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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning.

Swarnadeep Seth¹, Aniket Bhattacharya¹

¹Department of Physics, University of Central Florida, Orlando, Florida 32816-2385, United States.

Biomacromolecules

|March 12, 2025

Summary

This summary is machine-generated.

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Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

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This study combines Brownian dynamics simulations and deep learning to rapidly identify harmful missense mutations in intrinsically disordered proteins (IDPs). The developed neural network accelerates the discovery of mutation-prone regions, aiding disease research and therapeutic development.

Area of Science:

Computational Biology
Protein Science
Machine Learning

Background:

Intrinsically disordered proteins (IDPs) play crucial roles in cellular processes.
Missense mutations in IDPs can lead to significant structural changes and disease.
Predicting the impact of mutations in IDPs is computationally challenging.

Purpose of the Study:

To develop a rapid and accurate method for identifying large structural changes in IDPs caused by missense mutations.
To leverage deep learning and Brownian dynamics simulations for mutation impact prediction.
To accelerate the discovery of mutation-prone regions in IDPs relevant to disease.

Main Methods:

Utilized Brownian dynamics (BD) simulations on ∼6500 IDP sequences from MobiDB using the HPS2 model.

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Generated training datasets of gyration radii from BD simulations.

Developed a multilayer perceptron neural network (NN) architecture for predicting gyration radii.

Applied the NN to predict mutation effects for all missense permutations in IDPs.

Main Results:

Achieved 97% accuracy in predicting gyration radii for known IDPs using the NN.
Successfully identified mutation-prone regions that significantly alter the radius of gyration compared to wild-type sequences.
Demonstrated a (10^4–10^6)-fold increase in computational speed for mutation analysis.
Validated predictions through targeted BD simulations on selected mutants.

Conclusions:

The combined BD simulation and DL approach offers a highly efficient method for identifying detrimental missense mutations in IDPs.
This strategy significantly accelerates the identification of disease-associated mutations and potential therapeutic targets.
The developed methodology can be extended to predict other mutation-induced effects in disordered proteins.