cryo-EM Refinement Research Articles

Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps

Abstract Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing structure prediction method at CASP14) on cryo-EM refinement using the Phenix refinement suite for AlphaFold2 models. To study the robustness of model refinement at a lower resolution of interest, we introduced hybrid maps (i.e. experimental cryo-EM maps) filtered to lower resolutions by real-space convolution. The AlphaFold2 models were refined to attain good accuracies above 0.8 TM scores for 9 of the 13 cryo-EM maps. TM scores improved for AlphaFold2 models refined against all 13 cryo-EM maps of better than 4.5 Å resolution, 8 hybrid maps of 6 Å resolution, and 3 hybrid maps of 8 Å resolution. The results show that it is possible (at least with the Phenix protocol) to extend the refinement success below 4.5 Å resolution. We even found isolated cases in which resolution lowering was slightly beneficial for refinement, suggesting that high-resolution cryo-EM maps might sometimes trap AlphaFold2 models in local optima.

Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells.

Cryo-electron microscopy (cryo-EM) enables macromolecular structure determination in vitro and inside cells. In addition to aligning individual particles, accurate registration of sample motion and 3D deformation during exposures are crucial for achieving high resolution reconstructions. Here we describe M, a software tool that establishes a reference-based, multi-particle refinement framework for cryo-EM data and couples a comprehensive spatial deformation model to in silico correction of electron-optical aberrations. M provides a unified optimization framework for both frame-series and tomographic tilt-series data. We show that tilt-series data can provide the same resolution as frame-series on a purified protein specimen, indicating that the alignment step no longer limits the resolution obtainable from tomographic data. In combination with Warp and RELION, M resolves to residue-level a 70S ribosome bound to an antibiotic inside intact bacterial cells. Our work provides a computational tool that facilitates structural biology in cells.

Real-space quantum-based refinement for cryo-EM: Q|R#3.

Electron cryo-microscopy (cryo-EM) is rapidly becoming a major competitor toX-ray crystallography, especially for large structures that are difficult or impossible to crystallize. While recent spectacular technological improvements have led to significantly higher resolution three-dimensional reconstructions, the average quality of cryo-EM maps is still at the low-resolution end of the range compared with crystallography. A long-standing challenge for atomic model refinement has been the production of stereochemically meaningful models for this resolution regime. Here, it is demonstrated that including accurate model geometry restraints derived from ab initio quantum-chemical calculations (HF-D3/6-31G) can improve the refinement of an example structure (chain A of PDB entry 3j63). The robustness of the procedure is tested for additional structures with up to 7000 atoms (PDB entry 3a5x and chain C of PDB entry 5fn5) using the less expensive semi-empirical (GFN1-xTB) model. The necessary algorithms enabling real-space quantum refinement have been implemented in the latest version of qr.refine and are described here.

Dynamic and asymmetric fluctuations in the microtubule wall captured by high-resolution cryoelectron microscopy

Microtubules are tubular polymers with essential roles in numerous cellular activities. Structures of microtubules have been captured at increasing resolution by cryo-EM. However, dynamic properties of the microtubule are key to its function, and this behavior has proved difficult to characterize at a structural level due to limitations in existing structure determination methods. We developed a high-resolution cryo-EM refinement method that divides an imaged microtubule into its constituent protofilaments, enabling deviations from helicity and other sources of heterogeneity to be quantified and corrected for at the single-subunit level. We demonstrate that this method improves the resolution of microtubule 3D reconstructions and substantially reduces anisotropic blurring artifacts, compared with methods that utilize helical symmetry averaging. Moreover, we identified an unexpected, discrete behavior of the m-loop, which mediates lateral interactions between neighboring protofilaments and acts as a flexible hinge between them. The hinge angle adopts preferred values corresponding to distinct conformations of the m-loop that are incompatible with helical symmetry. These hinge angles fluctuate in a stochastic manner, and perfectly cylindrical microtubule conformations are thus energetically and entropically penalized. The hinge angle can diverge further from helical symmetry at the microtubule seam, generating a subpopulation of highly distorted microtubules. However, the seam-distorted subpopulation disappears in the presence of Taxol, a microtubule stabilizing agent. These observations provide clues into the structural origins of microtubule flexibility and dynamics and highlight the role of structural polymorphism in defining microtubule behavior.

Efficient real-space refinement for cryo-EM and crystallography

Efficient real-space refinement for cryo-EM and crystallography

Automatically Fixing Errors in Glycoprotein Structures with Rosetta

Recent advances in single-particle cryo-electron microscopy (cryoEM) have resulted in determination of an increasing number of protein structures with resolved glycans. However, existing protocols for the refinement of glycoproteins at low resolution have failed to keep up with these advances. As a result, numerous deposited structures contain glycan stereochemical errors. Here, we describe a Rosetta-based approach for both cryoEM and X-ray crystallography refinement of glycoproteins that is capable of correcting conformational and configurational errors in carbohydrates. Building upon a previous Rosetta framework, we introduced additional features and score terms enabling automatic detection, setup, and refinement of glycan-containing structures. We benchmarked this approach using 12 crystal structures and showed that glycan geometries can be automatically improved while maintaining good fit to the crystallographic data. Finally, we used this method to refine carbohydrates of the human coronavirus NL63 spike glycoprotein and of an HIV envelope glycoprotein, demonstrating its usefulness for cryoEM refinement.

Higher Resolution with the Same Data - Tuning cryo-EM Refinement in Relion

Structure refinement used to be limited by crystallization or computational time. We have eliminated the computational burden of cryo-em by accelerating data processing through development of a new, GPU-enabled version of RELION, in collaboration with its ongoing development. The iterative refinement of bio-molecular structures from cryo-em image data, is therefore faster than ever before. However no systematic analysis has been published on the optimal use of input data in the final stages of high-resolution reconstruction. As a consequence, selective exclusion of available image data (at the discretion of the experimenter) represents the currently most efficient (and often used) way to increase the attained resolution. We show that a higher level of reconstructed detail can be achieved by incorporating new methods into existing cryo-em refinement algorithms, which aim to increase the density of high-resolution data used in the final reconstruction steps. Using a combination of tunable parameters and guided data exclusion, we show that these methods can both increase resolution, as well as reduce sensitivity on the input data. These measures facilitate an improvement to the cryo-em refinement method by a) making reconstructed detail more robust to the used data, and b) rendering the process less subjective. Since cryo-em is rapidly becoming a common tool in structural biology, this represents a necessary development for an enormously influential method over the coming years.

Orders of Magnitude Faster cryo-EM Refinement with GPUs

Direct-electron detectors, increased computational power and advances in sample preparation have rapidly made single particle cryogenic electron microscopy (cryo-em) a leading structural determination method. However, the growing number of images collected with constantly increasing resolution raises the demand on the computational processing capabilities, rendering the refinement step an increasingly notable bottleneck. For large macromolecular complexes, like ribosomes, this can require up to weeks of time on computer clusters using RELION, the currently fastest growing refinement tool. In recognition of this issue we have, in close collaboration with the original developers, optimized a significant portion of RELION for execution on consumer streaming multiprocessors (GPUs). The massive parallelism inherent in the algorithm enables an efficient utilization of such hardware layout, where the large number of processors can be assigned isolated small tasks. Here we present a new implementation yielding a 100-fold speed-up of the most time consuming step (3D classification) using GPUs, with comparable prices to conventional hardware. The resulting increase in overall cost efficiency, and removal of the need for large clusters, drastically increases the accessibility of the method to the field. Whereas, the reduced computational time makes it feasible to further strengthen the capabilities of the method by expanding the biophysical analysis, with other computationally intensive methods. Cryo-em is undoubtedly becoming a dominant tool within structural biology, why the acquired leap in throughput and applicability will have a major overall impact on the field in the coming years.

From CRYO-EM Densities to Atom Coordinates and Ensembles with Bayes Approach

Near-atom resolution in 3D cryo-electron microscopy brings two new challenges for refining atom coordinates to densities. Because their underlying potentials become more rugged with increased resolution, established refinement methods are easily trapped in local minima. Second, single structures no longer well describe the inherent structural diversity that can be resolved with cryo-EM.Here, we developed a method to derive cryo-EM refinement potentials from a Bayesian approach. Our refinement potential takes a physical model of the cryo-EM measuring and reconstruction process into account using Bayesian statistics. The result is a potential that statistically correctly reflects the given EM-data and is smooth even at high resolution. Previously developed algorithms are contained as limiting cases.With our method we are able to represent the configurational dynamics that is captured in cryo-EM density maps through a series of features. The smoothness of our refinement energy landscape allows efficient sampling and refinement while also taking into account thermal fluctuations. We provide an refinement force constant and potential from the Bayes approach that truthfully represents the complete distribution of atom configurations underlying the given EM maps. Thus, in combination with traditional molecular dynamics simulation we create refined ensembles that - as a whole - represent a given cryo-EM map.We further use the advantage of the Bayes approach to generate molecular dynamics ensembles that represent the simultaneous input from multiple cryo-EM maps together to capture the physically relevant transitions between different states as resolved by cryo-EM. Overall, our methods enables complete use of the data in cryo-EM maps and provides structural interpretation from ensembles that will aid understanding ever more complex cryo-EM data.

Refinement of Atomic Structures Against cryo-EM Maps.

This review describes some of the methods for atomic structure refinement (fitting) against medium/high-resolution single-particle cryo-EM reconstructed maps. Some of the tools developed for macromolecular X-ray crystal structure analysis, especially those encapsulating prior chemical and structural information can be transferred directly for fitting into cryo-EM maps. However, despite the similarities, there are significant differences between data produced by these two techniques; therefore, different likelihood functions linking the data and model must be used in cryo-EM and crystallographic refinement. Although tools described in this review are mostly designed for medium/high-resolution maps, if maps have sufficiently good quality, then these tools can also be used at moderately low resolution, as shown in one example. In addition, the use of several popular crystallographic methods is strongly discouraged in cryo-EM refinement, such as 2Fo-Fc maps, solvent flattening, and feature-enhanced maps (FEMs) for visualization and model (re)building. Two problems in the cryo-EM field are overclaiming resolution and severe map oversharpening. Both of these should be avoided; if data of higher resolution than the signal are used, then overfitting of model parameters into the noise is unavoidable, and if maps are oversharpened, then at least parts of the maps might become very noisy and ultimately uninterpretable. Both of these may result in suboptimal and even misleading atomic models.

Bayesian cryo-EM Refinement

In light of recent high-resolution cryo-electron-microscopy (cryo-EM) structures of large complexes, modeling atom coordinates into cryo-EM densities faces new challenges. Though high-resolution structures provide more data, finding the best-fitting structure with current refinement methods is more difficult with increased resolution, because the used refinement potentials become more rugged the higher the resolution. Currently used refinement potentials are defined by empirically chosen measures of similarity between a calculated cryo-EM density and the given experimental map, e.g. cross-correlation or absolute distance.Here, we present a new refinement potential that is based on a statistical physics model of the cryo-EM measuring and reconstruction process using Bayesian statistics. Our method contains previously previously developed algorithms as limiting cases.The minima of the refinement potential and its shape both influence the efficiency of the refinement algorithm; a smoother energy landscape allows a more efficient exploration of the minima, i.e. fitting structures, in this landscape. Compared to earlier methods, our refinement energy landscape is smoother, allowing more efficient sampling of the energy landscape. Further, our refinement protocol provides an appropriate refinement force constant and takes into account the thermal fluctuation of the atoms. Additionally, our algorithm allows us to generate molecular dynamics ensembles that represent the simultaneous input from multiple cryo-EM maps.