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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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PEGASUS: Prediction of MD-derived protein flexibility from sequence.

Yann Vander Meersche1, Gabriel Duval1, Gabriel Cretin1

  • 1Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, DSIMB, Paris, France.

Protein Science : a Publication of the Protein Society
|July 17, 2025
PubMed
Summary

Predicting protein flexibility is crucial for function. PEGASUS, a new tool, uses protein language models to predict simulated protein dynamics from sequences, bridging the gap left by experimental methods.

Keywords:
deep learningmolecular dynamicsprediction of protein propertiesprotein dynamicsprotein flexibilityprotein language modelssequence‐based predictions

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Protein flexibility is vital for biological function.
  • Experimental methods for assessing protein dynamics are costly and limited.
  • Molecular dynamics (MD) simulations offer detailed insights into protein flexibility, with increasing data availability.

Purpose of the Study:

  • To develop a sequence-based predictor for MD-derived protein flexibility information.
  • To address the gap between protein sequence identification and dynamic information.

Main Methods:

  • Utilized the ATLAS database and Protein Language Models.
  • Developed ProtEin lanGuAge models for prediction of SimUlated dynamicS (PEGASUS).
  • Integrated four sequence representations to predict residue-wise backbone fluctuation, dihedral angle deviations, and Local Distance Difference Test.

Main Results:

  • PEGASUS predicts key MD-derived flexibility metrics from protein sequences.
  • The tool integrates multiple sequence representations for enhanced prediction.
  • Web server and standalone utility are available for user access.

Conclusions:

  • PEGASUS provides a valuable computational tool for predicting protein dynamics.
  • The approach leverages advances in protein language models and MD simulations.
  • Facilitates the study of protein flexibility for a large number of sequences.