Structural Biology Research Articles

Current perspectives of viral hepatitis

Viral hepatitis represents a major danger to public health, and is a globally leading cause of death. The five liver-specific viruses: Hepatitis A virus, hepatitis B virus, hepatitis C virus, hepatitis D virus, and hepatitis E virus, each have their own unique epidemiology, structural biology, transmission, endemic patterns, risk of liver complications, and response to antiviral therapies. There remain few options for treatment, in spite of the increasing prevalence of viral-hepatitis-caused liver disease. Furthermore, chronic viral hepatitis is a leading worldwide cause of both liver-related morbidity and mortality, even though effective treatments are available that could reduce or prevent most patients’ complications. In 2016, the World Health Organization released its plan to eliminate viral hepatitis as a public health threat by the year 2030, along with a discussion of current gaps and prospects for both regional and global eradication of viral hepatitis. Today, treatment is sufficiently able to prevent the disease from reaching advanced phases. However, future therapies must be extremely safe, and should ideally limit the period of treatment necessary. A better understanding of pathogenesis will prove beneficial in the development of potential treatment strategies targeting infections by viral hepatitis. This review aims to summarize the current state of knowledge on each type of viral hepatitis, together with major innovations.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

Stimulation of Biological Structures on the Nanoscale Using Interfaces with Large Built-In Spontaneous Polarizations

Stimulation of Biological Structures on the Nanoscale Using Interfaces with Large Built-In Spontaneous Polarizations

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Multiresolution semantic segmentation of biological structures in digital histopathology.

Semantic segmentation in high-resolution, histopathology whole slide images (WSIs) is an important fundamental task in various pathology applications. Convolutional neural networks (CNN) are the state-of-the-art approach for image segmentation. A patch-based CNN approach is often employed because of the large size of WSIs; however, segmentation performance is sensitive to the field-of-view and resolution of the input patches, and balancing the trade-offs is challenging when there are drastic size variations in the segmented structures. We propose a multiresolution semantic segmentation approach, which is capable of addressing the threefold trade-off between field-of-view, computational efficiency, and spatial resolution in histopathology WSIs. We propose a two-stage multiresolution approach for semantic segmentation of histopathology WSIs of mouse lung tissue and human placenta. In the first stage, we use four different CNNs to extract the contextual information from input patches at four different resolutions. In the second stage, we use another CNN to aggregate the extracted information in the first stage and generate the final segmentation masks. The proposed method reported 95.6%, 92.5%, and 97.1% in our single-class placenta dataset and 97.1%, 87.3%, and 83.3% in our multiclass lung dataset for pixel-wise accuracy, mean Dice similarity coefficient, and mean positive predictive value, respectively. The proposed multiresolution approach demonstrated high accuracy and consistency in the semantic segmentation of biological structures of different sizes in our single-class placenta and multiclass lung histopathology WSI datasets. Our study can potentially be used in automated analysis of biological structures, facilitating the clinical research in histopathology applications.

B-Factor Rescaling for Protein Crystal Structure Analyses

The B-factor, also known as the atomic displacement parameter, is a fundamental metric in crystallography for quantifying the positional flexibility of atoms within crystal lattices. In structural biology, various developments have expanded the use of B-factors beyond conventional crystallographic analysis, allowing for a deeper understanding of protein flexibility, enzyme manipulation, and an improved understanding of molecular dynamics. However, the interpretation of B-factors is complicated by their sensitivity to various experimental and computational factors, necessitating rigorous rescaling methods to ensure meaningful comparisons across different structures. This article provides an in-depth description of rescaling approaches used for B-factors. It includes an examination of several methods for managing conformational disorder and selecting the atom types required for the analysis.

How are Trypanosoma brucei receptors protected from host antibody-mediated attack?

Trypanosoma brucei is the causal agent of African Trypanosomiasis in humans and other animals. It maintains a long-term infection through an antigenic variation based population survival strategy. To proliferate in a mammal, T. brucei acquires iron and haem through the receptor mediated uptake of host transferrin and haptoglobin-hemoglobin respectively. The receptors are exposed to host antibodies but this does not lead to clearance of the infection. Here we discuss how the trypanosome avoids this fate in the context of recent findings on the structure and cell biology of the receptors.

Concept-based Extension of SKOS Defense Controlled Vocabulary: Techniques and Implications

A controlled vocabulary is a set of terms that are utilized to represent knowledge in a domain. In the domain of defense, the use of terms such as “command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR)”, “armored personnel carriers (APC)”, or “biological and bioinspired structures for multispectral surveillance” denote the core competencies of domain experts and the depth of diverse knowledge. This paper describes the second phase of the pilot project to create a defense-related controlled vocabulary with a focus on the Russo-Ukraine conflict. Applications for data annotation, SKOS hierarchical vocabulary development, and vocabulary quality analysis are used to identify terms in text, express identified terms in military press releases as SKOS vocabulary, and assess its structure. The preliminary vocabulary is extended by 173 concepts. The quality of the vocabulary is validated against a SKOS checker of twenty-four criteria.

Applying 3D ED/MicroED workflows toward the next frontiers.

We report on the latest advancements in Microcrystal Electron Diffraction (3D ED/MicroED), as discussed during a symposium at the National Center for CryoEM Access and Training housed at the New York Structural Biology Center. This snapshot describes cutting-edge developments in various facets of the field and identifies potential avenues for continued progress. Key sections discuss instrumentation access, research applications for small molecules and biomacromolecules, data collection hardware and software, data reduction software, and finally reporting and validation. 3D ED/MicroED is still early in its wide adoption by the structural science community with ample opportunities for expansion, growth, and innovation.

UPF1 helicase orchestrates mutually exclusive interactions with the SMG6 endonuclease and UPF2.

Nonsense-mediated mRNA decay (NMD) is a conserved co-translational mRNA surveillance and turnover pathway across eukaryotes. NMD has a central role in degrading defective mRNAs and also regulates the stability of a significant portion of the transcriptome. The pathway is organized around UPF1, an RNA helicase that can interact with several NMD-specific factors. In human cells, degradation of the targeted mRNAs begins with a cleavage event that requires the recruitment of the SMG6 endonuclease to UPF1. Previous studies have identified functional links between SMG6 and UPF1, but the underlying molecular mechanisms have remained elusive. Here, we used mass spectrometry, structural biology and biochemical approaches to identify and characterize a conserved short linear motif in SMG6 that interacts with the cysteine/histidine-rich (CH) domain of UPF1. Unexpectedly, we found that the UPF1-SMG6 interaction is precluded when the UPF1 CH domain is engaged with another NMD factor, UPF2. Based on cryo-EM data, we propose that the formation of distinct SMG6-containing and UPF2-containing NMD complexes may be dictated by different conformational states connected to the RNA-binding status of UPF1. Our findings rationalize a key event in metazoan NMD and advance our understanding of mechanisms regulating activity and guiding substrate recognition by the SMG6 endonuclease.

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.

Network pharmacology speaking to ethnopharmacology: new data on an ancient remedy

Network pharmacology as a “green approach”, predicting metabolite behaviours chemically and biologically and guid¬ing biological experimental design, is a new strategy aiming to uncover the mechanism of action of natural products as drug candidates. It provides a powerful way to identify novel mechanisms of natural products with potential thera¬peutic effects. This approach has emerged as a powerful tool to overcome the limitations of traditional methods, such as the ability to predict the adverse effects of a drug and the likelihood of failure during clinical trials, by applying systems biology principles to the field of pharmacology. This method combines the multi-omics dataset, computer modeling, and chemical biology so as to reveal pharmaceutical actions and guide drug discovery. Therefore, computer-aided drug design combined with network pharmacology can be viewed as a novel in silico screening ap¬proach to drug discovery, by utilising chemoinformatics, bioinformatics, structure biology, and chemical biology. This strategy includes target-based virtual screening - molecular docking, ligand similarity-based virtual screening, and inverse screening (Inver-dock), providing a powerful tool for target identification of drug candidates, multitarget dis¬covery, and natural bioactive product profiling. It can also be used for selectivity profiling of drugs, drug repositioning, safety profiling, and metabolism profiling prediction (ADMET).

Build and operation of a custom 3D, multicolor, single-molecule localization microscope.

Single-molecule localization microscopy (SMLM) enables imaging scientists to visualize biological structures with unprecedented resolution. Particularly powerful implementations of SMLM are capable of three-dimensional, multicolor and high-throughput imaging and can yield key biological insights. However, widespread access to these technologies is limited, primarily by the cost of commercial options and complexity of de novo development of custom systems. Here we provide a comprehensive guide for interested researchers who wish to establish a high-end, custom-built SMLM setup in their laboratories. We detail the initial configuration and subsequent assembly of the SMLM, including the instructions for the alignment of all the optical pathways, the software and hardware integration, and the operation of the instrument. We describe the validation steps, including the preparation and imaging of test and biological samples with structures of well-defined geometries, and assist the user in troubleshooting and benchmarking the system's performance. Additionally, we provide a walkthrough of the reconstruction of a super-resolved dataset from acquired raw images using the Super-resolution Microscopy Analysis Platform. Depending on the instrument configuration, the cost of the components is in the range US$95,000-180,000, similar to other open-source advanced SMLMs, and substantially lower than the cost of a commercial instrument. A builder with some experience of optical systems is expected to require 4-8 months from the start of the system construction to attain high-quality three-dimensional and multicolor biological images.

Structural Characterization of Monoclonal Antibodies and Epitope Mapping by FFAP Footprinting.

Covalent labeling in combination with mass spectrometry is a powerful approach used in structural biology to study protein structures, interactions, and dynamics. Recently, the toolbox of covalent labeling techniques has been expanded with fast fluoroalkylation of proteins (FFAP). FFAP is a novel radical labeling method that utilizes fluoroalkyl radicals generated from hypervalent Togni reagents for targeting aromatic residues. This report further demonstrates the benefits of FFAP as a new method for structural characterization of therapeutic antibodies and interaction interfaces of antigen-antibody complexes. The results obtained from human trastuzumab and its complex with human epidermal growth factor receptor 2 (HER2) correlate well with previously published structural data and demonstrate the potential of FFAP in structural biology.

Taste and its receptors in human physiology: A comprehensive look

AbstractIncreasing evidence shows that food has significance beyond traditional perception (providing nutrition and energy) in maintaining normal life activities. It is indicated that the sense of taste plays a crucial part in regulating human life activities. Taste is one of the basic physiological sensations in mammals, and it is the fundamental guarantee for them to perceive, select, and ingest nutrients in order to survive. With the advances in electrophysiology, molecular biology, and structural biology, studies on the intracellular and extracellular transduction mechanisms of taste have made great progress and gradually revealed the indispensable role of taste receptors in the regulation and maintenance of normal physiological activities. Up to now, how food regulates life activities through the taste pathway remains unclear. Thus, this review comprehensively and systematically summarizes the current study about the sense of taste, the function of taste receptors, the taste–structure relationship of gustatory molecules, the cross‐talking between distinctive tastes, and the role of the gut–organ axis in the realization of taste. Moreover, we also provide forward‐looking perspectives on taste research to afford a scientific basis for revealing the scientific connotation of taste receptors regulating body health.

From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge.Scientific contributionThis study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize thisknowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

The Effect of the Prior and the Experimental Design on the Inference of the Precision Matrix in Gaussian Chain Graph Models

AbstractHere, we investigate whether (and how) experimental design could aid in the estimation of the precision matrix in a Gaussian chain graph model, especially the interplay between the design, the effect of the experiment and prior knowledge about the effect. Estimation of the precision matrix is a fundamental task to infer biological graphical structures like microbial networks. We compare the marginal posterior precision of the precision matrix under four priors: flat, conjugate Normal-Wishart, Normal-MGIG and a general independent. Under the flat and conjugate priors, the Laplace-approximated posterior precision is not a function of the design matrix rendering useless any efforts to find an optimal experimental design to infer the precision matrix. In contrast, the Normal-MGIG and general independent priors do allow for the search of optimal experimental designs, yet there is a sharp upper bound on the information that can be extracted from a given experiment. We confirm our theoretical findings via a simulation study comparing (i) the KL divergence between prior and posterior and (ii) the Stein’s loss difference of MAPs between random and no experiment. Our findings provide practical advice for domain scientists conducting experiments to better infer the precision matrix as a representation of a biological network.

Introducing: Ulrich J. Lorenz, Radoslav I. Enchev, Clare F. Megarity, Helen Ginn, Patrick Rabe, Alice R. Walker, and Kartik Ayyer

In this Voices article we introduce seven impressive young group leaders who spoke at the recent Ringberg Workshop on Structural Biology organized by Ilme Schlichting from the Max Planck Institute for Medical Research, Heidelberg. We asked them to tell us more about their careers and their exciting research.

A bio-inspired broadband absorption metamaterial: driven by dual-structure synergistically induced current vortices

The performance breakthroughs of some stealth materials have benefited from incorporating biomimetic concepts, and the design ideas of wave-absorbing metamaterials have been greatly broadened. However, stealth materials developed based on a single biological structure still have limitations regarding overall performance and design freedom. Herein, a dual-structure element combination model with a butterfly-wing porous structure and moth-eye raised structure arranged in an orderly manner is established. Carbonyl iron and polyurethane are mixed as wave absorbents, and the model is utilized to make a biomimetic metamaterial (CSMA), which has an absorption rate of more than 90% at 6.07–18 GHz, achieving broadband effective absorption. It has been verified that the two biostructures designed after an ordered arrangement show synergistic effects in the combined model, and the cooperation between the structures induces the formation of current vector vortices, which are able to induce microwave losses to broaden the effective absorbing bandwidth. Further, the model has the combined application performance of polarization insensitivity, strong stability of oblique incidence, and low bistatic RCS. Such a thought based on the combination of multiple components provides an effective strategy for the design of broadband-absorbing metamaterials.

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.