PLOS Computational BiologyBiophysicsPeer-reviewed journal article
All-Atom Simulation of the HET-s Prion Replication
L. Terruzzi, G. Spagnolli, A. Boldrini, J. R. Requena, E. Biasini, p. faccioli
Abstract
Prions are self-replicative protein particles lacking nucleic acids. Originally discovered for causing infectious neurodegenerative disorders, they have also been found to play several physiological roles in a variety of species. Functional and pathogenic prions share a common mechanism of replication, characterized by the ability of an amyloid conformer to propagate by inducing the conversion of its physiological, soluble counterpart. In this work, we focus on the propagation of the prion forming domain of HET-s, a physiological fungal prion for which high-resolution structural data are available. Since time-resolved biophysical experiments cannot yield a full reconstruction of prion replication, we resort to computational methods. To overcome the computational limitations of plain Molecular Dynamics (MD) simulations, we adopt a special type of biased dynamics called ratchet-and-pawl MD (rMD). The accuracy of this enhanced path sampling protocol strongly depends on the choice of the collective variable (CV) used to define the biasing force. Since for prion propagation a reliable reaction coordinate (RC) is not yet available, we resort to the recently developed Self-Consistent Path Sampling (SCPS). Indeed, in such an approach the CV where the biasing force is applied is not heuristically postulated but is calculated through an iterative refinement procedure. Our atomistic reconstruction of the HET-s replication shows remarkable similarities with a previously reported mechanism of mammalian PrPSc propagation obtained with a different computational protocol. Together, these results indicate that the propagation of prions generated by evolutionary distant proteins shares common features. In particular, in both these cases, prions propagate their conformation through a very similar templating mechanism.
- organism: fungi (other)
- assay: protein structure
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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