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Linking protein aggregation and structural stability to predict pathogenic MYH7 variants via machine learning.

Ivan A Pyankov1, Marina A Kokorina2, Georgy N Rychkov3

  • 1Department of Chemical Medicine, Institute of Chemistry, St. Petersburg State University, St. Petersburg, Russian Federation; Chemical Engineering Center, ITMO University, St. Petersburg, Russian Federation.

Journal of Structural Biology
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

Genetic variants causing Myosin Storage Myopathy (MSM) destabilize the MYH7 protein. A new machine learning tool, RDSM-MYH7, accurately predicts pathogenic MYH7 mutations for early disease diagnosis.

Keywords:
Artificial intelligenceCoiled coilGenome sequencingMYH-7Machine learningMissense mutationMyosin storage myopathyProtein structure

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Genetic sequencing data is rapidly expanding, outpacing the identification of disease-causing variants.
  • Computational methods are crucial for linking genetic mutations to specific pathologies.

Purpose of the Study:

  • To develop a computational tool for predicting the pathogenicity of missense mutations in the MYH7 gene.
  • To improve the diagnosis of hereditary Myosin Storage Myopathy (MSM).

Main Methods:

  • Analysis of disease-causing variants in the MYH7 α-helical coiled-coil domain.
  • Development of a machine learning model (RDSM-MYH7) integrating protein aggregation and structural stability features.
  • Performance comparison with existing prediction tools.

Main Results:

  • Disease-associated MYH7 variants destabilize the α-helical coiled-coil domain more than non-pathogenic variants.
  • Pathogenic variants cluster in specific strained regions of the MYH7 coiled-coil dimer.
  • RDSM-MYH7 achieved high performance (F1=0.869, accuracy=0.875), outperforming existing tools.

Conclusions:

  • The RDSM-MYH7 tool can reliably identify pathogenic MYH7 variants associated with storage myopathy.
  • Clinical implementation of RDSM-MYH7 can aid in the early diagnosis of myopathies and other protein storage diseases.
  • Understanding protein unfolding and aggregation is key to diagnosing hereditary protein storage diseases.