scholarly journals Twists or turns: stabilising alpha vs. beta turns in tetrapeptides

2019 ◽  
Vol 10 (45) ◽  
pp. 10595-10600
Author(s):  
Huy N. Hoang ◽  
Timothy A. Hill ◽  
Gloria Ruiz-Gómez ◽  
Frederik Diness ◽  
Jody M. Mason ◽  
...  
Keyword(s):  
Secondary Structures ◽  
Side Chain ◽  
Ring Size ◽  
Beta Turns

Twisting or turning peptides: ring size and chi angle in side chain cross-linked tetrapeptides together control α- or β-turn structures, which mimic irregular secondary structures in proteins.

scholarly journals Role of Side Chain Size in the Formation of Secondary Structures in Model Peptides

2015 ◽  
Vol 108 (2) ◽  
pp. 519a
Author(s):  
Farbod Mahmoudinobar ◽  
Cristiano L. Dias ◽  
Ronen Zangi
Keyword(s):  
Secondary Structures ◽  
Side Chain ◽  
Model Peptides

CuI-Catalyzed Azide–Alkyne Intramoleculari-to-(i+4) Side-Chain-to-Side-Chain Cyclization Promotes the Formation of Helix-Like Secondary Structures

2010 ◽  
Vol 2010 (3) ◽  
pp. 446-457 ◽  
Author(s):  
Mario Scrima ◽  
Alexandra Le Chevalier-Isaad ◽  
Paolo Rovero ◽  
Anna Maria Papini ◽  
Michael Chorev ◽  
...  
Keyword(s):  
Secondary Structures ◽  
Side Chain

scholarly journals N-Oxide Polyethers as Kinetic Hydrate Inhibitors: Side Chain Ring Size Makes the Difference

2021 ◽  
Author(s):  
Qian Zhang ◽  
Larissa Limmer ◽  
Holger Frey ◽  
Malcolm A. Kelland
Keyword(s):  
Side Chain ◽  
Ring Size ◽  
Chain Ring ◽  
The Difference

scholarly journals MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns

2019 ◽  
Author(s):  
Chao Fang ◽  
Zhaoyu Li ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Sequence Similarity ◽  
Protein Secondary Structure ◽  
Web Server ◽  
Experimental Tests ◽  
Secondary Structures ◽  
Supplementary Information ◽  
Beta Turns ◽  
Torsion Angles ◽  
Protein Secondary Structures ◽  
Protein Functions

Abstract Motivation Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, developed based on advanced deep neural networks, achieved state-of-the-art performance for predicting secondary structures, backbone torsion angles, beta-turns and gamma-turns, respectively. An easy-to-use web service will provide the community a convenient way to use these methods for research and development. Results MUFold-SSW, a new web server, is presented. It provides predictions of protein secondary structures, torsion angles, beta-turns and gamma-turns for a given protein sequence. This server implements MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, which performed well for both easy targets (proteins with weak sequence similarity in PDB) and hard targets (proteins without detectable similarity in PDB) in various experimental tests, achieving results better than or comparable with those of existing methods. Availability and implementation MUFold-SSW is accessible at http://mufold.org/mufold-ss-angle. Supplementary information Supplementary data are available at Bioinformatics online.

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