Macromolecular Structure Determination Research Articles

Unveiling success determinants for AMB-assisted phase expansion of fusion proteins in ARP/wARP

Fusion proteins (FPs) are frequently utilized as a biotechnological tool in the determination of macromolecular structures using X-ray methods. Here, we explore the use of different protein tags in various FP, to obtain initial phases by using them in a partial molecular replacement (MR) and constructing the remaining FP structure with ARP/wARP. Usually, the tag is removed prior to crystallization, however leaving the tag on may facilitate crystal formation, and structural determination by expanding phases from known to unknown segments of the complex. In this study, the Protein Data Bank was mined for an up-to-date list of FPs with the most used protein tags, Maltose Binding Protein (MBP), Green Fluorescent Protein (GFP), Thioredoxin (TRX), Glutathione transferase (GST) and the Small Ubiquitin-like Modifier Protein (SUMO). Partial MR using the protein tag, followed by automatic model building, was tested on a subset of 116 FP. The efficiency of this method was analyzed and factors that influence the coordinate construction of a substantial portions of the fused protein were identified. Using MBP, GFP, and SUMO as phase generators it was possible to build at least 75 % of the protein of interest in 36 of the 116 cases tested. Our results reveal that tag selection has a significant impact; tags with greater structural stability, such as GFP, increase the success rate. Further statistical analysis identifies that resolution, Wilson B factor, solvent percentage, completeness, multiplicity, protein tag percentage in the FP (considering amino acids), and the linker length play pivotal roles using our approach. In cases where a structural homologous is absent, this method merits inclusion in the toolkit of protein crystallographers.

The time revolution in macromolecular crystallography.

Macromolecular crystallography has historically provided the atomic structures of proteins fundamental to cellular functions. However, the advent of cryo-electron microscopy for structure determination of large and increasingly smaller and flexible proteins signaled a paradigm shift in structural biology. The extensive structural and sequence data from crystallography and advanced sequencing techniques have been pivotal for training computational models for accurate structure prediction, unveiling the general fold of most proteins. Here, we present a perspective on the rise of time-resolved crystallography as the new frontier of macromolecular structure determination. We trace the evolution from the pioneering time-resolved crystallography methods to modern serial crystallography, highlighting the synergy between rapid detection technologies and state-of-the-art x-ray sources. These innovations are redefining our exploration of protein dynamics, with high-resolution crystallography uniquely positioned to elucidate rapid dynamic processes at ambient temperatures, thus deepening our understanding of protein functionality. We propose that the integration of dynamic structural data with machine learning advancements will unlock predictive capabilities for protein kinetics, revolutionizing dynamics like macromolecular crystallography revolutionized structural biology.

Challenges and opportunities in macromolecular structure determination.

Challenges and opportunities in macromolecular structure determination.

Particle-Picking in Cryo-Electron Microscopy Images with Online Resources

Aims: Use deep learning online resources to identify and pick single particle views in micrographs to enable high quality three-dimensional reconstruction for macromolecular structure determination. Study Design: Using the keyhole limpet hemocyanin dataset, a public cryo electron microscopy (cryo-EM) dataset containing two dimensional projections of the particles in two views (top and side) in several orientations, and a recent deep learning algorithm made available in a GitHub repository, we design the procedure to pick both views with high degree of confidence, using only online resources and running in a standard laptop. Methodology: The defocus images are subject to a pre-processing stage to increase its contrast and ameliorate its radiometric range. This is followed by a training stage, that needs a few images annotated with examples of both views of interest – top and side view – identified using user-friendly tools available online. The annotated subset of images is divided for train and validation purposes, and the algorithm runs to produce a set of weights, that can be used for inference in any other similar image, locating all the instances of the particles in both views in seconds. Results: From the 57 images used to evaluate the performance of the algorithm, 63% had both precision and recall better than 90%. The global precision on the test dataset was 91.6% and recall 98.1%. Considering each view separately, top views detections attain a precision of 91.7%, with 100% for recall; side views recall remains on 96.7%, while precision attain 91.5%. Conclusion: Deep learning methods are a promising tool for extracting large amounts of single particle views as needed for quality 3D structure reconstruction, as they can be implemented with minimal computer skills and trained to achieve state-of-the-art automatic discrimination, as described in this study.

Elucidating polymorphs of crystal structures by intensity-based hierarchical clustering analysis of multiple diffraction data sets.

In macromolecular structure determination using X-ray diffraction from multiple crystals, the presence of different structures (structural polymorphs) necessitates the classification of the diffraction data for appropriate structural analysis. Hierarchical clustering analysis (HCA) is a promising technique that has so far been used to extract isomorphous data, mainly for single-structure determination. Although in principle the use of HCA can be extended to detect polymorphs, the absence of a reference to define the threshold used to group the isomorphous data sets (the `isomorphic threshold') poses a challenge. Here, unit-cell-based and intensity-based HCAs have been applied to data sets for apo trypsin and inhibitor-bound trypsin that were mixed post data acquisition to investigate the efficacy of HCA in classifying polymorphous data sets. Single-step intensity-based HCA successfully classified polymorphs with a certain `isomorphic threshold'. In data sets for several samples containing an unknown degree of structural heterogeneity, polymorphs could be identified by intensity-based HCA using the suggested `isomorphic threshold'. Polymorphs were also detected in single crystals using data collected using the continuous helical scheme. These findings are expected to facilitate the determination of multiple structural snapshots by exploiting automated data collection and analysis.

Cryogenic electron ptychographic single particle analysis with wide bandwidth information transfer

Advances in cryogenic transmission electron microscopy have revolutionised the determination of many macromolecular structures at atomic or near-atomic resolution. This method is based on conventional defocused phase contrast imaging. However, it has limitations of weaker contrast for small biological molecules embedded in vitreous ice, in comparison with cryo-ptychography, which shows increased contrast. Here we report a single-particle analysis based on the use of ptychographic reconstruction data, demonstrating that three dimensional reconstructions with a wide information transfer bandwidth can be recovered by Fourier domain synthesis. Our work suggests future applications in otherwise challenging single particle analyses, including small macromolecules and heterogeneous or flexible particles. In addition structure determination in situ within cells without the requirement for protein purification and expression may be possible.

Continuous Validation Across Macromolecular Structure Determination Process.

The overall quality of the experimentally determined structures contained in the PDB is exceptionally high, mainly due to the continuous improvement of model building and structural validation programs. Improving reproducibility on a large scale requires expanding the concept of validation in structural biology and all other disciplines to include a broader framework that encompasses the entire project. A successful approach to science requires diligent attention to detail and a focus on the future. An earnest commitment to data availability and reuse is essential for scientific progress, be that by human minds or artificial intelligence.

Errors in structural biology are not the exception.

During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future.

Accelerating crystal structure determination with iterative AlphaFold prediction.

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

Cryogenic Fluorescence Spectroscopy of Ionic Fluorones in Gaseous and Condensed Phases: New Light on Their Intrinsic Photophysics.

Fluorescence spectroscopy of gas-phase ions generated through electrospray ionization is an emerging technique able to probe intrinsic molecular photophysics directly without perturbations from solvent interactions. While there is ample scope for the ongoing development of gas-phase fluorescence techniques, the recent expansion into low-temperature operating conditions accesses a wealth of data on intrinsic fluorophore photophysics, offering enhanced spectral resolution compared with room-temperature measurements, without matrix effects hindering the excited-state dynamics. This perspective reviews current progress on understanding the photophysics of anionic fluorone dyes, which exhibit an unusually large Stokes shift in the gas phase, and discusses how comparison of gas- and condensed-phase fluorescence spectra can fingerprint structural dynamics. The capacity for temperature-dependent measurements of both fluorescence emission and excitation spectra helps establish the foundation for the use of fluorone dyes as fluorescent tags in macromolecular structure determination. We suggest ideas for technique development.

A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0.

We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe the approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt-series alignments, beam-induced motions of the particles throughout the tilt-series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, particularly for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.

CCP4 Cloud for structure determination and project management in macromolecular crystallography.

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4.

Serial femtosecond crystallography

With the advent of X-ray Free Electron Lasers (XFELs), new, high-throughput serial crystallography techniques for macromolecular structure determination have emerged. Serial femtosecond crystallography (SFX) and related methods provide possibilities beyond canonical, single-crystal rotation crystallography by mitigating radiation damage and allowing time-resolved studies with unprecedented temporal resolution. This primer aims to assist structural biology groups with little or no experience in serial crystallography planning and carrying out a successful SFX experiment. It discusses the background of serial crystallography and its possibilities. Microcrystal growth and characterization methods are discussed, alongside techniques for sample delivery and data processing. Moreover, it gives practical tips for preparing an experiment, what to consider and do during a beamtime and how to conduct the final data analysis. Finally, the Primer looks at various applications of SFX, including structure determination of membrane proteins, investigation of radiation damage-prone systems and time-resolved studies.

Curiosity: a tool for exploring density features in crystallographic and cryoEM maps

A key component of macromolecular structure determination from crystallographic or cryoEM density maps is the discovery and repair of modeling errors. In the case of chemically and physically unreasonable arrangements of atoms, we are informed by the clashscore, Ramachandran plots, bond distance/angle/torsion/chirality/planarity outliers, and so forth. Some tools for identifying chemically unlikely but biologically important structural motifs such as Hoogsteen base pairing have also been developed.

Detecting ice artefacts in processed macromolecular diffraction data with machine learning.

Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software.

Data- and diversity-driven development of a Shotgun crystallization screen using the Protein Data Bank.

Protein crystallization has for decades been a critical and restrictive step in macromolecular structure determination via X-ray diffraction. Crystallization typically involves a multi-stage exploration of the available chemical space, beginning with an initial sampling (screening) followed by iterative refinement (optimization). Effective screening is important for reducing the number of optimization rounds required, reducing the cost and time required to determine a structure. Here, an initial screen (Shotgun II) derived from analysis of the up-to-date Protein Data Bank (PDB) is proposed and compared with the previously derived (2014) Shotgun I screen. In an update to that analysis, it is clarified that the Shotgun approach entails finding the crystallization conditions that cover the most diverse space of proteins by sequence found in the PDB, which can be mapped to the well known maximum coverage problem in computer science. With this realization, it was possible to apply a more effective algorithm for selecting conditions. In-house data demonstrate that compared with alternatives, the Shotgun I screen has been remarkably successful over the seven years that it has been in use, indicating that Shotgun II is also likely to be a highly effective screen.

Pink-beam serial femtosecond crystallography for accurate structure-factor determination at an X-ray free-electron laser.

Serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs) enables essentially radiation-damage-free macromolecular structure determination using microcrystals that are too small for synchrotron studies. However, SFX experiments often require large amounts of sample in order to collect highly redundant data where some of the many stochastic errors can be averaged out to determine accurate structure-factor amplitudes. In this work, the capability of the Swiss X-ray free-electron laser (SwissFEL) was used to generate large-bandwidth X-ray pulses [Δλ/λ = 2.2% full width at half-maximum (FWHM)], which were applied in SFX with the aim of improving the partiality of Bragg spots and thus decreasing sample consumption while maintaining the data quality. Sensitive data-quality indicators such as anomalous signal from native thaumatin micro-crystals and de novo phasing results were used to quantify the benefits of using pink X-ray pulses to obtain accurate structure-factor amplitudes. Compared with data measured using the same setup but using X-ray pulses with typical quasi-monochromatic XFEL bandwidth (Δλ/λ = 0.17% FWHM), up to fourfold reduction in the number of indexed diffraction patterns required to obtain similar data quality was achieved. This novel approach, pink-beam SFX, facilitates the yet underutilized de novo structure determination of challenging proteins at XFELs, thereby opening the door to more scientific breakthroughs.

Data- and diversity-driven development of a Shotgun crystallisation screen using the Protein Data Bank

Protein crystallisation has for decades been a critical and restrictive step in macro-molecular structure determination via X-ray diffraction. Crystallisation typically involves a multi-stage exploration of the available chemical space, beginning with an initial sampling (screening) followed by iterative refinement (optimisation). Effective screening is important for reducing the number of optimisation rounds required, reducing the cost and time required to determine a structure. Here, we propose an initial screen (Shotgun II) derived from analysis of the up-to-date Protein Data Bank (PDB) and compare it with the previously derived (2014) Shotgun I screen. In an update to that analysis, we clarify that the Shotgun approach entails finding the crystallisation conditions which cover the most diverse space of proteins by sequence found in the PDB - which can be mapped to the well known Maximum Coverage problem in computer science. With this realisation we are able to apply a more effective algorithm for selecting conditions, such that the Shotgun II screen outperforms the Shotgun I screen both in protein coverage and quantity of data input. Our data demonstrates that the Shotgun I screen, compared with alternatives, has been remarkably successful over the seven years it has been in use, indicating that Shotgun II is likely to be a highly effective screen.

Cryo-EM: A new dawn in thyroid biology

The thyroid gland accumulates the rare dietary element iodine and incorporates it into iodinated thyroid hormones, utilising several tightly regulated reactions and molecular mechanisms. Thyroid hormones are essential in vertebrates and play a central role in many biological processes, such as development, thermogenesis and growth. The control of these functions is exerted through the binding of hormones to nuclear thyroid hormone receptors that rule the transcription of numerous metabolic genes. Over the last 50 years, thyroid biology has been studied extensively at the cellular and organismal levels, revealing its multiple clinical implications, yet, a complete molecular understanding is still lacking. This includes the atomic structures of crucial pathway components that would be needed to elucidate molecular mechanisms. Here we review the currently known protein structures involved in thyroid hormone synthesis, regulation, transport, and actions. We also highlight targets for future investigations that will significantly benefit from recent advances in macromolecular structure determination by electron cryo-microscopy (cryo-EM). As an example, we demonstrate how cryo-EM was crucial to obtain the structure of the large thyroid hormone precursor protein, thyroglobulin. We discuss modern cryo-EM compared to other structure determination methods and how an integrated structural and cell biological approach will help filling the molecular knowledge gap in our understanding of thyroid hormone metabolism. Together with clinical, cellular and high-throughput 'omics' studies, atomic structures of thyroid components will provide an important framework to map disease mutations and to interpret and predict thyroid phenotypes.

Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins.

Crystallography and NMR system (CNS) is currently a widely used method for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to popular CNS- and fragment-based approaches and energy-minimization protocols, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS.