Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes

Area of Science:

• Biophysics
• Computational Chemistry
• Neuroscience

Background:

• Protein misfolding and aggregation are central to neurodegenerative diseases like Alzheimer's.
• Amyloid monomers' energy landscapes influence their stability and propensity to misfold.
• Solubility is a critical factor in protein aggregation.

Purpose of the Study:

• To investigate the energy landscapes of amyloid monomers.
• To identify structural features correlated with reduced solubility and aggregation.
• To elucidate the thermodynamics and kinetics of amyloid monomer behavior.

Main Methods:

• Utilized the UNOPTIM program and Cambridge energy landscape framework.
• Employed single-ended transition state searches and discrete path sampling to build kinetic transition networks.
• Applied graph convolutional networks for structural analysis and solubility trend identification.

Main Results:

• Identified specific energy minima associated with low solubility and aggregation-prone states.
• Highlighted key residues, such as Phe19, that drive reduced solubility through structural collapse.
• Quantified energy landscapes and investigated kinetics, including first passage times between states.

Conclusions:

• Specific structural features of amyloid monomers dictate their solubility and aggregation propensity.
• Understanding these energy landscapes offers insights into Alzheimer's disease pathogenesis.
• Findings may guide the development of novel therapeutic strategies targeting protein misfolding.