Helix Propensity of Highly Fluorinated Amino Acids

2006 ◽  
Vol 128 (49) ◽  
pp. 15556-15557 ◽  
Author(s):  
Hsien-Po Chiu ◽  
Yuta Suzuki ◽  
Donald Gullickson ◽  
Raheel Ahmad ◽  
Bashkim Kokona ◽  
...  
Keyword(s):  
Amino Acids ◽  
Fluorinated Amino Acids ◽  
Helix Propensity

Fluorinated amino acids in amyloid formation: a symphony of size, hydrophobicity and α-helix propensity

2014 ◽  
Vol 5 (2) ◽  
pp. 819-830 ◽  
Author(s):  
Ulla I. M. Gerling ◽  
Mario Salwiczek ◽  
Cosimo D. Cadicamo ◽  
Holger Erdbrink ◽  
Constantin Czekelius ◽  
...  
Keyword(s):  
Amino Acids ◽  
Amyloid Formation ◽  
Fluorinated Amino Acids ◽  
Helix Propensity ◽  
Α Helix

scholarly journals Evaluating electronic structure methods for accurate calculation of 19 F chemical shifts in fluorinated amino acids

2017 ◽  
Vol 38 (30) ◽  
pp. 2605-2617 ◽  
Author(s):  
Jayangika N. Dahanayake ◽  
Chandana Kasireddy ◽  
Jonathan M. Ellis ◽  
Derek Hildebrandt ◽  
Olivia A. Hull ◽  
...  
Keyword(s):  
Amino Acids ◽  
Electronic Structure ◽  
Chemical Shifts ◽  
Accurate Calculation ◽  
Fluorinated Amino Acids ◽  
Electronic Structure Methods

scholarly journals Mechanomorphogenic Films Formed via Interfacial Assembly of Fluorinated Amino Acids (Adv. Funct. Mater. 40/2021)

2021 ◽  
Vol 31 (40) ◽  
pp. 2170300
Author(s):  
Janna N. Sloand ◽  
Tyler E. Culp ◽  
Nichole M. Wonderling ◽  
Enrique D. Gomez ◽  
Scott H. Medina
Keyword(s):  
Amino Acids ◽  
Interfacial Assembly ◽  
Fluorinated Amino Acids

scholarly journals Chemical aminoacylation of tRNAs with fluorinated amino acids for in vitro protein mutagenesis

2010 ◽  
Vol 6 ◽  
Author(s):  
Shijie Ye ◽  
Allison Ann Berger ◽  
Dominique Petzold ◽  
Oliver Reimann ◽  
Benjamin Matt ◽  
...  
Keyword(s):  
Amino Acids ◽  
Translation System ◽  
Biologically Relevant ◽  
Fluorinated Amino Acids ◽  
Free Translation ◽  
Trna Species ◽  
A Cell ◽  
Protein Environment ◽  
Vitro Protein

This article describes the chemical aminoacylation of the yeast phenylalanine suppressor tRNA with a series of amino acids bearing fluorinated side chains via the hybrid dinucleotide pdCpA and ligation to the corresponding truncated tRNA species. Aminoacyl-tRNAs can be used to synthesize biologically relevant proteins which contain fluorinated amino acids at specific sites by means of a cell-free translation system. Such engineered proteins are expected to contribute to our understanding of discrete fluorines’ interaction with canonical amino acids in a native protein environment and to enable the design of fluorinated proteins with arbitrary desired properties.

scholarly journals A novel method for achieving an optimal classification of the proteinogenic amino acids

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andre Then ◽  
Karel Mácha ◽  
Bashar Ibrahim ◽  
Stefan Schuster
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Interaction Potential ◽  
Binary Separation ◽  
Multiple Alignments ◽  
Novel Method ◽  
Optimal Classification ◽  
Helix Propensity ◽  
Α Helix

Abstract The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find “the” optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron–ion-interaction potential, hydrophobicity, α-helix propensity, and π-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are.

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