Literature DB >> 35737839

Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution.

Emanuele Scalone1, Luca Broggini1,2, Cristina Visentin1,2, Davide Erba1, Fran Bačić Toplek1, Kaliroi Peqini3, Sara Pellegrino3, Stefano Ricagno1,2, Cristina Paissoni1, Carlo Camilloni1.   

Abstract

Protein aggregation into amyloid fibrils is the archetype of aberrant biomolecular self-assembly processes, with more than 50 associated diseases that are mostly uncurable. Understanding aggregation mechanisms is thus of fundamental importance and goes in parallel with the structural characterization of the transient oligomers formed during the process. Oligomers have been proven elusive to high-resolution structural techniques, while the large sizes and long time scales, typical of aggregation processes, have limited the use of computational methods to date. To surmount these limitations, we here present multi-eGO, an atomistic, hybrid structure-based model which, leveraging the knowledge of monomers conformational dynamics and of fibril structures, efficiently captures the essential structural and kinetics aspects of protein aggregation. Multi-eGO molecular dynamics simulations can describe the aggregation kinetics of thousands of monomers. The concentration dependence of the simulated kinetics, as well as the structural features of the resulting fibrils, are in qualitative agreement with in vitro experiments carried out on an amyloidogenic peptide from Transthyretin, a protein responsible for one of the most common cardiac amyloidoses. Multi-eGO simulations allow the formation of primary nuclei in a sea of transient lower-order oligomers to be observed over time and at atomic resolution, following their growth and the subsequent secondary nucleation events, until the maturation of multiple fibrils is achieved. Multi-eGO, combined with the many experimental techniques deployed to study protein aggregation, can provide the structural basis needed to advance the design of molecules targeting amyloidogenic diseases.

Entities:  

Keywords:  aggregation kinetics; amyloids; molecular dynamics; protein aggregation; structure-based models

Mesh:

Substances:

Year:  2022        PMID: 35737839      PMCID: PMC9245614          DOI: 10.1073/pnas.2203181119

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   12.779


  87 in total

1.  How fast-folding proteins fold.

Authors:  Kresten Lindorff-Larsen; Stefano Piana; Ron O Dror; David E Shaw
Journal:  Science       Date:  2011-10-28       Impact factor: 47.728

2.  Definition and testing of the GROMOS force-field versions 54A7 and 54B7.

Authors:  Nathan Schmid; Andreas P Eichenberger; Alexandra Choutko; Sereina Riniker; Moritz Winger; Alan E Mark; Wilfred F van Gunsteren
Journal:  Eur Biophys J       Date:  2011-04-30       Impact factor: 1.733

3.  Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model.

Authors:  Michael Knott; Robert B Best
Journal:  J Chem Phys       Date:  2014-05-07       Impact factor: 3.488

4.  Pathways of Amyloid-β Aggregation Depend on Oligomer Shape.

Authors:  Bogdan Barz; Qinghua Liao; Birgit Strodel
Journal:  J Am Chem Soc       Date:  2017-12-28       Impact factor: 15.419

5.  Pathogenic mutations in the hydrophobic core of the human prion protein can promote structural instability and misfolding.

Authors:  Marc W van der Kamp; Valerie Daggett
Journal:  J Mol Biol       Date:  2010-10-07       Impact factor: 5.469

6.  Differences in nucleation behavior underlie the contrasting aggregation kinetics of the Aβ40 and Aβ42 peptides.

Authors:  Georg Meisl; Xiaoting Yang; Erik Hellstrand; Birgitta Frohm; Julius B Kirkegaard; Samuel I A Cohen; Christopher M Dobson; Sara Linse; Tuomas P J Knowles
Journal:  Proc Natl Acad Sci U S A       Date:  2014-06-17       Impact factor: 11.205

7.  Lymphotactin: how a protein can adopt two folds.

Authors:  Carlo Camilloni; Ludovico Sutto
Journal:  J Chem Phys       Date:  2009-12-28       Impact factor: 3.488

8.  Developing a molecular dynamics force field for both folded and disordered protein states.

Authors:  Paul Robustelli; Stefano Piana; David E Shaw
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-07       Impact factor: 11.205

9.  Physical determinants of the self-replication of protein fibrils.

Authors:  Alexander K Buell; Georg Meisl; Anđela Šarić; Thomas C T Michaels; Christopher M Dobson; Sara Linse; Tuomas P J Knowles; Daan Frenkel
Journal:  Nat Phys       Date:  2016-07-18       Impact factor: 20.034

10.  Cryo-EM reveals structural breaks in a patient-derived amyloid fibril from systemic AL amyloidosis.

Authors:  Lynn Radamaker; Julian Baur; Stefanie Huhn; Christian Haupt; Ute Hegenbart; Stefan Schönland; Akanksha Bansal; Matthias Schmidt; Marcus Fändrich
Journal:  Nat Commun       Date:  2021-02-08       Impact factor: 14.919

View more
  2 in total

1.  Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution.

Authors:  Emanuele Scalone; Luca Broggini; Cristina Visentin; Davide Erba; Fran Bačić Toplek; Kaliroi Peqini; Sara Pellegrino; Stefano Ricagno; Cristina Paissoni; Carlo Camilloni
Journal:  Proc Natl Acad Sci U S A       Date:  2022-06-23       Impact factor: 12.779

2.  Exploring the misfolding and self-assembly mechanism of TTR (105-115) peptides by all-atom molecular dynamics simulation.

Authors:  Yuqi Zhang; Yanyan Zhu; Haiyan Yue; Qingjie Zhao; Huiyu Li
Journal:  Front Mol Biosci       Date:  2022-08-31
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.