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes

作者：Patryk A. Wesołowski, Bojun Yang, Anthony J. Davolio, Esmae J. Woods, Philipp Pracht, Krzysztof K. Bojarski, Krzysztof Wierbiłowicz, M. C. Payne, David J. Wales · 发表于：Journal of Chemical Theory and Computation · 年份：2025 · DOI：10.1021/acs.jctc.4c01623 · 被引用次数：4 · 研究领域：Machine Learning in Materials Science、Computational Drug Discovery Methods、Crystallization and Solubility Studies

This study investigates the energy landscapes of amyloid monomers, which are crucial for understanding protein misfolding mechanisms in Alzheimer's disease. While proteins possess inherent thermodynamic stability, environmental factors can induce deviations from native folding pathways, leading to misfolding and aggregation, phenomena closely linked to solubility. Using the UNOPTIM program, which integrates the UNRES potential into the Cambridge energy landscape framework, we conducted single-ended transition state searches and employed discrete path sampling to compute kinetic transition networks starting from PDB structures. These kinetic transition networks consist of local energy minima and the transition states that connect them, which quantify the energy landscapes of the amyloid monomers. We defined clusters within each landscape using energy thresholds and selected their lowest-energy structures for the structural analysis. Applying graph convolutional networks, we identified solubility trends and correlated them with structural features. Our findings identify specific minima with low solubility, characteristic of aggregation-prone states, highlighting the key residues that drive reduced solubility. Notably, the exposure of the hydrophobic residue Phe19 to the solvent triggers a structural collapse by disrupting the neighboring helix. Additionally, we investigated selected minima to determine the first passage times between states, thereby elucidating the kinetics of ...