KNexPHENIX: A PHENIX-Based Workflow for Improving Cryo-EM and Crystallographic Structural Models

New and improved methods for visualizing complex macromolecules in atomic detail continue to expand structural information in the Protein Data Bank but accurately refining atomic models from experimental maps remains a challenge due to efficiency limitations of current refinement approaches. Standard PHENIX refinement can partially address these limitations with its speed and accessibility but often fails to yield the best model compared to more computationally demanding approaches. To support improved macromolecular model building, we therefore developed "KNexPHENIX", a PHENIX-based workflow that combines staged refinement, geometry minimization, and customized refinement parameters. KNexPHENIX can be used to refine macromolecular structures obtained via cryo-electron microscopy (cryo-EM) or X-ray crystallography, regardless of molecular size or composition. KNexPHENIX was evaluated on deposited structures and de novo models and consistently produced models with lower MolProbity scores, indicating improved model stereochemistry, compared to default PHENIX, REFMAC Servalcat, REFMAC, or CERES refinement. Importantly, this was accomplished while maintaining model-to-map correlation for cryo-EM data sets and maintaining or reducing the R_free - R_work difference below accepted thresholds for X-ray crystallographic structures, thus limiting overfitting while preserving refinement accuracy. While remaining dependent on the initial model and the choice of starting structure (e.g., from AlphaFold, Boltz, or RoseTTAFold), these results establish the KNexPHENIX workflow as a practical, accessible approach for refining both cryo-EM and crystallographic structures, enabling the generation of improved models for deposition.