Abstract
Advances in docking protocols have significantly enhanced the field of protein-protein interaction (PPI) modulation, with AlphaFold2 (AF2) and molecular dynamics (MD) refinements playing pivotal roles. This study evaluates the performance of AF2 models against experimentally solved structures in docking protocols targeting PPIs. Using a dataset of 16 interactions with validated modulators, we benchmarked eight docking protocols, revealing similar performance between native and AF2 models. Local docking strategies outperformed blind docking, with TankBind_local and Glide providing the best results across the structural types tested. MD simulations and other ensemble generation algorithms such as AlphaFlow, refined both native and AF2 models, improving docking outcomes but showing significant variability across conformations. These results suggest that, while structural refinement can enhance docking in some cases, overall performance appears to be constrained by limitations in scoring functions and docking methodologies. Although protein ensembles can improve virtual screening, predicting the most effective conformations for docking remains a challenge. These findings support the use of AF2-generated structures in docking protocols targeting PPIs and highlight the need to improve current scoring methodologies. SCIENTIFIC CONTRIBUTION: This study provides a systematic benchmark of docking protocols applied to protein-proteininteractions (PPIs) using both experimentally solved structures and AlphaFold2 models. Byintegrating molecular dynamics ensembles and AlphaFlow-generated conformations, we showthat structural refinement improves docking outcomes in selected cases, but overallperformance remains constrained by docking scoring function limitations. Our analysis showsthat AlphaFold2 models perform comparably to native structures in PPI docking, validating theiruse when experimental data are unavailable. These results establish a reference framework forfuture PPI-focused virtual screening and underscore the need for improved scoring functionsand ensemble-based approaches to better exploit emerging structural prediction tools.