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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
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.
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.
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