Use of molecular mechanics for secondary structure prediction. Is it possible to reveal α-helix?

Relationships among the amino acid sequences of viral movement proteins related to the 30 kDa (‘30K’) movement protein of tobacco mosaic virus – the 30K superfamily – were explored. Sequences were grouped into 18 families. A comparison of secondary structure predictions for each family revealed a common predicted core structure flanked by variable N- and C-terminal domains. The core consisted of a series of β-elements flanked by an α-helix on each end. Consensus sequences for each of the families were generated and aligned with one another. From this alignment an overall secondary structure prediction was generated and a consensus sequence that can recognize each family in database searches was obtained. The analysis led to criteria that were used to evaluate other virus-encoded proteins for possible membership of the 30K superfamily. A rhabdoviral and a tenuiviral protein were identified as 30K superfamily members, as were plant-encoded phloem proteins. Parsimony analysis grouped tubule-forming movement proteins separate from others. Establishment of the alignment of residues of diverse families facilitates comparison of mutagenesis experiments done on different movement proteins and should serve as a guide for further such experiments.

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Statistical analysis of pair-wise compatibility of spatially nearest neighbor and adjacent residues in α-helix and β-strands: application to a minimal model for secondary structure prediction

Biophysical Chemistry ◽

10.1016/s0301-4622(02)00230-2 ◽

2003 ◽

Vol 103 (1) ◽

pp. 35-49 ◽

Cited By ~ 5

Author(s):

Srikanta Sen

Keyword(s):

Statistical Analysis ◽

Secondary Structure ◽

Minimal Model ◽

Structure Prediction ◽

Nearest Neighbor ◽

Secondary Structure Prediction ◽

Α Helix

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Faculty Opinions recommendation of COFOLD: an RNA secondary structure prediction method that takes co-transcriptional folding into account.

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

10.3410/f.718010599.793476797 ◽

2013 ◽

Author(s):

Scott Silverman

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Prediction Method ◽

Rna Secondary Structure Prediction ◽

Structure Prediction Method ◽

Secondary Structure Prediction Method

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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 Discrete Hopfield Neural Network Based MIS Finding Algorithm for Stems Selecting and Its Application in RNA Secondary Structure Prediction

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2008.00051 ◽

2009 ◽

Vol 31 (1) ◽

pp. 51-58

Author(s):

Qi LIU ◽

Yin ZHANG ◽

Xiu-Zi YE ◽

Rong-Dong YU

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Hopfield Neural Network ◽

Rna 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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