Machine Learning and Structural Dynamics-Based Approach to Reveal Molecular Mechanism of PTEN Missense Mutations Shared by Cancer and Autism Spectrum Disorder

Missense mutations in oncogenic proteins
that are concurrently
associated with neurodevelopmental disorders have garnered significant
attention. Phosphatase and tensin homologue (PTEN) serves as a paradigmatic
model for mapping its mutational landscape and identifying genotypic
predictors of distinct phenotypic outcomes, including cancer and autism
spectrum disorder (ASD). Despite extensive research into the genotype-phenotype
correlations of PTEN mutations, the mechanisms underlying the dual
association of specific PTEN mutations with both cancer and ASD (PTEN-cancer/ASD
mutations) remain elusive. This study introduces an integrative approach
that combines machine learning (ML) with structural dynamics to elucidate
the molecular effects of PTEN-cancer/ASD mutations. Analysis of biophysical
and network-biology-based signatures reveals a complex energetic and
functional landscape. Subsequently, an ML model and corresponding
integrated score were developed to classify and predict PTEN-cancer/ASD
mutations, underscoring the significance of protein dynamics in predicting
cellular phenotypes. Further molecular dynamics simulations demonstrated
that PTEN-cancer/ASD mutations induce dynamic alterations characterized
by open conformational changes restricted to the P loop and coupled
with interdomain allosteric regulation. This research aims to enhance
the genotypic and phenotypic understanding of PTEN-cancer/ASD mutations
through an interpretable ML model integrated with structural dynamics
analysis. By identifying shared mechanisms between cancer and ASD,
the findings pave the way for the development of novel therapeutic
strategies.