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

The number of structures deposited in the Protein Data Bank (PDB) has increased exponentially in recent years due to the availability of new methods to visualize increasingly complex macromolecules and their complexes in atomic detail. However, accurately refining atomic models from experimental maps remains a challenge due to limitations of current approaches for efficient model refinement. Standard PHENIX refinement can partially address these limitations with its speed and broad accessibility but often fails to yield the best model compared to those generated by more computationally intensive approaches. We therefore developed "KNexPHENIX", a customized semi-automated PHENIX-based workflow to support optimal macromolecular model building. KNexPHENIX can be used to refine macromolecular structures obtained from both cryo-electron microscopy (cryo-EM) and X-ray crystallography and is independent of the nature or size of the molecule. We evaluated KNexPHENIX on deposited structures as well as on de novo models chosen based on their visual fit to the deposited map or generated by molecular replacement. Compared to other approaches-PHENIX with default settings, REFMAC Servalcat, REFMAC, and CERES-KNexPHENIX consistently produced models with lower MolProbity scores, indicating improved model stereochemistry. Importantly, this was accomplished while maintaining model-to-map correlation for cryo-EM datasets and maintaining or reducing the R_free-R_work difference below accepted thresholds for X-ray crystallographic structures, limiting overfitting while preserving refinement accuracy. These results establish the KNexPHENIX workflow as a practical, accessible approach for refining both cryo-EM and crystallographic structures, enabling the generation of models with enhanced quality metrics suitable for deposition and to guide further experimental studies.