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Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
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Published on: November 5, 2018

P.R.E.S.S.--an R-package for exploring residual-level protein structural statistics.

Yuanyuan Huang1, Stephen Bonett, Andrzej Kloczkowski

  • 1Program on Bioinformatics and Computational Biology, Department of Mathematics, Iowa State University, Ames, Iowa 50011, USA.

Journal of Bioinformatics and Computational Biology
|July 20, 2012
PubMed
Summary

P.R.E.S.S. (Protein Residue-level Structural Statistics) is an R-package providing researchers access to protein structural data. It enables analysis of residue-level properties and includes tools for modeling and refinement.

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Area of Science:

  • Structural Biology
  • Bioinformatics
  • Computational Chemistry

Background:

  • Protein structure analysis relies on understanding residue-level properties.
  • Accessing and analyzing large datasets of these properties is computationally intensive.
  • Existing tools may lack comprehensive statistical analysis and modeling capabilities.

Purpose of the Study:

  • To develop an R-package, P.R.E.S.S., for accessing and manipulating protein residue-level structural data.
  • To provide tools for statistical analysis, modeling, and refinement of protein structures.
  • To make these functionalities accessible to researchers through an open-source package with a GUI.

Main Methods:

  • Downloaded and surveyed a large set of high-resolution protein structures.
  • Calculated and documented residue-level virtual bond lengths, angles, and torsion angles.
  • Developed new tools for computing residue-level statistical potentials and Ramachandran-like plots.

Main Results:

  • Established a comprehensive dataset of protein residue-level structural properties.
  • Enabled querying and display of statistical distributions and correlations.
  • Implemented novel tools for structural analysis and refinement, including statistical potentials and Ramachandran-like plots.

Conclusions:

  • P.R.E.S.S. provides a valuable, open-source resource for protein structural analysis.
  • The package facilitates advanced modeling and refinement through statistical insights.
  • Its user-friendly GUI ensures accessibility for researchers in any R environment.