Integrating model simulation tools and cryo‐electron microscopy

Abstract

The power of computer simulations, including machine-learning, has become an inseparable part of scientific analysis of biological data. This has significantly impacted the field of cryogenic electron microscopy (cryo-EM), which has grown dramatically since the "resolution-revolution." Many maps are now solved at 3-4 Å or better resolution, although a significant proportion of maps deposited in the Electron Microscopy Data Bank are still at lower resolution, where the positions of atoms cannot be determined unambiguously. Additionally, cryo-EM maps are often characterized by a varying local resolution, partly due to conformational heterogeneity of the imaged molecule. To address such problems, many computational methods have been developed for cryo-EM map reconstruction and atomistic model building. Here, we review the development in algorithms and tools for building models in cryo-EM maps at different resolutions. We describe methods for model building, including rigid and flexible fitting of known models, model validation, small-molecule fitting, and model visualization. We provide examples of how these methods have been used to elucidate the structure and function of dynamic macromolecular machines.

Assessment of three‐dimensional RNA structure prediction in CASP15

“…Third, many and perhaps most of the CASP15 RNA targets showed conformational flexibility, for example, as evidenced by differences in conformations of different monomers in crystallographic asymmetric units or, in cryo‐ensembles captured by electron microscopy as classes of conformations separable by automated subclassification and/or 3D variability analysis 39 . In the current assessment, predictor groups were scored based on the best observed agreement of all their submitted models vs. all available experimental models, effectively assuming that modelers were predicting single structures.…”

“…To prevent penalization of novel methods that might give poor models for some targets, the sums of just the positive Z RNA over all targets were used to make final rankings. For targets where experimentalists provided multiple conformations to either represent experimental uncertainty or bona fide conformational diversity (e.g., different copies in the crystallographic asymmetric unit or multiple conformations captured by cryo-EM 39 ), predictor models were compared to all available experimental models. Groups were rewarded based on their best score.…”

Assessment of three-dimensional RNA structure prediction in CASP15

“…Third, many and perhaps most of the CASP15 RNA targets showed conformational flexibility, e.g., as evidenced by differences in conformations of different monomers in crystallographic asymmetric units or, in cryo-ensembles captured by electron microscopy, by classes of conformations separable by automated subclassification and/or 3D variability analysis 36 . In the current assessment, predictor groups were scored based on the best observed agreement of all their submitted models vs. all available experimental models, effectively assuming that modelers were predicting single structures.…”

CryoFold 2.0: Cryo-EM Structure Determination with MELD

“…These innovations have enabled researchers to determine single-particle structures at subnanometer resolution . When the resolution is better than 2 Å, structure-building workflows originally developed for X-ray crystallography, such as Coot, Phenix, CNS, and REFMAC, can be applied to determining atomic positions in the map . For samples with resolutions higher than 3.5 Å, recent developments in deep-learning-based tools, such as DeepTracer and ModelAngelo, have been instrumental in reducing the need for demanding human intervention.…”