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AlphaKnot: server to analyze entanglement in structures predicted by AlphaFold methods.

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AlphaKnot analyzes protein entanglement in AlphaFold models using pLDDT confidence. It offers a webserver for custom analysis and a database of existing protein knotting predictions.

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Protein structure prediction accuracy has significantly improved with tools like AlphaFold.
  • Understanding protein entanglement is crucial for deciphering protein function and evolution.
  • Quantifying and classifying protein knotting in predicted structures remains a challenge.

Purpose of the Study:

  • To introduce AlphaKnot, a computational server for analyzing and visualizing protein entanglement in AlphaFold models.
  • To provide a probabilistic framework for defining and categorizing protein knotting.
  • To create a database of protein knotting information derived from publicly available AlphaFold predictions.

Main Methods:

  • Development of a web server integrating AlphaFold model analysis with pLDDT confidence values.
  • Probabilistic definition of protein knotting and knot core identification.
  • Classification of entanglements into 'Knots', 'Unsure', and 'Artifacts' based on confidence scores.

Main Results:

  • AlphaKnot provides a user-friendly web interface for analyzing knotting in individual AlphaFold predictions.
  • A comprehensive database of protein knotting for 21 proteomes is now available.
  • Knot types, complexities, and core locations are visualized using matrix diagrams.

Conclusions:

  • AlphaKnot offers valuable insights into protein entanglement, aiding in the interpretation of AlphaFold models.
  • The server and database facilitate studies on entanglement-function correlations, protein evolution, and modeling.
  • Probabilistic assessment of knotting, informed by pLDDT, enhances the reliability of entanglement analysis.