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Protein Dynamics in Living Cells

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Related Experiment Video

Updated: May 7, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Learning protein representations with conformational dynamics.

Dan Kalifa¹, Eric Horvitz^2,3, Kira Radinsky¹

¹Department of Computer Science, Technion-Israel Institute of Technology, Technion City, Haifa 3200003, Israel.

Bioinformatics (Oxford, England)

|May 5, 2026

Summary

This summary is machine-generated.

We developed DynamicsPLM, a novel protein language model that uses multiple protein shapes to improve predictions for protein interactions and functions. This dynamics-aware approach better reflects how proteins work in cells.

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Area of Science:

* Computational biology
* Structural bioinformatics
* Machine learning in protein science

Background:

* Proteins exist in multiple conformational states, influencing their function.
* Current protein language models often use static structures, missing dynamic information.
* Understanding protein dynamics is crucial for predicting interactions, localization, and activity.

Purpose of the Study:

* To develop a protein language model that incorporates protein conformational dynamics.
* To improve the accuracy of predicting protein functions and interactions by considering multiple states.
* To advance machine learning models in protein biology towards more biologically relevant representations.

Main Methods:

* Developed DynamicsPLM, a protein language model.
* Conditioned the model on ensembles of computationally generated protein conformations.
* Utilized state-aware representations derived from dynamic ensembles.

Main Results:

* DynamicsPLM significantly improves performance in protein-protein interaction, subcellular localization, enzyme classification, and metal-ion binding predictions.
* Achieved a four-point accuracy gain on a protein-protein interaction benchmark compared to static models.
* Demonstrated an eleven-point accuracy gain on a dataset enriched for proteins with multiple conformational states.

Conclusions:

* Protein conformational variability is informative and should be integrated into modeling.
* A shift towards dynamics-aware protein modeling is necessary for biological accuracy.
* This work provides a foundation for understanding protein mechanisms and generating testable hypotheses.