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

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 determ...

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Autores principales:	Marco S. Nobile, Federico Fontana, Luca Manzoni, Paolo Cazzaniga, Giancarlo Mauri, Gloria A. A. Saracino, Daniela Besozzi, Fabrizio Gelain
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d5f7ef478f74696a15526357172f6b7
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spelling	oai:doaj.org-article:7d5f7ef478f74696a15526357172f6b72021-12-02T18:15:33ZHyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides10.1038/s41598-021-87087-02045-2322https://doaj.org/article/7d5f7ef478f74696a15526357172f6b72021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87087-0https://doaj.org/toc/2045-2322Abstract 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 $$\beta$$ 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 $$\beta$$ 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.Marco S. NobileFederico FontanaLuca ManzoniPaolo CazzanigaGiancarlo MauriGloria A. A. SaracinoDaniela BesozziFabrizio GelainNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marco S. Nobile Federico Fontana Luca Manzoni Paolo Cazzaniga Giancarlo Mauri Gloria A. A. Saracino Daniela Besozzi Fabrizio Gelain HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
description	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 $$\beta$$ 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 $$\beta$$ 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.
format	article
author	Marco S. Nobile Federico Fontana Luca Manzoni Paolo Cazzaniga Giancarlo Mauri Gloria A. A. Saracino Daniela Besozzi Fabrizio Gelain
author_facet	Marco S. Nobile Federico Fontana Luca Manzoni Paolo Cazzaniga Giancarlo Mauri Gloria A. A. Saracino Daniela Besozzi Fabrizio Gelain
author_sort	Marco S. Nobile
title	HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
title_short	HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
title_full	HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
title_fullStr	HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
title_full_unstemmed	HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides
title_sort	hyperbeta: characterizing the structural dynamics of proteins and self-assembling peptides
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d5f7ef478f74696a15526357172f6b7
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HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides

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