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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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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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AI-based quality assessment methods for protein structure models from cryo-EM.

Han Zhu1, Genki Terashi2, Farhanaz Farheen1

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Current Research in Structural Biology
|February 25, 2025
PubMed
Summary

Cryo-electron microscopy (cryo-EM) is advancing structural biology, but map interpretation can be challenging. Artificial intelligence tools are emerging to improve the accuracy of protein models derived from cryo-EM data.

Keywords:
Cryo-EMCryo-electron microscopyDeep learningModel quality assessmentModel validationStructural biologyStructure modeling

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution protein structures.
  • Accurate interpretation of cryo-EM maps, especially in low-resolution regions, remains a significant challenge.
  • Existing validation scores assess map-model compatibility and model stereochemistry but have limitations.

Purpose of the Study:

  • To highlight the challenges in interpreting cryo-EM maps and validating resulting protein models.
  • To introduce the potential of artificial intelligence (AI) in addressing these challenges.
  • To emphasize the role of AI in improving the accuracy and reliability of cryo-EM-derived structures.

Main Methods:

  • Review of current validation strategies for cryo-EM structure models.
  • Exploration of recent advancements in artificial intelligence applications for structural biology.
  • Discussion of AI-driven tools for cryo-EM map interpretation and model refinement.

Main Results:

  • Cryo-EM is increasingly used for high-resolution structure determination.
  • Manual model building in low-resolution cryo-EM map regions is prone to errors.
  • AI tools show promise in enhancing the validation and refinement of cryo-EM protein models.

Conclusions:

  • AI offers novel capabilities for improving the accuracy of cryo-EM-derived atomic models.
  • AI-driven validation and refinement can lead to deeper biological insights.
  • The integration of AI is crucial for overcoming current limitations in cryo-EM data interpretation.