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Elvati, Paolo
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- Creator:
- Elvati, Paolo, Luyet, Chloe, Wang, Yichun, Liu, Changjiang, VanEpps, J. Scott, Kotov, Nicholas A., and Violi, Angela
- Description:
- Amyloid nanofibers are abundant in microorganisms and are integral components of many biofilms, serving various purposes, from virulent to structural. Nonetheless, the precise characterization of bacterial amyloid nanofibers has been elusive, with incomplete and contradicting results. The present work focuses on the molecular details and characteristics of PSMa1-derived functional amyloids present in Staphylococcus aureus biofilms, using a combination of computational and experimental techniques, to develop a model that can aid the design of compounds to control amyloid formation. Results from molecular dynamics simulations, guided and supported by spectroscopy and microscopy, show that PSMa1 amyloid nanofibers present a helical structure formed by two protofilaments, have an average diameter of about 12 nm, and adopt a left-handed helicity with a periodicity of approximately 72 nm. The chirality of the self-assembled nanofibers, an intrinsic geometric property of its constituent peptides, is central to determining the fibers' lateral growth.
- Keyword:
- molecular self-assembly, computational nanotechnology, nanobiotechnology, and structural properties
- Citation to related publication:
- Paolo Elvati, Chloe Luyet, Yichun Wang, Changjiang Liu, J. Scott VanEpps, Nicholas A. Kotov, and Angela Violi ACS Applied Nano Materials 2023 6 (8), 6594-6604 DOI: 10.1021/acsanm.3c00174
- Discipline:
- Engineering and Science
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Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles
- Creator:
- Saldinger, Jacob, Raymond, Matt , Elvati, Paolo, and Violi, Angela
- Description:
- The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at https://software.rcc.uchicago.edu/mscg/
- Keyword:
- Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
- Citation to related publication:
- https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
- Discipline:
- Science