scholarly journals The Jpred 3 secondary structure prediction server

2008 ◽  
Vol 36 (Web Server) ◽  
pp. W197-W201 ◽  
Author(s):  
C. Cole ◽  
J. D. Barber ◽  
G. J. Barton
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Prediction Server

scholarly journals JPred: a consensus secondary structure prediction server

1998 ◽  
Vol 14 (10) ◽  
pp. 892-893 ◽  
Author(s):  
J. A. Cuff ◽  
M. E. Clamp ◽  
A. S. Siddiqui ◽  
M. Finlay ◽  
G. J. Barton
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Consensus Secondary Structure ◽  
Prediction Server

scholarly journals JPred4: a protein secondary structure prediction server

2015 ◽  
Vol 43 (W1) ◽  
pp. W389-W394 ◽  
Author(s):  
Alexey Drozdetskiy ◽  
Christian Cole ◽  
James Procter ◽  
Geoffrey J. Barton
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
Prediction Server

scholarly journals Computational studies on LiP H isolated from Ganoderma lucidum GD88

2015 ◽  
Vol 67 (3) ◽  
pp. 817-828
Author(s):  
Nayana Parambayil ◽  
Aiswarya Chenthamarakshan ◽  
Arinnia Anto ◽  
Sudha Hariharan ◽  
Padma Nambisan
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Ganoderma Lucidum ◽  
Secondary Structure Prediction ◽  
Cell Wall Component ◽  
Reading Frame ◽  
Prediction Server ◽  
Structure Stabilization ◽  
H Protein

Ganoderma lucidum is a basidiomycete fungus that produces ligninase for the modification of lignin. Lignin peroxidase (LiP) is a glycoprotein that acts on the recalcitrant cell wall component lignin. In the present study, the phylogenetic analysis of Ganoderma lucidum GD88 with the partial coding sequence (cds) of other LiP isoforms was performed using MEGA6. After determination of the open reading frame, the +3 frame nucleotide sequence was converted to protein using the EMBOSS Transseq and the secondary structure was predicted using the Chou and Fasman Secondary Structure Prediction server (CFSSP). Protein modeling was also performed by SWISS-MODEL. The obtained result shows that the lipH partial cds of Ganoderma lucidum GD88 is homologous to the lipD gene of Phanerochaete chrysosporium. The secondary structure prediction result revealed that the percent content of the helix (67) is higher than the percent contents of sheet (53.4) and turns (13.6). According to the generated model, LiP H protein is a homodimer with chains A and B. The heme acts as a ligand and plays a major role in structure stabilization.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

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.

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