Scale-dependent hierarchical resolution: applications to atomic resolution and model validation in cryoEM

Abstract

AbstractThe recent cryoEM resolution revolution has had a tremendous impact on our ability to investigate biomolecular structure and function. However, outstanding questions about the reliability of using a cryoEM-derived molecular model for interpreting experiments and building further hypotheses limit its full impact. Significant amounts of research have been focused on developing metrics to assess cryoEM model quality, yet no consensus exists. This is in part because the meaning of cryoEM model quality is not well defined. In this work, we formalize cryoEM model quality in terms of whether a cryoEM map is better described by a model with localized atomic coordinates or by a lower-resolution model that lacks atomic-level information. This approach emerges from a novel, quantitative definition of image resolution based upon the hierarchical structure of biomolecules, which enables computational selection of the length scale to which a biomolecule is resolved based upon the available evidence embedded in the experimental data. In the context of cryoEM, we develop a machine learning-based implementation of this framework, called hierarchical atomic resolution perception (HARP), for assessing local atomic resolution in a cryoEM map and thus evaluating cryoEM model quality in a theoretically and statistically well-defined manner. Finally, using HARP, we perform a meta-analysis of the cryoEM-derived structures in the Protein Data Bank (PDB) to assess the state of atomic resolution in the field and quantify factors that affect it.Significance StatementInformation about biomolecular structure is very useful to researchers investigating the mechanistic basis of biomolecular function. To enable researchers to assess the reliability of biomolecular structural information that is derived from experimental data, a new theoretical definition of atomic resolution has been developed. This definition forms the basis of a machine learning-based tool called HARP that can be used to measure the local atomic resolution of cryoEM maps, as well as the quality of the biomolecular structural models derived from such cryoEM maps. To demonstrate the power and utility of these conceptually novel approaches, this work demonstrates how they may be used to investigate the scientific and social factors that have historically affected the quality of cryoEM experiments.