摘要
Protein crystallization remains a major bottleneck in X-ray crystallography due to difficulties in achieving favorable molecular arrangements within the crystal lattice. While protein–protein interactions at molecular packing interfaces are crucial for determining crystallization conditions, methods for predicting crystal packing interfaces and systematically exploring crystallization conditions remain limited. In this study, we present MASCL (Molecular Assembly Simulation in Crystal Lattice), a novel approach that integrates AlphaFold with symmetrical docking to simulate crystal packing. To evaluate packing quality, we introduced PackQ, a stringent metric based on the DockQ framework, where models with scores above 0.36 are considered successful. In benchmark tests on P4 1 2 1 2 and P4 3 2 1 2 space groups, MASCL successfully predicted packing interfaces for 26.8% and 30.1% of targets within the top 100 models. When focusing on models with successfully predicted initial crystallographic dimeric assemblies (DockQ ≥ 0.23), success rates improved to 57.9% and 39.8% within the top 25 models, respectively. Additionally, we developed AAI-PatchBag, a patch-based method using physicochemical descriptors to assess molecular interface similarity. Compared to conventional condition-searching strategies like sequence alignment, structure superposition, and shape comparison, AAI-PatchBag reduced the number of trials required to identify potential crystallization conditions. Applied to lysozyme crystallization, AAI-PatchBag efficiently identified conditions yielding crystals with the desired packing. Overall, MASCL and AAI-PatchBag advance the prediction of protein–protein interactions within the crystal lattice and facilitate the identification of potential crystallization conditions through molecular packing interface similarity, contributing to a deeper understanding of protein crystallization.