Amyloids refer to a diverse group of highly ordered proteinaceous structures conserved across all of the domains of life that not only are involved in severe proteinopathies but also serve as unique functional platforms. They are notably organized around a cross-β sheet core arranged into a dry steric zipper. Identifying regions promoting such type of assembly requires investigating, usually at the atomistic level, their structural and morphological properties. Although more amyloid structures are being solved and described by means of high-resolution experimental techniques, most amyloidogenic systems remain experimentally elusive or uncharacterized. To overcome such limitations, we propose a multilevel sequence-based computational approach taking advantage of aggregation-oriented predictors and modelers coupled with all-atom equilibrium molecular dynamics (MD) simulations. As an example of this approach, we introduced our protein of interest, the intrinsically disordered zinc finger DPF3a, to the pipeline, thus identifying a hit hexapeptide (52NCYIWM57) exhibiting remarkable pro-amyloid features. Indeed, this peptide was shown to stabilize different steric zipper topologies, spontaneously self-assemble into β-sheeted oligomers, and to act as an amyloid seed enabling the conversion and addition of β-sheet multimers for fibril elongation, which echoes the pathological repertoire of DPF3a. In comparison, simulations performed on a peptide sequence consensually predicted as nonamyloid (33AERSVR38) congruently underscored its inability to maintain a steric zipper configuration and nucleate into oligomers enriched in β character. Using only the sequence of a target protein as starting input, our methodology noteworthily proved to be reliable for detecting aggregation-prone regions, assessing their amyloidogenicity, and elucidating their fibrillation mechanisms while holding promise for the design of new anti-amyloid drugs and amyloid-inspired biomaterials.
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Julien Mignon
Tanguy Leyder
Hugo Bâlon
Journal of Chemical Information and Modeling
Centre National de la Recherche Scientifique
Université Paris Cité
University of Liège
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Mignon et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69e31ff140886becb653f176 — DOI: https://doi.org/10.1021/acs.jcim.6c00951