Expanding the NMR Toolkit for Biological Solids: Oxygen-17 Enriched Fmoc-Amino Acids

2021 ◽  
Author(s):  
Brittney Klein ◽  
Dylan Tkachuk ◽  
Victor Terskikh ◽  
Vladimir K Michaelis
Keyword(s):  
Amino Acids ◽  
Solid State ◽  
Biological Systems ◽  
Nmr Parameters ◽  
Biological Solids

We report the solid-state 17O NMR parameters for five previously uncharacterized N-α-fluoren-9-yl-methoxycarbonyl-O-t-butyl (Fmoc) protected amino acids. These molecules are critical to constructing synthetic biological systems, like peptides, and provide an...

Solid state 19F NMR parameters of fluorine-labeled amino acids. Part II: Aliphatic substituents

2008 ◽  
Vol 191 (1) ◽  
pp. 16-23 ◽  
Author(s):  
Stephan L. Grage ◽  
Ulrich H.N. Dürr ◽  
Sergii Afonin ◽  
Pavel K. Mikhailiuk ◽  
Igor V. Komarov ◽  
...  
Keyword(s):  
Amino Acids ◽  
Solid State ◽  
19F Nmr ◽  
Nmr Parameters

Solid state 19F NMR parameters of fluorine-labeled amino acids. Part I: Aromatic substituents

2008 ◽  
Vol 191 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Ulrich H.N. Dürr ◽  
Stephan L. Grage ◽  
Raiker Witter ◽  
Anne S. Ulrich
Keyword(s):  
Amino Acids ◽  
Solid State ◽  
19F Nmr ◽  
Nmr Parameters

scholarly journals Theoretical Investigations Into the Variability of the 15N Solid-State NMR Parameters Within an Antimicrobial Peptide Ampullosporin A

2018 ◽  
pp. S349-S356 ◽  
Author(s):  
J. CZERNEK ◽  
J. BRUS
Keyword(s):  
Amino Acids ◽  
Solid State ◽  
Solid State Nmr ◽  
Density Functional ◽  
Antimicrobial Properties ◽  
15N Nmr ◽  
Nmr Parameters ◽  
Theoretical Investigations ◽  
Computational Data ◽  
Shielding Tensor

The solid-state NMR measurements play an indispensable role in studies of interactions between biological membranes and peptaibols, which are amphipathic oligopeptides with a high abundance of α-aminobutyric acid (Aib). The solid-state NMR investigations are important in establishing the molecular models of the pore forming and antimicrobial properties of peptaibols, but rely on certain simplifications. Some of the underlying assumptions concern the parameters describing the 15N NMR chemical shielding tensor (CST) of the amide nitrogens in Aib and in conventional amino acids. Here the density functional theory (DFT) based calculations were applied to the known crystal structure of one of peptaibols, Ampullosporin A, in order to explicitly describe the variation of the 15N NMR parameters within its backbone. Based on the DFT computational data it was possible to verify the validity of the assumptions previously made about the differences between Aib and other amino acids in the isotropic part of the CST. Also the trends in the magnitudes and orientations of the anisotropic components of the CST, as revealed by the DFT calculations of the full periodic structure of Ampullosporin A, were thoroughly analyzed, and may be employed in future studies of peptaibols.

scholarly journals Far infrared spectra of solid state aliphatic amino acids in different protonation states

2010 ◽  
Vol 132 (11) ◽  
pp. 115105 ◽  
Author(s):  
Aurélien Trivella ◽  
Thomas Gaillard ◽  
Roland H. Stote ◽  
Petra Hellwig
Keyword(s):  
Amino Acids ◽  
Solid State ◽  
Infrared Spectra ◽  
Far Infrared ◽  
Far Infrared Spectra

scholarly journals Supervised Learning Model Predicts Protein Adsorption to Nanotubes

2021 ◽  
Author(s):  
Rebecca L Pinals ◽  
Nicholas Ouassil ◽  
Jackson Travis Del Bonis-O'Donnell ◽  
Jeffrey W Wang ◽  
Markita P Landry
Keyword(s):  
Amino Acids ◽  
Protein Adsorption ◽  
Biological Systems ◽  
Protein Corona ◽  
Engineered Nanoparticles ◽  
Mass Spectrometry Data ◽  
In Vitro Synthesis ◽  
Intractable Problem

Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling often prevents effective implementation. Such biofouling is the result of spontaneous protein adsorption to the nanoparticle surface, forming the "protein corona" and altering the physicochemical properties, and thus intended function, of the nanotechnology. To better apply engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies which proteins adsorb to nanoparticles. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we then apply the classifier to rapidly identify and experimentally validate proteins with high binding affinity to SWCNTs. Using protein properties based solely on amino acid sequence, we further determine protein features associated with increased likelihood of SWCNT binding: proteins with high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Furthermore, proteins with high leucine residue content and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides an important tool to undertake the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use.

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