scholarly journals 3D PERCEPTION OF MAXIMUM DENSITY ZONE ON RAMACHANDRAN PLOTS FOR ZIKA VIRUS PROTEIN STRUCTURES.

2016 ◽  
Vol 4 (5) ◽  
pp. 1076-1086
Author(s):  
Mayukh Mukhopadhyay ◽  
◽  
Parama Bhaumik ◽  
2016 ◽  
Author(s):  
Mayukh Mukhopadhyay ◽  
Parama Bhaumik

AbstractThe Ramachandran plot is among the most central concepts in structural biology which uses torsion angles to describe polypeptide and protein conformation. To help visualize the features of high-fidelity Ramachandran plots, it is helpful to look beyond the common two-dimensional psi-phi-plot, which for a large dataset does not serve very well to convey the true nature of the distribution. In particular, when a large subset of the observations is found very narrowly distributed within one small region, this is not well seen in the simple plot because the data points congest one another. Zika Virus (ZIKV) protein databank has been chosen as specimen for analysis. This is because the structure, tropism, and pathogenesis of ZIKV are largely unknown and are the focus of current investigations in an effort to address the need for rapid development of vaccines and therapeutics. After a brief survey on Zika Virus, it is shown that when a dense dataset of ZIKV protein databank is passed through a colour-coded scaled algorithm, a three dimensional plot gets generated which gives a much more compelling impression of the proportions of residues in the different parts of the protein rather than representing it in a normal two dimensional psi-phi plot.


2017 ◽  
Vol 71 ◽  
pp. 180-187 ◽  
Author(s):  
Tom Kazmirchuk ◽  
Kevin Dick ◽  
Daniel. J. Burnside ◽  
Brad Barnes ◽  
Houman Moteshareie ◽  
...  

Author(s):  
Ina Lee ◽  
Sandra Bos ◽  
Ge Li ◽  
Shusheng Wang ◽  
Gilles Gadea ◽  
...  

The recent Zika virus (ZIKV) outbreak in Americas surprised all of us because of its rapid spread and association with neurologic disorders including fetal microcephaly, brain and ocular anomalies and Guillain-Barré syndrome. In responding to this global health outcry, unprecedented and world-wide efforts are taking place to study the ZIKV etiology. Much have been learned about this virus in the areas of epidemiology, clinical manifestation, viral sequences and protein structures, as well as effects of ZIKV infection on fetal brain development and microcephaly. However, the molecular mechanism underlying ZIKV-mediated neurologic disorders remains elusive. Some critical questions include: 1) what type of virologic changes has taken place that increased the viral virulence? 2) which ZIKV protein(s) is responsible for the enhanced viral pathogenicity? And 3) how the newly adapted and pathogenic ZIKV strains alter their interactions with host cells leading to neurologic disorders? The goal of this review is to explore the molecular insights into the ZIKV-host interactions with special focuses on host cell receptor usage for viral entry, host cellular and immune antiviral responses, ZIKV counteraction and ZIKV-induced cytopathic effects. Our hope with this literature review is to inspire additional studies focusing on molecular studies of ZIKV-host Interactions.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ajeet Kaushik ◽  
Adriana Yndart ◽  
Sanjeev Kumar ◽  
Rahul Dev Jayant ◽  
Arti Vashist ◽  
...  

2021 ◽  
pp. 2002140
Author(s):  
Ana Castro ◽  
Juan Manuel Carreño ◽  
James Duehr ◽  
Florian Krammer ◽  
Ravi S. Kane

2013 ◽  
Vol 82 (2) ◽  
pp. 230-239 ◽  
Author(s):  
Steven Hayward ◽  
David P. Leader ◽  
Fawzia Al-Shubailly ◽  
E. James Milner-White

2020 ◽  
Author(s):  
Julia Abel ◽  
Marika Kaden ◽  
Katrin Sophie Bohnsack ◽  
Mirko Weber ◽  
Christoph Leberecht ◽  
...  

AbstractIn this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. α-helices and β-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.


PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5745 ◽  
Author(s):  
Ranjan Mannige

Protein backbones occupy diverse conformations, but compact metrics to describe such conformations and transitions between them have been missing. This report re-introduces the Ramachandran number (ℛ) as a residue-level structural metric that could simply the life of anyone contending with large numbers of protein backbone conformations (e.g., ensembles from NMR and trajectories from simulations). Previously, the Ramachandran number (ℛ) was introduced using a complicated closed form, which made the Ramachandran number difficult to implement. This report discusses a much simpler closed form of ℛ that makes it much easier to calculate, thereby making it easy to implement. Additionally, this report discusses how ℛ dramatically reduces the dimensionality of the protein backbone, thereby making it ideal for simultaneously interrogating large numbers of protein structures. For example, 200 distinct conformations can easily be described in one graphic using ℛ (rather than 200 distinct Ramachandran plots). Finally, a new Python-based backbone analysis tool—BackMAP—is introduced, which reiterates how ℛ can be used as a simple and succinct descriptor of protein backbones and their dynamics.


2010 ◽  
Vol 1 (3-4) ◽  
pp. 271-283 ◽  
Author(s):  
Scott A. Hollingsworth ◽  
P. Andrew Karplus

AbstractThe Ramachandran plot is among the most central concepts in structural biology, seen in publications and textbooks alike. However, with the increasing numbers of known protein structures and greater accuracy of ultra-high resolution protein structures, we are still learning more about the basic principles of protein structure. Here, we use high-fidelity conformational information to explore novel ways, such as geo-style and wrapped Ramachandran plots, to convey some of the basic aspects of the Ramachandran plot and of protein conformation. We point out the pressing need for a standard nomenclature for peptide conformation and propose such a nomenclature. Finally, we summarize some recent conceptual advances related to the building blocks of protein structure. The results for linear groups imply the need for substantive revisions in how the basics of protein structure are handled.


2017 ◽  
Author(s):  
Tom Kazmirchuk ◽  
Kevin Dick ◽  
Daniel. J. Burnside ◽  
Brad Barnes ◽  
Houman Moteshareie ◽  
...  

AbstractThe production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.


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