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

Ribbon diagrams are important for protein visualization, used to convey the secondary structure in a clear and concise manner. However, most algorithms used to generate these diagrams do not maintain visual continuity when viewing a molecular trajectory, with certain sections of ribbons flipping between clockwise and counterclockwise twists. Here we outline a new method which prevents this artifact by morphing between consecutive cross sections instead of rotating. This yields diagrams which are well suited for viewing dynamic simulations, such as those used for interactive molecular dynamics. We illustrate the utility of this algorithm by using it to visualize iMD-VR (interactive molecular dynamics in virtual reality) simulations of the secondary structure of the SARS-CoV-2 main protease (Mpro), which is being investigated as a potential target for COVID drug therapies.

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A new method for determining protein secondary structure by laser raman spectroscopy applied to FD phage

Biophysical Journal ◽

10.1016/s0006-3495(80)84944-7 ◽

1980 ◽

Vol 32 (1) ◽

pp. 232-234 ◽

Cited By ~ 15

Author(s):

R.W. Williams ◽

A.K. Dunker ◽

W.L. Peticolas

Keyword(s):

Raman Spectroscopy ◽

Secondary Structure ◽

Protein Secondary Structure ◽

New Method ◽

Laser Raman Spectroscopy ◽

Laser Raman

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Faculty Opinions recommendation of Protein secondary structure prediction using deep convolutional neural fields.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726067888.793548469 ◽

2018 ◽

Author(s):

Patrice Koehl

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Neural Fields ◽

Protein Secondary Structure Prediction

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

Current Drug Discovery Technologies ◽

10.2174/1570163815666180227162157 ◽

2019 ◽

Vol 16 (2) ◽

pp. 159-172 ◽

Cited By ~ 3

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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