Publications

2022

We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.

Low-THz Vibrations of Biological Membranes

REF Chloe Luyet, Paolo Elvati, Jordan Vinh, and Angela Violi

A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low- THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell.

A mobile phase of water/acetonitrile was able to achieve chiral separation of CNPs derived from L- and D-cysteine denoted as L-CNPs and D-CNPs. Molecular dynamics simulations show that the teicoplanin-based stationary phase has a higher affinity for L-CNPs than for D-CNPs, in agreement with experiments. The experimental and computational findings jointly indicate that chiral centers of chiral CNPs are present at their surface, which is essential for the multiple applications of these chiral nanostructures and equally essential for interactions with biomolecules and circularly polarized photons.

Chiral chromatography and surface chirality of carbon nanoparticles

REF Misché Hubbard, Chloe Luyet, Prashant Kumar, Paolo Elvati, J. Scott VanEpps, Angela Violi, and Nicholas A. Kotov

Chiral carbon nanoparticles (CNPs) represent a rapidly evolving area of research for optical and biomedical technologies. Similar to small molecules, applications of CNPs as well as fundamental relationships between their optical activity and structural asymmetry would greatly benefit from their enantioselective separations by chromatography. However, this technique remains in its infancy for chiral carbon and other nanoparticles. The possibility of effective separations using high performance liquid chromatography (HPLC) with chiral stationary phases remains an open question whose answer can also shed light on the components of multiscale chirality of the nanoparticles. Herein, we report a detailed methodology of HPLC for successful separation of chiral CNPs and establish a path for its future optimization. 

In contrast, the corresponding value for a dusty plasma liquid was found to be as small as 0.0139. Another basic finding for the dusty plasma liquid is that S(k) at small k generally increases with temperature, with its lowest value, noted above, occurring near the melting point. Simulations were carried out for the dusty plasma liquid, and their results are generally consistent with the experiment. Since a dusty plasma has a soft interparticle interaction, our findings support earlier theoretical suggestions that a useful design strategy for creating materials having exceptionally low values of S(0), so-called "hyperuniform’’ materials, is the use of a condensed material composed of particles that interact softly at their periphery.

A Multiphysics Modeling of Electromagnetic Signaling Phenomena at kHz-GHz Frequencies in Bacterial Biofilms

REF (*Abstract Only*) Navid Barani; Kamal Sarabandi; Nicholas A. Kotov; J. Scott Vanepps; Paolo Elvati; Yichun Wang; Angela Violi

Especially small values of the static structure factor S(k) at long wavelengths, i.e., small k, were obtained in an analysis of experimental data, for a two-dimensional dusty plasma in its liquid state. For comparison, an analysis of S(k) data was carried out for many previously published experiments with other liquids. The latter analysis indicates that the magnitude of S(k) at small k is typically in a range of about 0.02 to 0.13. 

For the reception, the induced electric field can either exert force on the charges of adjacent nanofibrils associated with the neighboring cells or affect the placement/conformation of a certain charged messenger protein within the cell. The proposed model is based on a coupled system of electrical and mechanical nanoscale structures, which predicts signal transmission and reception within kHz-GHz frequency ranges. Different mechanisms for generating EM signals at various frequency bands related to the structure of the cell and their biofilm constituents are discussed.

Comparison of the static structure factor at long wavelengths for a dusty plasma liquid and other liquids

REF Vitaliy Zhuravlyov, J. Goree, Jack F. Douglas, Paolo Elvati, and Angela Violi

This paper presents a model that describes a possible mechanism for electromagnetic (EM) signal transmission and reception by bacterial cells within their biofilm communities. Bacterial cells in biofilms are embedded into a complex extracellular matrix containing, among other components, charged helical nanofibrils from amyloid-forming peptides. Based on the current knowledge about the nanoscale structure and dynamics of the amyloids, we explore a hypothetical model that the mechanical vibration of these nanofibrils allows the cells to transmit EM signals to their neighboring cells and the surrounding environment. 

Silicon nanocrystal solids with inter-dot distances varying from 3 to 5 nm are fabricated by varying the length and surface coverage of alkyl ligands in solution-phase and gas-phase functionalized silicon nanocrystals. The inter-dot energy transfer rates are extracted from steady-state and time-resolved photoluminescence measurements, enabling a direct comparison to theoretical predictions. Our results reveal that the distance-dependent energy transfer rates in Si NCs decay faster than predicted by the Förster mechanism, suggesting higher-order multipole interactions.

Distance-dependent resonance energy transfer in alkyl-terminated Si nanocrystal solids

REF Zhaohan Li, Zachary L. Robinson,  Paolo Elvati, Angela Violi, and  Uwe R. Kortshagen

Understanding and controlling the energy transfer between silicon nanocrystals is of significant importance for the design of efficient optoelectronic devices. However, previous studies on silicon nanocrystal energy transfer were limited because of the strict requirements to precisely control the inter-dot distance and to perform all measurements in air-free environments to preclude the effect of ambient oxygen. Here, we systematically investigate the distance-dependent resonance energy transfer in alkyl-terminated silicon nanocrystals for the first time.

interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein–protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.

Unifying structural descriptors for biological and bioinspired nanoscale complexes

REF Minjeong Cha, Emine Sumeyra Turali Emre, Xiongye Xiao, Ji-Young Kim, Paul Bogdan, J. Scott VanEpps, Angela Violi & Nicholas A. Kotov

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein–protein interactions can serve as a guide for designing protein–nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein–protein 

Development of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. 

Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

REF Mayank Baranwal, Abram Magner, Jacob Saldinger, Emine S. Turali‑Emre, Paolo Elvati, Shivani Kozarekar, J. Scott VanEpps, Nicholas A. Kotov, Angela Violi and Alfred O. Hero

However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.