Self-assembly of 33-mer gliadin peptide oligomers

Soft Matter ◽  
2015 ◽  
Vol 11 (44) ◽  
pp. 8648-8660 ◽  
Author(s):  
M. G. Herrera ◽  
L. A. Benedini ◽  
C. Lonez ◽  
P. L. Schilardi ◽  
T. Hellweg ◽  
...  

The 33-mer gliadin peptide, is a highly immunogenic peptide involved in celiac disease and probably in other immune pathologies associated to gliadin. The spontaneous self-assembly of 33-mer in water is reported, providing a better insight into oligomers morphology and secondary structure.

FEBS Journal ◽  
2019 ◽  
Vol 287 (10) ◽  
pp. 2134-2149 ◽  
Author(s):  
María Georgina Herrera ◽  
María Florencia Gómez Castro ◽  
Eduardo Prieto ◽  
Exequiel Barrera ◽  
Veronica Isabel Dodero ◽  
...  

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.


2019 ◽  
Vol 29 (8) ◽  
pp. 873
Author(s):  
Gaetana Paolella ◽  
Marilena Lepretti ◽  
Stefania Martucciello ◽  
Lillà Lionetti ◽  
Carla Esposito ◽  
...  

2004 ◽  
Vol 126 (22) ◽  
pp. 7009-7014 ◽  
Author(s):  
Ho-Joong Kim ◽  
Wang-Cheol Zin ◽  
Myongsoo Lee

2012 ◽  
Vol 55 (1) ◽  
pp. 50-55 ◽  
Author(s):  
Lourdes Mozo ◽  
Jesús Gómez ◽  
Esther Escanlar ◽  
Carlos Bousoño ◽  
Carmen Gutiérrez

2004 ◽  
Vol 2004 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Mourad Elhabiri ◽  
Josef Hamacek ◽  
Jean-Claude G. Bünzli ◽  
Anne-Marie Albrecht-Gary

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