Machine Learning and Structural Dynamics-Based Approach to Reveal Molecular Mechanism of PTEN Missense Mutations...

Area of Science:

• Molecular Biology
• Genetics
• 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 specific mechanisms by which PTEN mutations contribute to both cancer and ASD phenotypes is crucial but remains challenging.
• PTEN mutations represent a key area for investigating genotype-phenotype correlations and their underlying molecular effects.

Purpose of the Study:

• To elucidate the molecular mechanisms of PTEN mutations associated with both cancer and ASD using an integrative approach.
• To develop a predictive model for classifying PTEN-cancer/ASD mutations based on their biophysical and dynamic properties.
• To identify shared molecular pathways between cancer and ASD driven by PTEN mutations.

Main Methods:

• Utilized machine learning (ML) combined with structural dynamics analysis to study PTEN mutations.
• Analyzed biophysical and network-biology-based signatures to understand the energetic and functional landscape.
• Performed molecular dynamics simulations to investigate conformational changes and allosteric regulation.

Main Results:

• Identified complex energetic and functional landscapes associated with PTEN-cancer/ASD mutations.
• Developed an interpretable ML model and integrated score for classifying and predicting these mutations.
• Demonstrated that PTEN-cancer/ASD mutations induce specific dynamic alterations, including open conformational changes in the P loop and interdomain allosteric regulation.

Conclusions:

• Protein dynamics play a significant role in predicting cellular phenotypes associated with PTEN mutations.
• The study provides a framework for understanding the dual role of PTEN mutations in cancer and ASD.
• Findings offer insights into shared mechanisms, potentially guiding the development of novel therapeutic strategies for both conditions.