FSC-Q: a CryoEM map-to-atomic model quality validation based on the local Fourier shell correlation

Bacterial selenocysteine synthase structure revealed by single-particle cryoEM

The 21st amino acid, selenocysteine (Sec), is synthesized on its dedicated transfer RNA (tRNASec). In bacteria, Sec is synthesized from Ser-tRNA[Ser]Sec by Selenocysteine Synthase (SelA), which is a pivotal enzyme in the biosynthesis of Sec. The structural characterization of bacterial SelA is of paramount importance to decipher its catalytic mechanism and its role in the regulation of the Sec-synthesis pathway. Here, we present a comprehensive single-particle cryo-electron microscopy (SPA cryoEM) structure of the bacterial SelA with an overall resolution of 2.69 Å. Using recombinant Escherichia coli SelA, we purified and prepared samples for single-particle cryoEM. The structural insights from SelA, combined with previous in vivo and in vitro knowledge, underscore the indispensable role of decamerization in SelA's function. Moreover, our structural analysis corroborates previous results that show that SelA adopts a pentamer of dimers configuration, and the active site architecture, substrate binding pocket, and key K295 catalytic residue are identified and described in detail. The differences in protein architecture and substrate coordination between the bacterial enzyme and its counterparts offer compelling structural evidence supporting the independent molecular evolution of the bacterial and archaea/eukarya Ser-Sec biosynthesis present in the natural world.

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on map-model fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Q-score. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at http://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

Advancing cryo-electron microscopy data analysis through accelerated simulation-based flexible fitting approaches

Flexible fitting based on molecular dynamics simulation is a technique for structure modeling from cryo-EM data. It has been utilized for nearly two decades, and while cryo-EM resolution has improved significantly, it remains a powerful approach that can provide structural and dynamical insights that are not directly accessible from experimental data alone. Molecular dynamics simulations provide a means to extract atomistic details of conformational changes that are encoded in cryo-EM data and can also assist in improving the quality of structural models. Additionally, molecular dynamics simulations enable the characterization of conformational heterogeneity in cryo-EM data. We will summarize the advancements made in these techniques and highlight recent developments in this field.

Residue-level error detection in cryoelectron microscopy models

Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. As modelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC's ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.

Development and validation of a newly developed nomogram for predicting the risk of deep vein thrombosis after surgery for lower limb fractures in elderly patients

Background

Prevention of deep vein thrombosis (DVT) is indispensable in the treatment of lower limb fractures during the perioperative period. This study aimed to develop and validate a novel model for predicting the risk of DVT in elderly patients after orthopedic surgeries for lower limb fractures.

Methods

This observational study included 576 elderly patients with lower limb fractures who were surgically treated from January 2016 to December 2018. Eleven items affecting DVT were optimized by least absolute shrinkage and selection operator regression analysis. Multivariable logistic regression analysis was performed to construct a predictive model incorporating the selected features. C-index was applied to evaluate the discrimination. Decision curve analysis was employed to determine the clinical effectiveness of this model and calibration plot was applied to evaluate the calibration of this nomogram. The internal validation of this model was assessed by bootstrapping validation.

Results

Predictive factors that affected the rate of DVT in this model included smoking, time from injury to surgery, operation time, blood transfusion, hip replacement arthroplasty, and D-dimer level after operation. The nomogram showed significant discrimination with a C-index of 0.919 (95% confidence interval: 0.893-0.946) and good calibration. Acceptable C-index value could still be reached in the interval validation. Decision curve analysis indicated that the DVT risk nomogram was useful within all possibility threshold.

Conclusion

This newly developed nomogram could be used to predict the risk of DVT in elderly patients with lower limb fractures during the perioperative period.

Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å-1, and present the results in a more interpretable form.

Using deep-learning predictions of inter-residue distances for model validation

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

Image processing tools for the validation of CryoEM maps

The number of maps deposited in public databases (Electron Microscopy Data Bank, EMDB) determined by cryo-electron microscopy has quickly grown in recent years. With this rapid growth, it is critical to guarantee their quality. So far, map validation has primarily focused on the agreement between maps and models. From the image processing perspective, the validation has been mostly restricted to using two half-maps and the measurement of their internal consistency. In this article, we suggest that map validation can be taken much further from the point of view of image processing if 2D classes, particles, angles, coordinates, defoci, and micrographs are also provided. We present a progressive validation scheme that qualifies a result validation status from 0 to 5 and offers three optional qualifiers (A, W, and O) that can be added. The simplest validation state is 0, while the most complete would be 5AWO. This scheme has been implemented in a website https://biocomp.cnb.csic.es/EMValidationService/ to which reconstructed maps and their ESI can be uploaded.

Overview and applications of map and model validation tools in the CCP-EM software suite

Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.

Juxtaposition of Bub1 and Cdc20 on phosphorylated Mad1 during catalytic mitotic checkpoint complex assembly

In response to improper kinetochore-microtubule attachments in mitosis, the spindle assembly checkpoint (SAC) assembles the mitotic checkpoint complex (MCC) to inhibit the anaphase-promoting complex/cyclosome, thereby delaying entry into anaphase. The MCC comprises Mad2:Cdc20:BubR1:Bub3. Its assembly is catalysed by unattached kinetochores on a Mad1:Mad2 platform. Mad1-bound closed-Mad2 (C-Mad2) recruits open-Mad2 (O-Mad2) through self-dimerization. This interaction, combined with Mps1 kinase-mediated phosphorylation of Bub1 and Mad1, accelerates MCC assembly, in a process that requires O-Mad2 to C-Mad2 conversion and concomitant binding of Cdc20. How Mad1 phosphorylation catalyses MCC assembly is poorly understood. Here, we characterized Mps1 phosphorylation of Mad1 and obtained structural insights into a phosphorylation-specific Mad1:Cdc20 interaction. This interaction, together with the Mps1-phosphorylation dependent association of Bub1 and Mad1, generates a tripartite assembly of Bub1 and Cdc20 onto the C-terminal domain of Mad1 (Mad1CTD). We additionally identify flexibility of Mad1:Mad2 that suggests how the Cdc20:Mad1CTD interaction brings the Mad2-interacting motif (MIM) of Cdc20 near O-Mad2. Thus, Mps1-dependent formation of the MCC-assembly scaffold functions to position and orient Cdc20 MIM near O-Mad2, thereby catalysing formation of C-Mad2:Cdc20.

Higher resolution in cryo-EM by the combination of macromolecular prior knowledge and image-processing tools

Single-particle cryo-electron microscopy has become a powerful technique for the 3D structure determination of biological molecules. The last decade has seen an astonishing development of both hardware and software, and an exponential growth of new structures obtained at medium-high resolution. However, the knowledge accumulated in this field over the years has hardly been utilized as feedback in the reconstruction of new structures. In this context, this article explores the use of the deep-learning approach deepEMhancer as a regularizer in the RELION refinement process. deepEMhancer introduces prior information derived from macromolecular structures, and contributes to noise reduction and signal enhancement, as well as a higher degree of isotropy. These features have a direct effect on image alignment and reduction of overfitting during iterative refinement. The advantages of this combination are demonstrated for several membrane proteins, for which it is especially useful because of their high disorder and flexibility.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Core and rod structures of a thermophilic cyanobacterial light-harvesting phycobilisome

Cyanobacteria, glaucophytes, and rhodophytes utilize giant, light-harvesting phycobilisomes (PBSs) for capturing solar energy and conveying it to photosynthetic reaction centers. PBSs are compositionally and structurally diverse, and exceedingly complex, all of which pose a challenge for a comprehensive understanding of their function. To date, three detailed architectures of PBSs by cryo-electron microscopy (cryo-EM) have been described: a hemiellipsoidal type, a block-type from rhodophytes, and a cyanobacterial hemidiscoidal-type. Here, we report cryo-EM structures of a pentacylindrical allophycocyanin core and phycocyanin-containing rod of a thermophilic cyanobacterial hemidiscoidal PBS. The structures define the spatial arrangement of protein subunits and chromophores, crucial for deciphering the energy transfer mechanism. They reveal how the pentacylindrical core is formed, identify key interactions between linker proteins and the bilin chromophores, and indicate pathways for unidirectional energy transfer.

Phycobilisome (PBS) absorbs solar energy and transfer the energy to photosynthetic membrane proteins. In this study, the structures of the pentacylindrical core and rod in PBS from a thermophilic cyanobacterium by cryo-electron microscopy.

Atomic model validation using the CCP-EM software suite

Recently, there has been a dramatic improvement in the quality and quantity of data derived using cryogenic electron microscopy (cryo-EM). This is also associated with a large increase in the number of atomic models built. Although the best resolutions that are achievable are improving, often the local resolution is variable, and a significant majority of data are still resolved at resolutions worse than 3 Å. Model building and refinement is often challenging at these resolutions, and hence atomic model validation becomes even more crucial to identify less reliable regions of the model. Here, a graphical user interface for atomic model validation, implemented in the CCP-EM software suite, is presented. It is aimed to develop this into a platform where users can access multiple complementary validation metrics that work across a range of resolutions and obtain a summary of evaluations. Based on the validation estimates from atomic models associated with cryo-EM structures from SARS-CoV-2, it was observed that models typically favor adopting the most common conformations over fitting the observations when compared with the model agreement with data. At low resolutions, the stereochemical quality may be favored over data fit, but care should be taken to ensure that the model agrees with the data in terms of resolvable features. It is demonstrated that further re-refinement can lead to improvement of the agreement with data without the loss of geometric quality. This also highlights the need for improved resolution-dependent weight optimization in model refinement and an effective test for overfitting that would help to guide the refinement process.

EMDA: A Python package for Electron Microscopy Data Analysis

An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying map-model differences and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA’s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out first to bring both maps into the same coordinate frame and then to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of adjusting the map magnification in structural comparison studies.

Advances in Xmipp for Cryo-Electron Microscopy: From Xmipp to Scipion

Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.

Cryo-EM density maps adjustment for subtraction, consensus and sharpening

Electron cryomicroscopy (cryo-EM) has emerged as a powerful structural biology instrument to solve near-atomic three-dimensional structures. Despite the fast growth in the number of density maps generated from cryo-EM data, comparison tools among these reconstructions are still lacking. Current proposals to compare cryo-EM data derived volumes perform map subtraction based on adjustment of each volume grey level to the same scale. We present here a more sophisticated way of adjusting the volumes before comparing, which implies adjustment of grey level scale and spectrum energy, but keeping phases intact inside a mask and imposing the results to be strictly positive. The adjustment that we propose leaves the volumes in the same numeric frame, allowing to perform operations among the adjusted volumes in a more reliable way. This adjustment can be a preliminary step for several applications such as comparison through subtraction, map sharpening, or combination of volumes through a consensus that selects the best resolved parts of each input map. Our development might also be used as a sharpening method using an atomic model as a reference. We illustrate the applicability of this algorithm with the reconstructions derived of several experimental examples. This algorithm is implemented in Xmipp software package and its applications are user-friendly accessible through the cryo-EM image processing framework Scipion.

Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

Cryo-EM Map-Based Model Validation Using the False Discovery Rate Approach

Significant technological developments and increasing scientific interest in cryogenic electron microscopy (cryo-EM) has resulted in a rapid increase in the amount of data generated by these experiments and the derived atomic models. Robust measures for the validation of 3D reconstructions and atomic models are essential for appropriate interpretation of the data. The resolution of data and availability of software tools that work across a range of resolutions often limit the quality of derived models. Hence, the final atomic model is often incomplete or contains regions where atomic positions are less reliable or incorrectly built. Extensive manual pruning and local adjustments or rebuilding are usually required to address these issues. The presented research introduces a software tool for the validation of the backbone trace of atomic models built in the cryo-EM density maps. In this study, we use the false discovery rate analysis, which can be used to segregate molecular signals from the background. Each atomic position in the model can be associated with an FDR backbone validation score, which can be used to identify potential mistraced residues. We demonstrate that the proposed validation score is complementary to existing validation metrics and is useful especially in cases where the model is built in the maps having varying local resolution. We also discuss the application of the score for automated pruning of atomic models built ab-initio during the iterative model building process in Buccaneer. We have implemented this score in the CCP-EM software suite.