Identification of a Conserved Aggregation-Prone Intermediate State in the Folding Pathways of Spc-SH3 Amyloidogenic Variants

A Simulated Intermediate State for Folding and Aggregation Provides Insights into ΔN6 β2-Microglobulin Amyloidogenic Behavior

“…In recent years the identification and structural characterization of intermediate states for folding and aggregation [33], [69] has greatly contributed to a better understanding of the relation between the folding and aggregation landscapes [70]. The identification of these states in association with proteins of medical interest is of paramount importance.…”

“…In doing so, we use a three-stage computational protocol based on an array of tools as detailed in the Methods section. In the first stage, following our previous studies [33], [34], replica-exchange discrete molecular dynamics (RE-DMD) simulations of a full atomistic Gō model [35] are used to study the folding transition and to identify intermediate states in the folding pathway of ΔN6. The adopted level of structural resolution encompasses the effect on the folding mechanism of detailed atomic native contacts and fully takes into account side-chain packing, a fundamental ingredient of the folding process.…”

“…Leaving aside its possible effect on large-scale conformational dynamics, we can afford to accurately capture the effect of pH by employing constant pH molecular dynamics (CpHMD) with explicit titration [37], [38], [40]–[43]. In doing so one also obtains conformations representative of the intermediate state with the most accurate representation of side-chain and backbone geometries, which is a requirement of the Monte Carlo ensemble docking (MC-ED) [33] protocol whose predictions depend critically on the structural accuracy of the analysed structures. The MC-ED is a low-resolution protocol that highlights the role of shape complementarity, a major driver of protein aggregation [44], [45].…”

Sketching protein aggregation with a physics-based toy model

“…Descriptions with many beads per amino acid have shown a remarkable success in the simulation of fibril nucleation and growth, 14,15 or in very recent cases also in the early stages of aggregation. 16 Models which use in their representations the heavy atoms in the protein backbone and one single bead for the side chain have been also successful in the study of peptide aggregation, 17 even trying to consider the competence between ordered ("folded") and disordered ("aggregated") situations. 18 Simpler, one-bead models (either lattice or offlattice) have also been used, providing some insight on some particular aspects of protein aggregation, but mainly focused in peptide simulations 19,20 due to the inherent drawbacks of such simple models.…”

Constrained versus unconstrained folding free-energy landscapes

“…Following the MFP, many studies have shown a strong correlation between the topology of the native structure and the folding dynamics. In particular, structure-based potentials (also known as Go-type models [11][12][13]) have been used to shed light on the underling mechanisms of folding and on the thermodynamic of this process [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The degree of success of the Gotype models strongly depends on the level of frustration present in the real folding free-energy landscape and also on the accuracy of the target structure used to build the interaction potentials [26].…”