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Related Experiment Video

Updated: Sep 14, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

SeaMoon: From protein language models to continuous structural heterogeneity.

Valentin Lombard¹, Dan Timsit¹, Sergei Grudinin²

¹Sorbonne Université, CNRS, IBPS, Department of Computational, Quantitative and Synthetic Biology (CQSB, UMR7238), 75005 Paris, France.

Structure (London, England : 1993)

|July 19, 2025

Summary

SeaMoon predicts protein motions directly from amino acid sequences using deep learning. This method captures alternative protein conformations, advancing our understanding of cellular functions.

Keywords:

PCA convolutional neural network deep learning principal component prediction protein language models protein motion prediction subspace prediction transfer learning

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Last Updated: Sep 14, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational biology
Structural bioinformatics
Deep learning in protein science

Background:

Protein dynamics are crucial for cellular functions, driving interactions and cellular processes.
Predicting protein 3D structures has advanced, shifting focus to sampling alternative conformations.
Deep learning models are increasingly used to explore protein conformational space.

Purpose of the Study:

To investigate the direct prediction of continuous protein motion representations from sequences.
To develop a deep learning model that bypasses the need for 3D structural information.
To assess the model's ability to capture diverse protein dynamics.

Main Methods:

Leveraging protein language model (pLM) embeddings as input.
Utilizing a lightweight convolutional neural network architecture.
Training and evaluating the model against experimental conformation datasets.

Main Results:

SeaMoon successfully predicted ground-truth motions for 40% of test proteins with reasonable accuracy.
The model captured protein motions not detectable by traditional physics-based methods like normal mode analysis.
SeaMoon demonstrated generalization capabilities to proteins with no significant sequence similarity to the training data.

Conclusions:

Direct prediction of protein motions from sequences is feasible using deep learning.
SeaMoon offers a novel approach to sample protein conformations, complementing existing methods.
The model's retrainability allows adaptation to new protein language models and data.