scholarly journals Avoiding Regions Symptomatic of Conformational and Functional Flexibility to Identify Antiviral Targets in Current and Future Coronaviruses

2016 ◽  
Vol 8 (11) ◽  
pp. 3471-3484 ◽  
Author(s):  
Jordon Rahaman ◽  
Jessica Siltberg-Liberles

Abstract Within the last 15 years, two related coronaviruses (Severe Acute Respiratory Syndrome [SARS]-CoV and Middle East Respiratory Syndrome [MERS]-CoV) expanded their host range to include humans, with increased virulence in their new host. Coronaviruses were recently found to have little intrinsic disorder compared with many other virus families. Because intrinsically disordered regions have been proposed to be important for rewiring interactions between virus and host, we investigated the conservation of intrinsic disorder and secondary structure in coronaviruses in an evolutionary context. We found that regions of intrinsic disorder are rarely conserved among different coronavirus protein families, with the primary exception of the nucleocapsid. Also, secondary structure predictions are only conserved across 50–80% of sites for most protein families, with the implication that 20–50% of sites do not have conserved secondary structure prediction. Furthermore, nonconserved structure sites are significantly less constrained in sequence divergence than either sites conserved in the secondary structure or sites conserved in loop. Avoiding regions symptomatic of conformational flexibility such as disordered sites and sites with nonconserved secondary structure to identify potential broad-specificity antiviral targets, only one sequence motif (five residues or longer) remains from the >10,000 starting sites across all coronaviruses in this study. The identified sequence motif is found within the nonstructural protein (NSP) 12 and constitutes an antiviral target potentially effective against the present day and future coronaviruses. On shorter evolutionary timescales, the SARS and MERS clades have more sequence motifs fulfilling the criteria applied. Interestingly, many motifs map to NSP12 making this a prime target for coronavirus antivirals.

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi

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.


2014 ◽  
Vol 169 ◽  
pp. 179-193 ◽  
Author(s):  
Julian Heinrich ◽  
Michael Krone ◽  
Seán I. O'Donoghue ◽  
Daniel Weiskopf

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination—however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human–computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.


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