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Missense mutations in the PTEN gene linked to both cancer and autism spectrum disorder (ASD) were studied. Machine learning and protein dynamics reveal how these mutations cause distinct cellular changes, aiding in understanding and potentially treating these conditions.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Missense mutations in oncogenic proteins, like PTEN, are linked to both cancer and neurodevelopmental disorders such as autism spectrum disorder (ASD).
  • Understanding the genotype-phenotype correlations of PTEN mutations is crucial, but the mechanisms behind its dual association with cancer and ASD remain unclear.
  • PTEN is a key tumor suppressor and its dysregulation is implicated in various cancers and neurodevelopmental conditions.

Purpose of the Study:

  • To elucidate the molecular mechanisms underlying PTEN mutations associated with both cancer and ASD (PTEN-cancer/ASD mutations).
  • To develop an integrated approach combining machine learning (ML) and structural dynamics for predicting PTEN-cancer/ASD mutation effects.
  • To identify shared molecular pathways between cancer and ASD driven by PTEN mutations.

Main Methods:

  • Integrative analysis of biophysical and network biology signatures.
  • Development of a machine learning (ML) model with an integrated score for classifying PTEN-cancer/ASD mutations.
  • Molecular dynamics simulations to investigate the impact of mutations on protein structure and dynamics.

Main Results:

  • Biophysical and network analyses revealed a complex energetic and functional landscape for PTEN-cancer/ASD mutations.
  • The developed ML model successfully classified and predicted PTEN-cancer/ASD mutations, highlighting the role of protein dynamics.
  • Molecular dynamics simulations showed that PTEN-cancer/ASD mutations induce specific conformational changes, including P loop alterations and inter-domain allosteric regulation.

Conclusions:

  • The study provides an interpretable ML model integrated with structural dynamics to enhance understanding of PTEN-cancer/ASD mutations.
  • Protein dynamics play a significant role in predicting cellular phenotypes associated with PTEN mutations.
  • Identifying shared mechanisms between cancer and ASD offers potential for developing novel therapeutic strategies targeting PTEN.