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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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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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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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FIBOS: R and python packages for analyzing protein packing and structure.

Herson H M Soares1, João P R Romanelli2, Patrick J Fleming3

  • 1Institute of Technological Sciences, Federal University of Itajubá, Itabira, 35903-087, Brazil.

Bioinformatics (Oxford, England)
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

FIBOS, a new R and Python package, enhances atomic packing analysis for protein structures. It enables precise comparison between experimental and AlphaFold-predicted models, revealing subtle differences in atomic detail.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning in Biology

Background:

  • Machine learning models achieve high accuracy in predicting protein 3D structures.
  • Challenges remain in achieving atomic-level accuracy for these predictions.
  • The Occluded Surface (OS) algorithm is crucial for atomic packing analysis but lacks high-level language implementations.

Purpose of the Study:

  • To introduce FIBOS, a novel R and Python package.
  • To integrate and enhance the Occluded Surface (OS) methodology.
  • To enable detailed atomic-level comparisons between experimental and predicted protein structures.

Main Methods:

  • FIBOS package development in R and Python.
  • Incorporation of the enhanced Occluded Surface (OS) methodology.
  • Application of FIBOS for comparing experimental and AlphaFold-predicted protein structures.

Main Results:

  • FIBOS provides an implementation of the OS algorithm in R and Python.
  • Atomic-level comparisons reveal similar average packing between experimental and AlphaFold models.
  • AlphaFold models show slightly higher variability and specific outlier patterns compared to experimental structures.

Conclusions:

  • FIBOS facilitates robust atomic-level structural comparisons.
  • The package aids in assessing the accuracy of protein structure prediction models.
  • Identified variability in AlphaFold models highlights areas for future refinement.