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Updated: May 24, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Mofan Feng1,2, Xiaoxi Wei1, Xi Zheng1,2

  • 1Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China.

Nature Communications
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

We developed PSMutPred, a machine learning tool to predict how missense mutations affect protein phase separation, aiding the interpretation of variants of uncertain significance (VUS) in intrinsically disordered regions (IDRs). This advances understanding of disease pathogenesis.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Genetics

Background:

  • Predicting the functional impact of protein variants is crucial but challenging, especially for variants of uncertain significance (VUS) in intrinsically disordered regions (IDRs).
  • Intrinsically disordered regions are implicated in numerous physiological processes, and their dysfunction is linked to various diseases.
  • Phase separation, a process central to many cellular functions, is closely associated with IDRs.

Purpose of the Study:

  • To develop a computational method for predicting the impact of missense mutations on protein phase separation.
  • To improve the interpretation of variants of uncertain significance (VUS) located in intrinsically disordered regions (IDRs).
  • To aid in understanding the pathogenicity of disease-associated variants.

Main Methods:

  • Developed PSMutPred, a machine learning model leveraging missense variants that alter phase separation propensity.
  • Integrated phase separation principles into the analysis of missense variants within IDRs.
  • Validated PSMutPred predictions using in vitro experiments.

Main Results:

  • PSMutPred demonstrates robust performance in predicting missense variants that affect natural phase separation.
  • The model successfully predicted the impact of mutations on phase separation propensity.
  • In vitro experiments confirmed the predictive accuracy of PSMutPred.

Conclusions:

  • PSMutPred significantly contributes to decoding the pathogenesis of disease variants, particularly those in IDRs.
  • The approach aids in the interpretation of a large number of VUSs in IDRs, accelerating clinical diagnosis.
  • This work highlights the importance of phase separation in understanding variant effects and disease mechanisms.