Rings and ribbons in protein structures: Characterization using helical parameters and Ramachandran plots for repeating dipeptides

2013 ◽  
Vol 82 (2) ◽  
pp. 230-239 ◽  
Author(s):  
Steven Hayward ◽  
David P. Leader ◽  
Fawzia Al-Shubailly ◽  
E. James Milner-White
Keyword(s):  
Protein Structures ◽  
Ramachandran Plots ◽  
Helical Parameters

scholarly journals Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

2020 ◽  
Author(s):  
Julia Abel ◽  
Marika Kaden ◽  
Katrin Sophie Bohnsack ◽  
Mirko Weber ◽  
Christoph Leberecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Structures ◽  
Structural Protein ◽  
Additional Information ◽  
Importance Weighting ◽  
Interpretable Machine Learning ◽  
Plot Analysis ◽  
Generalized Matrix ◽  
Protein Classes ◽  
Ramachandran Plots

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.

scholarly journals The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5745 ◽  
Author(s):  
Ranjan Mannige
Keyword(s):  
Closed Form ◽  
Protein Structures ◽  
Residue Level ◽  
Analysis Tool ◽  
Protein Backbone ◽  
Large Numbers ◽  
Protein Backbones ◽  
Ramachandran Plots

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.

scholarly journals A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins

2010 ◽  
Vol 1 (3-4) ◽  
pp. 271-283 ◽  
Author(s):  
Scott A. Hollingsworth ◽  
P. Andrew Karplus
Keyword(s):  
Protein Structure ◽  
Structural Biology ◽  
Protein Conformation ◽  
Protein Structures ◽  
Building Blocks ◽  
Linear Groups ◽  
High Fidelity ◽  
Ramachandran Plot ◽  
Basic Principles ◽  
Ramachandran Plots

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.

scholarly journals A global Ramachandran score identifies protein structures with unlikely stereochemistry

2020 ◽  
Author(s):  
Oleg V. Sobolev ◽  
Pavel V. Afonine ◽  
Nigel W. Moriarty ◽  
Maarten L. Hekkelman ◽  
Robbie P. Joosten ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Gold Standard ◽  
Protein Structures ◽  
Data Bank ◽  
Z Score ◽  
Torsion Angles ◽  
Current Gold Standard ◽  
Quality Metric ◽  
Ramachandran Plots ◽  
Experimental Structure

SummaryRamachandran plots report the distribution of the (φ, Ψ) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to “outlier”, “allowed” and “favored” regions. While “zero unexplained outliers” can be considered the current “gold standard”, this can be misleading if deviations from expected distributions, even within the favored region, are not considered. We therefore revisited the Ramachandran Z-score (Rama-Z), a quality metric introduced more than two decades ago, but underutilized. We describe a re-implementation of the Rama-Z score in the Computational Crystallography Toolbox along with a new algorithm to estimate its uncertainty for individual models; final implementations are available both in Phenix and in PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications.

scholarly journals 3D Perception of Maximum Density Zone on Ramachandran Plots for Zika Virus Protein Structures

2016 ◽  
Author(s):  
Mayukh Mukhopadhyay ◽  
Parama Bhaumik
Keyword(s):  
Zika Virus ◽  
Rapid Development ◽  
Protein Structures ◽  
Three Dimensional ◽  
Small Region ◽  
Virus Protein ◽  
Two Dimensional ◽  
True Nature ◽  
Data Points ◽  
Ramachandran Plots

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.

The three-dimensional structure of frozen-hydrated left- and right-handed forms of bacterial flagellar filaments from an identical serotype

1986 ◽  
Vol 44 ◽  
pp. 298-299
Author(s):  
S. Trachtenberg ◽  
D. J. DeRosier
Keyword(s):  
Ionic Strength ◽  
Basal Body ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Protein Species ◽  
Liquid Environment ◽  
Bacterial Flagellum ◽  
Left And Right ◽  
Rotary Motor ◽  
Helical Parameters

The bacterial cell is propelled through the liquid environment by means of one or more rotating flagella. The bacterial flagellum is composed of a basal body (rotary motor), hook (universal coupler), and filament (propellor). The filament is a rigid helical assembly of only one protein species — flagellin. The filament can adopt different morphologies and change, reversibly, its helical parameters (pitch and hand) as a function of mechanical stress and chemical changes (pH, ionic strength) in the environment.

Insights into the biochemical and functional characterization of sortase E transpeptidase of Corynebacterium glutamicum

2019 ◽  
Vol 476 (24) ◽  
pp. 3835-3847 ◽  
Author(s):  
Aliyath Susmitha ◽  
Kesavan Madhavan Nampoothiri ◽  
Harsha Bajaj
Keyword(s):  
Protein Engineering ◽  
Cell Attachment ◽  
Functional Characterization ◽  
Protein Structures ◽  
Structural Similarity ◽  
Surface Proteins ◽  
Sortase A ◽  
Peptide Cleavage ◽  
New Findings

Most Gram-positive bacteria contain a membrane-bound transpeptidase known as sortase which covalently incorporates the surface proteins on to the cell wall. The sortase-displayed protein structures are involved in cell attachment, nutrient uptake and aerial hyphae formation. Among the six classes of sortase (A–F), sortase A of S. aureus is the well-characterized housekeeping enzyme considered as an ideal drug target and a valuable biochemical reagent for protein engineering. Similar to SrtA, class E sortase in GC rich bacteria plays a housekeeping role which is not studied extensively. However, C. glutamicum ATCC 13032, an industrially important organism known for amino acid production, carries a single putative sortase (NCgl2838) gene but neither in vitro peptide cleavage activity nor biochemical characterizations have been investigated. Here, we identified that the gene is having a sortase activity and analyzed its structural similarity with Cd-SrtF. The purified enzyme showed a greater affinity toward LAXTG substrate with a calculated KM of 12 ± 1 µM, one of the highest affinities reported for this class of enzyme. Moreover, site-directed mutation studies were carried to ascertain the structure functional relationship of Cg-SrtE and all these are new findings which will enable us to perceive exciting protein engineering applications with this class of enzyme from a non-pathogenic microbe.

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