Structure Of Biological Macromolecules Research Articles

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning

To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their technical limitations. To overcome the limitations, we develop DeepETPicker, a deep learning model for fast and accurate picking of particles from cryo-electron tomograms. Training of DeepETPicker requires only weak supervision with low numbers of simplified labels, reducing the burden of manual annotation. The simplified labels combined with the customized and lightweight model architecture of DeepETPicker and accelerated pooling enable substantial performance improvement. When tested on simulated and real tomograms, DeepETPicker outperforms the competing state-of-the-art methods by achieving the highest overall accuracy and speed, which translate into higher authenticity and coordinates accuracy of picked particles and higher resolutions of final reconstruction maps. DeepETPicker is provided in open source with a user-friendly interface to support cryo-electron tomography in situ.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Clustering of bacteria by the triplet composition of 5S RNA

The relationship between the structure of nucleotide sequences, the function they encode, and the taxonomy of the host is an important subject of study for molecular biologists and bioinformaticians, as well as specialists in processing big data arrays. There are various approaches in this direction; one of them is connected with the study of the relationship between the structure of biological macromolecules (specifically, bacterial ribosomal RNA molecules) and the taxonomy of their carriers. For these organisms, 16S RNA sequences are a classic subject of study. However, of no less interest is the study of the relationship be-tween structure and taxonomy for other sequences. In the framework of this work, we studied the relationship between structure and taxonomy using bacterial 5S RNA sequences as an example.

Moment-based metrics for molecules computable from cryogenic electron microscopy images.

Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam's method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.

X-Ray Crystallography for Macromolecular Complexes.

X-ray crystallography has for most of the last century been the standard technique to determine the high-resolution structure of biological macromolecules, including multi-subunit protein-protein and protein-nucleic acids as large as the ribosome and viruses. As such, the successful application of X-ray crystallography to many biological problems revolutionized biology and biomedicine by solving the structures of small molecules and vitamins, peptides and proteins, DNA and RNA molecules, and many complexes-affording a detailed knowledge of the structures that clarified biological and chemical mechanisms, conformational changes, interactions, catalysis and the biological processes underlying DNA replication, translation, and protein synthesis. Now reaching well into the first quarter of the twenty-first century, X-ray crystallography shares the structural biology stage with cryo-electron microscopy and other innovative structure determination methods, as relevant and central to our understanding of biological function and structure as ever. In this chapter, we provide an overview of modern X-ray crystallography and how it interfaces with other mainstream structural biology techniques, with an emphasis on macromolecular complexes.

A minority of final stacks yields superior amplitude in single-particle cryo-EM

Cryogenic electron microscopy (cryo-EM) is widely used to determine near-atomic resolution structures of biological macromolecules. Due to the low signal-to-noise ratio, cryo-EM relies on averaging many images. However, a crucial question in the field of cryo-EM remains unanswered: how close can we get to the minimum number of particles required to reach a specific resolution in practice? The absence of an answer to this question has impeded progress in understanding sample behavior and the performance of sample preparation methods. To address this issue, we develop an iterative particle sorting and/or sieving method called CryoSieve. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks. The minority of particles remaining in the final stacks yield superior high-resolution amplitude in reconstructed density maps. For some datasets, the size of the finest subset approaches the theoretical limit.

AutoEMage: automatic data transfer, preprocessing, real-time display and monitoring in cryo-EM

Cryo-electron microscopy (cryo-EM), especially single-particle analysis, has become a powerful technique for visualizing the structure of biological macromolecules at high resolution. However, data acquisition in cryo-EM is time consuming because it requires the collection of thousands of images to achieve a high-quality reconstruction. Real-time preprocessing and display of the images can greatly enhance the efficiency and quality of data collection. This study presents AutoEMage, a new open-source software package that automates data transfer, preprocessing and real-time display in cryo-EM experiments. AutoEMage also includes a real-time data monitoring system that alerts users to issues with their data, allowing them to take corrective actions accordingly. The software is equipped with an easy-to-use graphical user interface that provides seamless data screening and real-time feedback on data quality and microscope status.

A new algorithm for particle weighted subtraction to decrease signals from unwanted components in single particle analysis

Single particle analysis (SPA) in cryo-electron microscopy (cryo-EM) is highly used to obtain the near-atomic structure of biological macromolecules. The current methods allow users to produce high-resolution maps from many samples. However, there are still challenging cases that require extra processing to obtain high resolution. This is the case when the macromolecule of the sample is composed of different components and we want to focus just on one of them. For example, if the macromolecule is composed of several flexible subunits and we are interested in a specific one, if it is embedded in a viral capsid environment, or if it has additional components to stabilize it, such as nanodiscs. The signal from these components, which in principle we are not interested in, can be removed from the particles using a projection subtraction method. Currently, there are two projection subtraction methods used in practice and both have some limitations. In fact, after evaluating their results, we consider that the problem is still open to new solutions, as they do not fully remove the signal of the components that are not of interest. Our aim is to develop a new and more precise projection subtraction method, improving the performance of state-of-the-art methods. We tested our algorithm with data from public databases and an in–house data set. In this work, we show that the performance of our algorithm improves the results obtained by others, including the localization of small ligands, such as drugs, whose binding location is unknown a priori.

Announcing the launch of Protein Data Bank China as an Associate Member of the Worldwide Protein Data Bank Partnership.

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.

Ensemble Reweighting Using Cryo-EM Particle Images.

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.

A strategy combining denoising and cryo-EM single particle analysis

In cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), high-resolution three-dimensional structures of biological macromolecules are determined by iteratively aligning and averaging a large number of two-dimensional projections of molecules. Since the correlation measures are sensitive to the signal-to-noise ratio, various parameter estimation steps in SPA will be disturbed by the high-intensity noise in cryo-EM. However, denoising algorithms tend to damage high frequencies and suppress mid- and high-frequency contrast of micrographs, which exactly the precise parameter estimation relies on, therefore, limiting their application in SPA. In this study, we suggest combining a cryo-EM image processing pipeline with denoising and maximizing the signal's contribution in various parameter estimation steps. To solve the inherent flaws of denoising algorithms, we design an algorithm named MScale to correct the amplitude distortion caused by denoising and propose a new orientation determination strategy to compensate for the high-frequency loss. In the experiments on several real datasets, the denoised particles are successfully applied in the class assignment estimation and orientation determination tasks, ultimately enhancing the quality of biomacromolecule reconstruction. The case study on classification indicates that our strategy not only improves the resolution of difficult classes (up to 5Å) but also resolves an additional class. In the case study on orientation determination, our strategy improves the resolution of the final reconstructed density map by 0.34Å compared with conventional strategy. The code is available at https://github.com/zhanghui186/Mscale.

The highly efficient protein crystallography beamline TLS 13B1 at the National Synchrotron Radiation Research Center

AbstractSynchrotron protein crystallography (PX) is one of the most powerful and dominant tools for determining the three‐dimensional structures of biological macromolecules. At the National Synchrotron Radiation Research Center, TLS 13B1 is the first dedicated energy‐tunable and highly automated PX beamline for efficiently solving de novo protein structures in Taiwan and has been open for users since September 2005. With remote access, user support services, and annual PX training courses over 17 years, the number of user groups at TLS 13B1 has grown from 11 to 139. In total, 1,429 projects were supported, and 2090 experiments were hosted during 67,846 hr of beamtime. This paper describes the photon source, beamline components, experimental station equipment, beamline software, beamline application process, and results from our users.

A REVIEW OF ROLE OF X-RAY DIFFRACTION IN STRUCTURAL ELUCIDATION OF BIOLOGICAL MOLECULES

The importance of structural elucidation via X-ray diffraction (XRD) in drug discovery cannot be overstated. It plays a pivotal role in advancing drug development and design by providing valuable insights into the three-dimensional structures of biological macromolecules, such as proteins and enzymes. This review will investigate the different XRD techniques employed in studying the structures of biological macromolecules and explore its role in drug discovery, including target identification, validation, rational drug design, and lead optimization. The review aims to systematically analyze the strengths and limitations of XRD in revealing the structural details of biological molecules. It will also highlight the contribution of XRD in understanding the structural and mechanism aspects of proteins and enzymes. In conclusion, it will identify the potential areas of growth and development in X-ray diffraction techniques for studying complex biological systems.

Learning to automate cryo-electron microscopy data collection with Ptolemy.

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

Relevance of water in biological membranes

Water stabilizes the structure of biological macromolecules and supramolecular lipid arrangements, controls biochemical activities, and regulates interfacial/intermolecular interactions. In consequence, due to their unique properties, “water” is an essential commodity for all functions in biology and, therefore, to act as a link between membrane and metabolic phenomena. However, current models of biomembranes do not include water as a structural, dynamic and thermodynamic determinant component.In a recent proposal water is incorporated in the membrane structure as a dynamic component and its properties allow to explain the responsiveness of lipid assembles on thermodynamic grounds. In this view, chemical and mechanical stresses acting on the membrane produces water exchange with the surrounding media. Thus, in thermodynamic terms the membrane is an open system. In order to analyze the membrane under this approach, it is considered as composed by a bidimensional solution of hydrated polar head groups immersed in water with solvent properties different than the bulk.This interphase region extends partially into the hydrocarbon region and expands along two–three layers from the head groups. This last possibility is reasonable when considering the cell as a crowded system in which interphases of macromolecules and supramolecular lipid structures can overlap. This would account for propagation phenomena of events between metabolism and cell membrane. The main water property that emerges in this coupling is the cooperativity of hydrogen bonds and proton displacement along them.Water is organized at the interphase region around polar head groups and acyl chains. However, not all of water content in lipids is active in terms of contributing to fundamental functions at different temporal and spatial scales, such as response to different biologically active compounds (bioeffectors) in the environment. The interrelation between the hydration properties at molecular level and the thermodynamic response is a particularly important point to discuss.

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves>10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside>1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

Automatic determination of the handedness of single-particle maps of macromolecules solved by CryoEM

Single-Particle Analysis by Cryo-Electron Microscopy is a well-established technique to elucidate the three-dimensional (3D) structure of biological macromolecules. The orientation of the acquired projection images must be initially estimated without any reference to the final structure. In this step, algorithms may find a mirrored version of all the orientations resulting in a mirrored 3D map. It is as compatible with the acquired images as its unmirrored version from the image processing point of view, only that it is not biologically plausible.In this article, we introduce HaPi (Handedness Pipeline), the first method to automatically determine the hand of electron density maps of macromolecules solved by CryoEM. HaPi is built by training two 3D convolutional neural networks. The first determines α-helices in a map, and the second determines whether the α-helix is left-handed or right-handed. A consensus strategy defines the overall map hand. The pipeline is trained on simulated and experimental data. The handedness can be detected only for maps whose resolution is better than 5 Å. HaPi can identify the hand in 89% of new simulated maps correctly. Moreover, we evaluated all the maps deposited at the Electron Microscopy Data Bank and 11 structures uploaded with the incorrect hand were identified.

Single-particle cryo-EM structures from iDPC–STEM at near-atomic resolution

In electron cryomicroscopy (cryo-EM), molecular images of vitrified biological samples are obtained by conventional transmission microscopy (CTEM) using large underfocuses and subsequently computationally combined into a high-resolution three-dimensional structure. Here, we apply scanning transmission electron microscopy (STEM) using the integrated differential phase contrast mode also known as iDPC–STEM to two cryo-EM test specimens, keyhole limpet hemocyanin (KLH) and tobacco mosaic virus (TMV). The micrographs show complete contrast transfer to high resolution and enable the cryo-EM structure determination for KLH at 6.5 Å resolution, as well as for TMV at 3.5 Å resolution using single-particle reconstruction methods, which share identical features with maps obtained by CTEM of a previously acquired same-sized TMV data set. These data show that STEM imaging in general, and in particular the iDPC–STEM approach, can be applied to vitrified single-particle specimens to determine near-atomic resolution cryo-EM structures of biological macromolecules.

The soft breeze of the cation atmosphere around DNA

Ions are indispensable to living organisms: in addition to their central role in, e.g., conveying neural signals and maintaining homeostasis via osmosis, they also have a strong impact on the structure of biological macromolecules. Remarkably, this structural effect can radically change with the nature of the ion. The importance of the ion’s nature on the interaction with proteins was already recognized in a pioneering work by Hofmeister “On the Doctrine of the Action of Salts” published back in 1888.