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HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides

Nobile M. S.
•
Fontana F.
•
Manzoni L.
altro
Gelain F.
2021
  • journal article

Periodico
SCIENTIFIC REPORTS
Abstract
Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of β-sheets formation. Differently from the existing tools, HyperBeta directly manipulates data generated by means of coarse-grained molecular dynamics simulation tools (GROMACS), performed using the MARTINI force field. Coarse-grained molecular structures are visualized using HyperBeta ’s proprietary real-time high-quality 3D engine, which provides a plethora of analysis tools and statistical information, controlled by means of an intuitive event-based graphical user interface. The high-quality renderer relies on a variety of visual cues to improve the readability and interpretability of distance and depth relationships between peptides. We show that HyperBeta is able to track the β-sheets formation in coarse-grained molecular dynamics simulations, and provides a completely new and efficient mean for the investigation of the kinetics of these nano-structures. HyperBeta will therefore facilitate biotechnological and medical research where these structural elements play a crucial role, such as the development of novel high-performance biomaterials in tissue engineering, or a better comprehension of the molecular mechanisms at the basis of complex pathologies like Alzheimer’s disease.
DOI
10.1038/s41598-021-87087-0
WOS
WOS:000639562100103
Archivio
http://hdl.handle.net/11368/2994640
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85104074192
https://www.nature.com/articles/s41598-021-87087-0
Diritti
open access
license:creative commons
license:creative commons
license uri:http://creativecommons.org/licenses/by/4.0/
license uri:http://creativecommons.org/licenses/by/4.0/
FVG url
https://arts.units.it/bitstream/11368/2994640/2/s41598-021-87087-0.pdf
Soggetti
  • protein

  • self-assembly peptide...

Scopus© citazioni
0
Data di acquisizione
Jun 7, 2022
Vedi dettagli
Web of Science© citazioni
0
Data di acquisizione
Mar 22, 2024
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