Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology

Colombo, Giorgio; Soto, Patricia; Gazit, Ehud

doi:10.1016/j.tibtech.2007.03.004

Cited by 137 publications

(119 citation statements)

References 52 publications

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“…(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and the monomers and dimers of A!40 and A!42. In particular, we focus on whether the formation of a loop in the region 21-30 is the rate-limiting step in A!…”

Section: Introductionmentioning

confidence: 99%

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Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42

Melquiond

Dong²,

Mousseau³

et al. 2008

CAR

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Self-assembly of the 40/42 amino acid A! peptide is a key player in Alzheimer's disease. A!40 is the most prevalent species, while A!42 is the most toxic. It has been suggested that the amino acids 21-30 could nucleate the folding of A! monomer and a bent in this region could be the rate-limiting step in A! fibril formation. In this study, we review our current understanding of the computer-predicted conformations of amino acids 23-28 in the monomer of A!(21-30) and the monomers A!40 and A!42. On the basis of new simulations on dimers of full-length A!, we propose that the ratelimiting step involves the formation of a multimeric !-sheet spanning the central hydrophobic core (residues 17-21).

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Section: Introductionmentioning

confidence: 99%

“…Reviews on the use of simulations to understand protein aggregation in general and the assembly of small amyloid-forming peptides A! can be found elsewhere [15,[18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42

Melquiond

Dong²,

Mousseau³

et al. 2008

CAR

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“…Moreover, the timescale of the AAFF molecular dynamics (MD) simulations is typically on the 10Õs Ð 100Õs of nanoseconds, which is much shorter than the experimental timescale for self-assembly. [25] Therefore, in order to provide insights into the selfassembled structures a number of simplifying approximations are typically employed.…”

Section: Atomistic Molecular Mechanicsmentioning

confidence: 99%

Computational Approaches to Understanding the Self‐assembly of Peptide‐based Nanostructures

Tuttle

2015

Israel Journal of Chemistry

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This version is available at https://strathprints.strath.ac.uk/54165/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Computational Approaches to Understanding the SelfAssembly of Peptide-Based NanostructuresTell Tuttle* [a] Abstract: The interest in the self-assembly of peptide-based systems has grown significantly over the past 10 Ð 15 years as more and more applications are shown to benefit from the useful properties of the amino acid based monomers. With the desire to apply the principals of self-assembly to systems within new application areas there has been an increasing emphasis in understanding the governing forces involved in the self-assembly process and using this understanding to predict the behavior of, and design, new materials.To this end, computational approaches have played an increasingly important role over the past decade in helping to decode how small changes in the primary structure can lead to significantly different nanostructures with new function. In this review a brief survey of the different computational approaches employed in this quest for understanding are provided, along with representative examples of the types of questions that can be answered with each of the different approaches.

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“…Thus, we can propose different peptide sequences and from their selforganization to obtain structures with different geometries (spherical, cylindrical, conical) and even nanotubes and/or nanofibers (Hirata, Fujimura et al, 2007) are obtained. Peptide nanomaterials form supramolecular structures which are interconnected by intermolecular interactions such as van der Waals forces, electrostatic, hydrophobic and hydrogen bonds, among others (Cheng, Zhu et al, 2007;Colombo, Soto et al, 2007). Due to these characteristics, crystal engineering of supramolecular architectures has rapidly expanded in recent years, mainly due to the possibility of intermolecular interactions, structural diversity and potential applications (Sanchez, Arribart et al, 2005;Cheng, Zhu et al, 2007;He, Duan et al, 2008;Aizenberg e Fratzl, 2009).…”

Section: Introductionmentioning

confidence: 99%

Biosensors Based on Biological Nanostructures

Alves¹,

Alves²,

Sousa³

et al. 2011

New Perspectives in Biosensors Technology and Applications

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Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology

Cited by 137 publications

References 52 publications

Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42

Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42

Computational Approaches to Understanding the Self‐assembly of Peptide‐based Nanostructures

Biosensors Based on Biological Nanostructures

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Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology

Cited by 137 publications

References 52 publications

Role of the Region 23-28 in A&#946; Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides A&#946;40 and A&#946;42

Role of the Region 23-28 in A&#946; Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides A&#946;40 and A&#946;42

Computational Approaches to Understanding the Self‐assembly of Peptide‐based Nanostructures

Biosensors Based on Biological Nanostructures

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Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42

Role of the Region 23-28 in Aβ Fibril Formation: Insights from Simulations of the Monomers and Dimers of Alzheimers Peptides Aβ40 and Aβ42