Investigating the Nature of PRM:SH3 Interactions Using Artificial Intelligence and Molecular Dynamics.

Area of Science:

• Biochemistry
• Structural Biology
• Biophysics

Background:

• Protein-peptide complexes are essential for biological processes.
• Biomolecular condensates, formed by these complexes, play critical roles in cellular signaling.
• The specific binding mechanisms and driving forces of complexes like PRM:SH3 remain unclear.

Purpose of the Study:

• To investigate the binding nature of the PRM:SH3 complex.
• To understand the impact of mutations on binding affinity and mode.
• To identify the key factors governing PRM:SH3 complex condensation.

Main Methods:

• Utilized AlphaFold2 for structural prediction.
• Performed molecular dynamics simulations to analyze binding interactions.
• Correlated simulation-derived metrics (RMSD, dendrogram height) with experimental data.

Main Results:

• Proline-to-alanine mutations increased flexibility and decreased binding affinity.
• Charge-altering mutations altered the binding mode and strength.
• Local flexibility and hydrophobic effects were identified as primary drivers of PRM:SH3 binding.
• Root-mean-square deviation and dendrogram height showed correlation with experimental dissociation constants.

Conclusions:

• Mutation analysis provides insights into PRM:SH3 binding dynamics.
• Flexibility and hydrophobic interactions are critical for PRM:SH3 complex formation.
• Computational approaches, including AlphaFold2 and MD simulations, are effective for studying protein-peptide binding and condensation.