Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) make up ∼30% of the human proteome and are implicated in many human diseases including cancer and neurodegenerative diseases. The aggregation of amyloid-forming IDPs into fibril structures has been linked to cytotoxicity in diseases like Parkinson’s and Alzheimer’s diseases, the two most common neurodegenerative diseases worldwide. The mechanism of aggregation is still poorly understood, and represents one of the key challenges in deciphering the progression of these irreversible conditions. Molecular dynamics (MD) simulations provide a powerful approach for understanding the structure and dynamics of IDPs. Conventional MD often struggles to adequately sample biologically relevant states of IDPs due to the wide range of states they can adopt. To address this shortcoming, a variety of enhanced sampling techniques can be used. One common approach is metadynamics, which enables reconstruction of the unbiased free energy surface after a simulation is complete. To perform metadynamics, a collective variable (CV) must be chosen. A CV is a function of atomic coordinates that can adopt a range of values for different states of a system. Choosing a CV for IDP systems can be very challenging, but recently data-driven approaches allow for automatically creating a CV from a non-linear transformation of atomic coordinates. Here, we present the results of metadynamics simulations of residues 10–35 amyloid β, the principal IDP involved in Alzheimer’s disease, with a data-driven CV. Our approach sought to drive sampling toward the fibril conformation using several characteristic interatomic distances as training data, to identify potential intermediate states in the aggregation process. By doing so, we provide a foundation for future computational investigations of aggregation in similar IDP systems.
Gilles et al. (Sun,) studied this question.