Cryo-electron microscopy and X-ray crystallography: complementary approaches to structural biology and drug discovery

AI-Driven Deep Learning Techniques in Protein Structure Prediction

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Expression, purification, and characterization of transmembrane protein homogentisate solanesyltransferase

Homogentisate solanesyltransferase (HST) is a crucial enzyme in the plastoquinone biosynthetic pathway and has recently emerged as a promising target for herbicides. In this study, we successfully expressed and purified a stable and highly pure form of seven times transmembrane protein Chlamydomonas reinhardtii HST (CrHST). The final yield of CrHST protein obtained was 12.2 mg per liter of M9 medium. We evaluated the inhibitory effect on CrHST using Des-Morpholinocarbony Cyclopyrimorate (DMC) and found its IC50 value to be 3.63 ± 0.53 μM, indicating significant inhibitory potential. Additionally, we investigated the substrate affinity of CrHST with two substrates, determining the Km values as 22.76 ± 1.70 μM for FPP and 48.54 ± 3.89 μM for HGA. Through sequence alignment analyses and three-dimensional structure predictions, we identified conserved amino acid residues forming the active cavity in the enzyme. The results from molecular docking and binding energy calculations indicate that DMC has a greater binding affinity with HST compared to HGA. These findings represent substantial progress in understanding CrHST's properties and potential for herbicide development. KEY POINTS: • First high-yield transmembrane CrHST protein via E. coli system • Preliminarily identified active cavity composition via activity testing • Determined substrate and inhibitor modes via molecular docking.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Time-Lapse Macro Imaging with Dissolution Tests for Exploring the Interrelationship Between Disintegration and Dissolution Behaviors of Solid Dosages

OBJECTIVE

This study aims to establish a Flow-through Visualization Dissolution System (FVDS) that combines time-lapse macro-imaging and a flow-through cell to simultaneously elucidate dissolution and disintegration profiles.

METHODS

Three cefaclor extended-release tablets (CEC-1, CEC-2, CEC-3) from different manufacturers were subjected to dissolution tests using both the US Pharmacopeia basket method and the FVDS method. Two dissolution media plans were implemented in FVDS: i) Plan I involved dissolution in pH1.0 medium for 12 h; ii) Plan II initiated dissolution in pH1.0 medium for 1 h, followed by pH6.8 phosphate buffer for 11 h. The resulting dissolution data were fitted using classic mathematical models. Pixel information was further extracted from images obtained using FVDS and plotted over time.

RESULTS

The basket method showed the cumulative dissolution of all three tablets in pH1.0, pH4.0 and water reached 80% within 6 h, but remained below 60% in the pH6.8 medium. The f2 values indicated CEC-2 was similar to CEC-1 in the pH4.0 medium, pH6.8 medium and water. Using FVDS with medium plan II, the cumulative dissolution of CEC-1 and CEC-2 reached about 80% showing similarity, while no similarity was observed between CEC-3 and CEC-1. The f2 factor of the percentage area change profiles also showed consistent results in the dissolution profile of medium plan II. However, FVDS with medium plan I cannot distinguish between CEC-2 and CEC-3.

CONCLUSION

FVDS offers an alternative to traditional dissolution methods by integrating imaging analysis as a complementary tool to disintegration and dissolution testing methods.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

Overview of Membrane Protein Sample Preparation for Single-Particle Cryo-Electron Microscopy Analysis

Single-particle cryo-electron microscopy (cryo-EM SPA) has recently emerged as an exceptionally well-suited technique for determining the structure of membrane proteins (MPs). Indeed, in recent years, huge increase in the number of MPs solved via cryo-EM SPA at a resolution better than 3.0 Å in the Protein Data Bank (PDB) has been observed. However, sample preparation remains a significant challenge in the field. Here, we evaluated the MPs solved using cryo-EM SPA deposited in the PDB in the last two years at a resolution below 3.0 Å. The most critical parameters for sample preparation are as follows: (i) the surfactant used for protein extraction from the membrane, (ii) the surfactant, amphiphiles, nanodiscs or other molecules present in the vitrification step, (iii) the vitrification method employed, and (iv) the type of grids used. The aim is not to provide a definitive answer on the optimal sample conditions for cryo-EM SPA of MPs but rather assess the current trends in the MP structural biology community towards obtaining high-resolution cryo-EM structures.

The current role and evolution of X-ray crystallography in drug discovery and development

INTRODUCTION

Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination.

AREAS COVERED

This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions.

EXPERT OPINION

Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.

Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics

Functional proteomics aims to elucidate biological functions, mechanisms, and pathways of proteins and proteoforms at the molecular level to examine complex cellular systems and disease states. A series of stability proteomics methods have been developed to examine protein functionality by measuring the resistance of a protein to chemical or thermal denaturation or proteolysis. These methods can be applied to measure the thermal stability of thousands of proteins in complex biological samples such as cell lysate, intact cells, tissues, and other biological fluids to measure proteome stability. Stability proteomics methods have been popularly applied to observe stability shifts upon ligand binding for drug target identification. More recently, these methods have been applied to characterize the effect of structural changes in proteins such as those caused by post-translational modifications (PTMs) and mutations, which can affect protein structures or interactions and diversify protein functions. Here, we discussed the current application of a suite of stability proteomics methods, including thermal proteome profiling (TPP), stability of proteomics from rates of oxidation (SPROX), and limited proteolysis (LiP) methods, to observe PTM-induced structural changes on protein stability. We also discuss future perspectives highlighting the integration of top-down mass spectrometry and stability proteomics methods to characterize intact proteoform stability and understand the function of variable protein modifications.

X-ray crystallography and sickle cell disease drug discovery-a tribute to Donald Abraham

X-ray crystallography and structure-based drug discovery have played a major role in the discovery of antisickling agents that target hemoglobin (Hb) for the treatment of sickle cell disease (SCD). Sickle cell disease, the most common inherited hematologic disorder, occurs as a result of a single point mutation of βGlu6 in normal human adult hemoglobin (HbA) to βVal6 in sickle hemoglobin (HbS). The disease is characterized by polymerization of HbS and sickling of red blood cells (RBCs), leading to several secondary pathophysiologies, including but not limited to vaso-occlusion, hemolytic anemia, oxidative stress, inflammation, stroke, pain crisis, and organ damage. Despite the fact that SCD was the first disease to have its molecular basis established, the development of therapies was for a very long time a challenge and took several decades to find therapeutic agents. The determination of the crystal structure of Hb by Max Perutz in the early 60s, and the pioneering X-ray crystallography research by Donald J. Abraham in the early 80s, which resulted in the first structures of Hb in complex with small molecule allosteric effectors of Hb, gave much hope that structure-based drug discovery (SBDD) could be used to accelerate development of antisickling drugs that target the primary pathophysiology of hypoxia-induced HbS polymerization to treat SCD. This article, which is dedicated to Donald J. Abraham, briefly reviews structural biology, X-ray crystallography and structure-based drug discovery from the perspective of Hb. The review also presents the impact of X-ray crystallography in SCD drug development using Hb as a target, emphasizing the major and important contributions by Don Abraham in this field.

Integration of functional peptides into nucleic acid-based nanostructures

In many applications such as diagnostics and therapy development, small peptide fragments consisting of only a few amino acids are often attractive alternatives to bulky proteins. This is due to factors such as the ease of scalable chemical synthesis and numerous methods for their discovery. One drawback of using peptides is that their activity can often be negatively impacted by the lack of a rigid, 3D stabilizing structure provided by the rest of the protein. In many cases, this can be alleviated by different methods of rational templating onto nanomaterials, which provides additional possibilities to use concepts of multivalence or rational nano-engineering to enhance or even create new types of function or structure. In recent years, nanostructures made from the self-assembly of DNA strands have been used as scaffolds to create functional arrangements of peptides, often leading to greatly enhanced biological activity or new material properties. This review will give an overview of nano-templating approaches based on the combination of DNA nanotechnology and peptides. This will include both bioengineering strategies to control interactions with cells or other biological systems, as well as examples where the combination of DNA and peptides has been leveraged for the rational design of new functional materials.

Potassium Ion Channels in Glioma: From Basic Knowledge into Therapeutic Applications

Ion channels, specifically those controlling the flux of potassium across cell membranes, have recently been shown to exhibit an important role in the pathophysiology of glioma, the most common primary central nervous system tumor with a poor prognosis. Potassium channels are grouped into four subfamilies differing by their domain structure, gating mechanisms, and functions. Pertinent literature indicates the vital functions of potassium channels in many aspects of glioma carcinogenesis, including proliferation, migration, and apoptosis. The dysfunction of potassium channels can result in pro-proliferative signals that are highly related to calcium signaling as well. Moreover, this dysfunction can feed into migration and metastasis, most likely by increasing the osmotic pressure of cells allowing the cells to initiate the "escape" and "invasion" of capillaries. Reducing the expression or channel blockage has shown efficacy in reducing the proliferation and infiltration of glioma cells as well as inducing apoptosis, priming several approaches to target potassium channels in gliomas pharmacologically. This review summarizes the current knowledge on potassium channels, their contribution to oncogenic transformations in glioma, and the existing perspectives on utilizing them as potential targets for therapy.

Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion.

Measuring the NQO2: Melatonin Complex by Native Nano-Electrospray Ionization Mass Spectrometry

Melatonin exerts its effects through a series of target proteins/receptors and enzymes. Its antioxidant capacity might be due to its capacity to inhibit a quinone reductase (NQO2) at high concentration (50 μM). Demonstrating the existence of a complex between a compound and a protein is often not easy. It requires either that the compound is an inhibitor-and the complex translates by an inhibition of the catalytic activity-or the compound is radiolabeled-and the complex translates in standard binding approaches, such as in receptology. Outside these two cases, the detection of the protein:small molecule complexes by mass spectrometry has recently been made possible, thanks to the development of so-called native mass spectrometry. Using this approach, one can measure masses corresponding to an intact noncovalent complex between a compound and its target, usually after titration or competition experiments. In the present chapter, we detail the characterization of NQO2:melatonin interaction using native mass spectrometry.

Computational methods and theory for ion channel research

Ion channels are fundamental biological devices that act as gates in order to ensure selective ion transport across cellular membranes; their operation constitutes the molecular mechanism through which basic biological functions, such as nerve signal transmission and muscle contraction, are carried out. Here, we review recent results in the field of computational research on ion channels, covering theoretical advances, state-of-the-art simulation approaches, and frontline modeling techniques. We also report on few selected applications of continuum and atomistic methods to characterize the mechanisms of permeation, selectivity, and gating in biological and model channels.

Biophysical applications in structural and molecular biology

The main objective of structural biology is to model proteins and other biological macromolecules and link the structural information to function and dynamics. The biological functions of protein molecules and nucleic acids are inherently dependent on their conformational dynamics. Imaging of individual molecules and their dynamic characteristics is an ample source of knowledge that brings new insights about mechanisms of action. The atomic-resolution structural information on most of the biomolecules has been solved by biophysical techniques; either by X-ray diffraction in single crystals or by nuclear magnetic resonance (NMR) spectroscopy in solution. Cryo-electron microscopy (cryo-EM) is emerging as a new tool for analysis of a larger macromolecule that couldn't be solved by X-ray crystallography or NMR. Now a day's low-resolution Cryo-EM is used in combination with either X-ray crystallography or NMR. The present review intends to provide updated information on applications like X-ray crystallography, cryo-EM and NMR which can be used independently and/or together in solving structures of biological macromolecules for our full comprehension of their biological mechanisms.

Imaging Infection Across Scales of Size: From Whole Animals to Single Molecules

Infectious diseases are a leading cause of global morbidity and mortality, and the threat of infectious diseases to human health is steadily increasing as new diseases emerge, existing diseases reemerge, and antimicrobial resistance expands. The application of imaging technology to the study of infection biology has the potential to uncover new factors that are critical to the outcome of host-pathogen interactions and to lead to innovations in diagnosis and treatment of infectious diseases. This article reviews current and future opportunities for the application of imaging to the study of infectious diseases, with a particular focus on the power of imaging objects across a broad range of sizes to expand the utility of these approaches. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Biochemistry, structure, and cellular internalization of a four nanobody-bearing Fc dimer

VHH stands for the variable regions of heavy chain only of camelid IgGs. The VHH family forms a set of interesting proteins derived from antibodies that maintain their capacity to recognize the antigen, despite their relatively small molecular weight (in the 12,000 Da range). Continuing our exploration of the possibilities of those molecules, we chose to design alternative molecules with maintained antigen recognition, but enhanced capacity, by fusing four VHH with one Fc, the fragment crystallizable region of antibodies. In doing so, we aimed at having a molecule with superior quantitative antigen recognition (×4) while maintaining its size below the 110 kDa. In the present paper, we described the building of those molecules that we coined VHH₂ -Fc-VHH₂ . The structure of VHH₂ -Fc-VHH₂ in complex with HER2 antigen was determined using electronic microscopy and modeling. The molecule is shown to bind four HER2 proteins at the end of its flexible arms. VHH₂ -Fc-VHH₂ also shows an internalization capacity via HER2 receptor superior to the reference anti-HER2 monoclonal antibody, Herceptin®, and to a simple fusion of two VHH with one Fc (VHH₂ -Fc). This new type of molecules, VHH₂ -Fc-VHH₂ , could be an interesting addition to the therapeutic arsenal with multiple applications, from diagnostic to therapy.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

Optical super-resolution microscopy (SRM) has enabled biologists to visualize cellular structures with near-molecular resolution, giving unprecedented access to details about the amounts, sizes, and spatial distributions of macromolecules in the cell. Precisely quantifying these molecular details requires large datasets of high-quality, reproducible SRM images. In this review, we discuss the unique set of challenges facing quantitative SRM, giving particular attention to the shortcomings of conventional specimen preparation techniques and the necessity for optimal labeling of molecular targets. We further discuss the obstacles to scaling SRM methods, such as lengthy image acquisition and complex SRM data analysis. For each of these challenges, we review the recent advances in the field that circumvent these pitfalls and provide practical advice to biologists for optimizing SRM experiments.

Ribosome-targeting antibacterial agents: Advances, challenges, and opportunities

Ribosomes, which synthesize proteins, are critical organelles for the survival and growth of bacteria. About 60% of approved antibiotics discovered so far combat pathogenic bacteria by targeting ribosomes. However, several issues, such as drug resistance and toxicity, have impeded the clinical use of ribosome-targeting antibiotics. Moreover, the complexity of the bacteria ribosome structure has retarded the discovery of new ribosome-targeting agents that are considered as the key to the drug-resistance and toxicity. To deal with these challenges, efforts such as medicinal chemistry optimization, combination treatment, and new drug delivery system have been developed. But not enough, the development of structural biology and new screening methods bring powerful tools, such as cryo-electron microscopy technology, advanced computer-aided drug design, and cell-free in vitro transcription/translation systems, for the discovery of novel ribosome-targeting antibiotics. Thus, in this paper, we overview the research on different aspects of bacterial ribosomes, especially focus on discussing the challenges in the discovery of ribosome-targeting antibacterial drugs and advances made to address issues such as drug-resistance and selectivity, which, we believe, provide perspectives for the discovery of novel antibiotics.

All-atom simulations to studying metallodrugs/target interactions

Metallodrugs are extensively used to treat and diagnose distinct disease types. The unique physical-chemical properties of metal ions offer tantalizing opportunities to tailor effective scaffolds for selectively targeting specific biomolecules. Modern experimental techniques have collected a large body of structural data concerning the interactions of metallodrugs with their biomolecular targets, although being unable to exhaustively assess the molecular basis of their mechanism of action. In this scenario, the complementary use of accurate computational methods allows uncovering the minutiae of metallodrugs/targets interactions and their underlying mechanism of action at an atomic-level of detail. This knowledge is increasingly perceived as an invaluable requirement to rationally devise novel and selective metallodrugs. Building on literature studies, selected largely from the last 2 years, this compendium encompasses a cross-section of the current role, advances, and challenges met by computer simulations to decipher the mechanistic intricacies of prototypical metallodrugs.

Towards an Optimal Sample Delivery Method for Serial Crystallography at XFEL

The advent of the X-ray free electron laser (XFEL) in the last decade created the discipline of serial crystallography but also the challenge of how crystal samples are delivered to X-ray. Early sample delivery methods demonstrated the proof-of-concept for serial crystallography and XFEL but were beset with challenges of high sample consumption, jet clogging and low data collection efficiency. The potential of XFEL and serial crystallography as the next frontier of structural solution by X-ray for small and weakly diffracting crystals and provision of ultra-fast time-resolved structural data spawned a huge amount of scientific interest and innovation. To utilize the full potential of XFEL and broaden its applicability to a larger variety of biological samples, researchers are challenged to develop better sample delivery methods. Thus, sample delivery is one of the key areas of research and development in the serial crystallography scientific community. Sample delivery currently falls into three main systems: jet-based methods, fixed-target chips, and drop-on-demand. Huge strides have since been made in reducing sample consumption and improving data collection efficiency, thus enabling the use of XFEL for many biological systems to provide high-resolution, radiation damage-free structural data as well as time-resolved dynamics studies. This review summarizes the current main strategies in sample delivery and their respective pros and cons, as well as some future direction.

Structural basis of light-harvesting in the photosystem II core complex

Photosystem II (PSII) is a membrane-spanning, multi-subunit pigment-protein complex responsible for the oxidation of water and the reduction of plastoquinone in oxygenic photosynthesis. In the present review, the recent explosive increase in available structural information about the PSII core complex based on X-ray crystallography and cryo-electron microscopy is described at a level of detail that is suitable for a future structure-based analysis of light-harvesting processes. This description includes a proposal for a consistent numbering scheme of protein-bound pigment cofactors across species. The structural survey is complemented by an overview of the state of affairs in structure-based modeling of excitation energy transfer in the PSII core complex with emphasis on electrostatic computations, optical properties of the reaction center, the assignment of long-wavelength chlorophylls, and energy trapping mechanisms.

The five dimensions of receptor pharmacology exemplified by melatonin receptors: An opinion

Receptology has been complicated with enhancements in our knowledge of G-protein-coupled-receptor (GPCR) biochemistry. This complexity is exemplified by the pharmacology of melatonin receptors. Here, we describe the complexity of GPCR biochemistry in five dimensions: (a) receptor expression, particularly in organs/tissues that are only partially understood; (b) ligands and receptor-associated proteins (interactome); (c) receptor function, which might be more complex than the known G-protein-coupled systems; (d) ligand bias, which favors a particular pathway; and (e) receptor dimerization, which might concern all receptors coexpressed in the same cell. Thus, receptor signaling might be modified or modulated, depending on the nature of the receptor complex. Fundamental studies are needed to clarify these points and find new ways to tackle receptor functionality. This opinion article emphasizes the global questions attached to new descriptions of GPCRs and aims to raise our awareness of the tremendous complexity of modern receptology.

Host Transcription Factors in Hepatitis B Virus RNA Synthesis

The hepatitis B virus (HBV) chronically infects over 250 million people worldwide and is one of the leading causes of liver cancer and hepatocellular carcinoma. HBV persistence is due in part to the highly stable HBV minichromosome or HBV covalently closed circular DNA (cccDNA) that resides in the nucleus. As HBV replication requires the help of host transcription factors to replicate, focusing on host protein–HBV genome interactions may reveal insights into new drug targets against cccDNA. The structural details on such complexes, however, remain poorly defined. In this review, the current literature regarding host transcription factors’ interactions with HBV cccDNA is discussed.

Modeling Structural Constraints on Protein Evolution via Side-Chain Conformational States

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well as information about the rotamer configuration of its side-chain: the discrete geometric pattern of permitted side-chain atomic positions, as defined by the dihedral angles between covalently linked atoms. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state “Dayhoff-like” model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference, and ancestral state reconstruction. We conclude that not only is rotamer configuration a valuable source of information for phylogenetic studies, but that modeling the concomitant evolution of sequence and structure may have important implications for understanding protein folding and function.

In cell Gd3+-based site-directed spin labeling and EPR spectroscopy of eGFP

A newly synthesized Gd³⁺ chelate complex allows in cell spin labeling and detection of eGFP by EPR spectroscopy.

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Protein-protein interaction (PPI) networks are known to be valuable targets for therapeutic intervention; yet the development of PPI modulators as next-generation drugs to target specific vertices, edges, and hubs has been impeded by the lack of structural information of many of the proteins and complexes involved. Building on recent advancements in cross-linking mass spectrometry (XL-MS), we describe an effective approach to obtain relevant structural data on R7BP, a master regulator of itch sensation, and its interfaces with other proteins in its network. This approach integrates XL-MS with a variety of modeling techniques to successfully develop antibody inhibitors of the R7BP and RGS7/Gβ5 duplex interaction. Binding and inhibitory efficiency are studied by surface plasmon resonance spectroscopy and through an R7BP-derived dominant negative construct. This approach may have broader applications as a tool to facilitate the development of PPI modulators in the absence of crystal structures or when structural information is limited.

Binding symmetry and surface flexibility mediate antibody self-association

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

How to fold and protect mitochondrial ribosomal RNA with fewer guanines

Mammalian mitochondrial ribosomes evolved from bacterial ribosomes by reduction of ribosomal RNAs, increase of ribosomal protein content, and loss of guanine nucleotides. Guanine is the base most sensitive to oxidative damage. By systematically comparing high-quality, small ribosomal subunit RNA sequence alignments and solved 3D ribosome structures from mammalian mitochondria and bacteria, we deduce rules for folding a complex RNA with the remaining guanines shielded from solvent. Almost all conserved guanines in both bacterial and mammalian mitochondrial ribosomal RNA form guanine-specific, local or long-range, RNA–RNA or RNA–protein interactions. Many solvent-exposed guanines conserved in bacteria are replaced in mammalian mitochondria by bases less sensitive to oxidation. New guanines, conserved only in the mitochondrial alignment, are strategically positioned at solvent inaccessible sites to stabilize the ribosomal RNA structure. New mitochondrial proteins substitute for truncated RNA helices, maintain mutual spatial orientations of helices, compensate for lost RNA–RNA interactions, reduce solvent accessibility of bases, and replace guanines conserved in bacteria by forming specific amino acid–RNA interactions.

Importance of the Choice of a Recombinant System to Produce Large Amounts of Functional Membrane Protein hERG

Human ether-a-gogo related gene (hERG) product is the membrane potassium channel Kv11.1, which is involved in the electrical activity of the heart. As such, it is a key player in the toxicity of many drug candidates. Therefore, having this protein at hand during earlier stages of drug discovery is important for preventing later toxicity. Furthermore, having a fair quantity of functional channels may help in the development of the necessary techniques for gaining insight in this channel structure. Thus, we performed a comparative study of methods for over-expressing a mutated but functional, hERG in different orthologous hosts, such as yeast, bacteria, insect and human cell lines. We also engineered the protein to test various constructs of a functional channel. We obtained a significant amount of a functional mutant channel from HEK cells that we thoroughly characterized. The present work paves the way for the expression of large amounts of this protein, with which protein crystallization or cryo-electronic microscopy will be attempted. This will be a way to gain information on the structure of the hERG active site and its modelization to obtain data on the pauses of various reference compounds from the pharmacopeia, as well as to gain information about the thermodynamics of the hERG/ligand relationship.

Current Advancements in Addressing Key Challenges of Therapeutic Antibody Design, Manufacture, and Formulation

Therapeutic antibody technology heavily dominates the biologics market and continues to present as a significant industrial interest in developing novel and improved antibody treatment strategies. Many noteworthy advancements in the last decades have propelled the success of antibody development; however, there are still opportunities for improvement. In considering such interest to develop antibody therapies, this review summarizes the array of challenges and considerations faced in the design, manufacture, and formulation of therapeutic antibodies, such as stability, bioavailability and immunological engagement. We discuss the advancement of technologies that address these challenges, highlighting key antibody engineered formats that have been adapted. Furthermore, we examine the implication of novel formulation technologies such as nanocarrier delivery systems for the potential to formulate for pulmonary delivery. Finally, we comprehensively discuss developments in computational approaches for the strategic design of antibodies with modulated functions.

Detection of Drug Binding to a Target Protein Using EVV 2DIR Spectroscopy

We demonstrate that electron-vibration-vibration two-dimensional infrared spectroscopy (EVV 2DIR) can be used to detect the binding of a drug to a target protein-active site. The EVV 2DIR spectrum of the FGFR1 kinase target protein is found to have ∼200 detectable cross-peaks in the spectral region 1250-1750 cm^-1/2600-3400 cm^-1, with additional 63 peaks caused by the addition of a drug, SU5402. Of these 63 new peaks, it is shown that only six are due to protein-drug interactions, with the other 57 being due to vibrational coupling within the drug itself. Quantum mechanical calculations employing density functional theory are used to support assignment of the six binding-dependent peaks, with one being assigned to a known interaction between the drug and a backbone carbonyl group which forms part of the binding site. None of the 57 intramolecular coupling peaks associated with the drug molecule change substantially in either intensity or frequency when the drug binds to the target protein. This strongly suggests that the structure of the drug in the target binding site is essentially identical to that when it is not bound.

Mobility Capillary Electrophoresis-Restrained Modeling Method for Protein Structure Analysis in Mixtures

Protein stereostructure analysis in mixtures still remains challenging, especially large-scale analysis such as in proteomics. With the capability of measuring the hydrodynamic radius of ions in the liquid phase, mobility capillary electrophoresis (MCE) has been applied to study the structure of peptides. In this study, MCE was extended for protein mixture separation and their corresponding hydrodynamic radius analyses. After ellipsoid approximation, the results obtained by MCE experiments were then used as a restraint in molecular dynamics simulations to predict the most probable structure of each protein. Besides a three-protein mixture, a mixture of disulfide bond reduced insulin was also studied by this MCE-restrained modeling method. The results obtained by this method agree with literature studies, and mass spectrometry experiments were also carried out to confirm our findings.

New Structures and Gating of Voltage-Dependent Potassium (Kv) Channels and Their Relatives: A Multi-Domain and Dynamic Question

Voltage-dependent potassium channels (Kv channels) are crucial regulators of cell excitability that participate in a range of physiological and pathophysiological processes. These channels are molecular machines that display a mechanism (known as gating) for opening and closing a gate located in a pore domain (PD). In Kv channels, this mechanism is triggered and controlled by changes in the magnitude of the transmembrane voltage sensed by a voltage-sensing domain (VSD). In this review, we consider several aspects of the VSD–PD coupling in Kv channels, and in some relatives, that share a common general structure characterized by a single square-shaped ion conduction pore in the center, surrounded by four VSDs located at the periphery. We compile some recent advances in the knowledge of their architecture, based in cryo-electron microscopy (cryo-EM) data for high-resolution determination of their structure, plus some new functional data obtained with channel variants in which the covalent continuity between the VSD and PD modules has been interrupted. These advances and new data bring about some reconsiderations about the use of exclusively a classical electromechanical lever model of VSD–PD coupling by some Kv channels, and open a view of the Kv-type channels as allosteric machines in which gating may be dynamically influenced by some long-range interactional/allosteric mechanisms.

Visualization of biological macromolecules at near-atomic resolution: cryo-electron microscopy comes of age

Structural biology is going through a revolution as a result of transformational advances in the field of cryo-electron microscopy (cryo-EM) driven by the development of direct electron detectors and ultrastable electron microscopes. High-resolution cryo-EM images of isolated biomolecules (single particles) suspended in a thin layer of vitrified buffer are subjected to powerful image-processing algorithms, enabling near-atomic resolution structures to be determined in unprecedented numbers. Prior to these advances, electron crystallography of two-dimensional crystals and helical assemblies of proteins had established the feasibility of atomic resolution structure determination using cryo-EM. Atomic resolution single-particle analysis, without the need for crystals, now promises to resolve problems in structural biology that were intractable just a few years ago.

Recent advances in understanding the ryanodine receptor calcium release channels and their role in calcium signalling

The ryanodine receptor calcium release channel is central to cytoplasmic Ca ²⁺ signalling in skeletal muscle, the heart, and many other tissues, including the central nervous system, lymphocytes, stomach, kidney, adrenal glands, ovaries, testes, thymus, and lungs. The ion channel protein is massive (more than 2.2 MDa) and has a structure that has defied detailed determination until recent developments in cryo-electron microscopy revealed much of its structure at near-atomic resolution. The availability of this high-resolution structure has provided the most significant advances in understanding the function of the ion channel in the past 30 years. We can now visualise the molecular environment of individual amino acid residues that form binding sites for essential modulators of ion channel function and determine its role in Ca ²⁺ signalling. Importantly, the structure has revealed the structural environment of the many deletions and point mutations that disrupt Ca ²⁺ signalling in skeletal and cardiac myopathies and neuropathies. The implications are of vital importance to our understanding of the molecular basis of the ion channel’s function and for the design of therapies to counteract the effects of ryanodine receptor-associated disorders.

Quo vadis, Acta Crystallographica F?

The Section Editor discusses the current state and future of Acta Crystallographica F and encourages readers to submit their best Research Communications.

Using cryo-electron microscopy maps for X-ray structure determination

X-ray crystallography and cryo-electron microscopy (cryo-EM) are complementary techniques for structure determination. Crystallography usually reveals more detailed information, while cryo-EM is an extremely useful technique for studying large-sized macromolecules. As the gap between the resolution of crystallography and cryo-EM data narrows, the cryo-EM map of a macromolecule could serve as an initial model to solve the phase problem of crystal diffraction for high-resolution structure determination. FSEARCH is a procedure to utilize the low-resolution molecular shape for crystallographic phasing. The IPCAS (Iterative Protein Crystal structure Automatic Solution) pipeline is an automatic direct-methods-aided dual-space iterative phasing and model-building procedure. When only an electron-density map is available as the starting point, IPCAS is capable of generating a completed model from the phases of the input map automatically, without the requirement of an initial model. In this study, a hybrid method integrating X-ray crystallography with cryo-EM to help with structure determination is presented. With a cryo-EM map as the starting point, the workflow of the method involves three steps. (1) Cryo-EM map replacement: FSEARCH is utilized to find the correct translation and orientation of the cryo-EM map in the crystallographic unit cell and generates the initial low-resolution map. (2) Phase extension: the phases calculated from the correctly placed cryo-EM map are extended to high-resolution X-ray data by non-crystallographic symmetry averaging with phenix.resolve. (3) Model building: IPCAS is used to generate an initial model using the phase-extended map and perform model completion by iteration. Four cases (the lowest cryo-EM map resolution being 6.9 Å) have been tested for the general applicability of the hybrid method, and almost complete models have been generated for all test cases with reasonable Rwork/Rfree. The hybrid method therefore provides an automated tool for X-ray structure determination using a cryo-EM map as the starting point.

When a transmembrane channel isn't, or how biophysics and biochemistry (mis)communicate

Annexins are a family of soluble proteins that bind to acidic phospholipids such as phosphatidylserine in a calcium-dependent manner. The archetypical member of the annexin family is annexin A5. For many years, its function remained unknown despite the availability of a high-resolution structure. This, combined with the observations of specific ion conductance in annexin-bound membranes, fueled speculations about the possible membrane-spanning forms of annexins that functioned as ion channels. The channel hypothesis remained controversial and did not gather sufficient evidence to become accepted. Yet, it continues to draw attention as a framework for interpreting indirect (e.g., biochemical) data. The goal of the mini-review is to examine the data on annexin-lipid interactions from the last ~30 years from the point of view of the controversy between the two lines of inquiry: the well-characterized peripheral assembly of the annexins at membranes vs. their putative transmembrane insertion. In particular, the potential role of lipid rearrangements induced by annexin binding is highlighted.