Application of Tree Growing up Algorithm to Protein Secondary Structure Estimation

2008 ◽  
Author(s):  
Seda Arslan Tuncer ◽  
Ali Karci
Keyword(s):  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Growing Up ◽  
Structure Estimation

A Holistic Approach for Protein Secondary Structure Estimation from Infrared Spectra in H2O Solutions

2000 ◽  
Vol 285 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Ganesh Vedantham ◽  
H.Gerald Sparks ◽  
Samir U. Sane ◽  
Stelios Tzannis ◽  
Todd M. Przybycien
Keyword(s):  
Secondary Structure ◽  
Infrared Spectra ◽  
Protein Secondary Structure ◽  
Holistic Approach ◽  
Structure Estimation

scholarly journals A New Method for Characterizing Biological Samples Using FTIR Microscopy with Protein Secondary Structure Estimation (IR-SSE) and ATR-TPZ Mapping

2003 ◽  
Vol 9 (S02) ◽  
pp. 1068-1069
Author(s):  
Ken-ichi Akao ◽  
Hisasi Masago ◽  
Toshiyuki Nagoshi ◽  
Amanda L. Jenkins ◽  
Richard A. Larsen ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Biological Samples ◽  
Protein Secondary Structure ◽  
New Method ◽  
Ftir Microscopy ◽  
Structure Estimation

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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