Integrating AlphaFold2, RoseTTAFold2, and HADDOCK to refine Protein-Protein Interaction (PPI) candidate selection: A...

Protein-protein interactions (PPIs) underpin most cellular processes, and disruptions to these interactions can lead to cellular dysfunction and disease. Understanding PPIs is essential for studying disease mechanisms, yet traditional experimental approaches are time- and labor-intensive. Recent advances in AI-based structural prediction tools, including AlphaFold2 and RoseTTAFold2, now enable efficient in silico exploration of potential PPIs. To develop an integrated and practical multi-tool system for PPI investigation, we present a dual-arm computational pipeline centered on the ZWINT (SIP30) kinetochore protein, which we identified as a key gene in neuropathic signaling. The first arm of the workflow generates PPI models using AlphaFold2 and RoseTTAFold2 and evaluates model consistency using TM-Align. The second arm assesses binding affinity by identifying interface residues with PyMOL and calculating docking scores with HADDOCK. Together, these methods provide both quantitative and qualitative evaluations of candidate PPIs. Using this framework, we examined three established interactors (SNAP25, CAMK2A, UBC) and four exploratory proteins (STX1A, VCP, BLOC1S2, ARC) in combination with ZWINT. This study demonstrates that AI-supported in silico analysis can streamline PPI discovery by prioritizing biologically plausible interactors and guiding downstream experimental validation.