Targeted removal of the FA2 site on human albumin prevents fatty acid-mediated inhibition of Zn2+ binding.

Zinc is required for virtually all biological processes. In plasma, Zn2+ is predominantly transported by human serum albumin (HSA), which possesses two Zn2+-binding sites of differing affinities (sites A and B). Fatty acids (FAs) are also transported by HSA, with seven structurally characterized FA-binding sites (named FA1-FA7) known. FA binding inhibits Zn2+-HSA interactions, in a manner that can impact upon hemostasis and cellular zinc uptake, but the degree to which binding at specific FA sites contributes to this inhibition is unclear. Wild-type HSA and H9A, H67A, H247A, and Y150F/R257A/S287A (FA2-KO) mutant albumins were expressed in Pichia pastoris. Isothermal titration calorimetry studies revealed that the Zn2+-binding capacity at the high-affinity Zn2+ site (site A) was reduced in H67A and H247A mutants, with site B less affected. The H9A mutation decreased Zn2+ binding at the lower-affinity site, establishing His9 as a site B ligand. Zn2+ binding to HSA and H9A was compromised by palmitate, consistent with FA binding affecting site A. 13C-NMR experiments confirmed that the FA2-KO mutations prohibited FA binding at site FA2. Zn2+ binding to the FA2-KO mutant was unaffected by myristate, suggesting binding at FA2 is solely responsible for inhibition. Molecular dynamics studies identified the steric obstruction exerted by bound FA in site FA2, which impedes the conformational change from open (FA-loaded) to closed (FA-free) states, required for Zn2+ to bind at site A. The successful targeting of the FA2 site will aid functional studies exploring the interplay between circulating FA levels and plasma Zn2+ speciation in health and disease.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules.

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

A high-throughput structural system biology approach to increase structure representation of proteins from Clostridioides difficile.

Clostridioides difficile causes life-threatening gastrointestinal infections. It is a high-risk pathogen due to a lack of effective treatments, antimicrobial resistance, and a poorly conserved genomic core. Herein, we report 30 X-ray structures from a structure genomics pipeline spanning 13 years, representing 10.2% of the X-ray structures for this important pathogen.

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.

Continuous Validation Across Macromolecular Structure Determination Process.

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

Structural and biochemical characterisation of Co2+-binding sites on serum albumins and their interplay with fatty acids.

Serum albumin-Co2+ interactions are of clinical importance. They play a role in mediating the physiological effects associated with cobalt toxicity and are central to the albumin cobalt binding (ACB) assay for diagnosis of myocardial ischemia. To further understand these processes, a deeper understanding of albumin-Co2+ interactions is required. Here, we present the first crystallographic structures of human serum albumin (HSA; three structures) and equine serum albumin (ESA; one structure) in complex with Co2+. Amongst a total of sixteen sites bearing a cobalt ion across the structures, two locations were prominent, and they relate to metal-binding sites A and B. Site-directed mutagenesis and isothermal titration calorimetry (ITC) were employed to characterise sites on HSA. The results indicate that His9 and His67 contribute to the primary (putatively corresponding to site B) and secondary Co2+-binding sites (site A), respectively. The presence of additional multiple weak-affinity Co2+ binding sites on HSA was also supported by ITC studies. Furthermore, addition of 5 molar equivalents of the non-esterified fatty acid palmitate (C16:0) reduced the Co2+-binding affinity at both sites A and B. The presence of bound myristate (C14:0) in the HSA crystal structures provided insight into the fatty acid-mediated structural changes that diminish the affinity of the protein toward Co2+. Together, these data provide further support for the idea that ischemia-modified albumin corresponds to albumin with excessive fatty-acid loading. Collectively, our findings provide a comprehensive understanding of the molecular underpinnings governing Co2+ binding to serum albumin.

Structural biology and public health response to biomedical threats.

Over the course of the pandemic caused by SARS-CoV-2, structural biologists have worked hand in hand with groups developing vaccines and treatments. However, relying solely on in vitro and clinical studies may be insufficient to guide vaccination and treatment developments, and other healthcare policies during virus mutations or peaks in infections and fatalities. Therefore, it is crucial to track statistical data related to the number of infections, deaths, and vaccinations in specific regions and present it in an easy-to-understand way.

Structure, Mutagenesis, and QM:MM Modeling of 3-Ketosteroid Δ1-Dehydrogenase from Sterolibacterium denitrificans─The Role of a New Putative Membrane-Associated Domain and Proton-Relay System in Catalysis.

3-Ketosteroid Δ1-dehydrogenases (KstD) are important microbial flavin enzymes that initiate the metabolism of steroid ring A and find application in the synthesis of steroid drugs. We present a structure of the KstD from Sterolibacterium denitrificans (AcmB), which contains a previously uncharacterized putative membrane-associated domain and extended proton-relay system. The experimental and theoretical studies show that the steroid Δ1-dehydrogenation proceeds according to the Ping-Pong bi-bi kinetics and a two-step base-assisted elimination (E2cB) mechanism. The mechanism is validated by evaluating the experimental and theoretical kinetic isotope effect for deuterium-substituted substrates. The role of the active-site residues is quantitatively assessed by point mutations, experimental activity assays, and QM/MM MD modeling of the reductive half-reaction (RHR). The pre-steady-state kinetics also reveals that the low pH (6.5) optimum of AcmB is dictated by the oxidative half-reaction (OHR), while the RHR exhibits a slight optimum at the pH usual for the KstD family of 8.5. The modeling confirms the origin of the enantioselectivity of C2-H activation and substrate specificity for Δ4-3-ketosteroids. Finally, the cholest-4-en-3-one turns out to be the best substrate of AcmB in terms of ΔG of binding and predicted rate of dehydrogenation.

CMM-An enhanced platform for interactive validation of metal binding sites.

Metal ions bound to macromolecules play an integral role in many cellular processes. They can directly participate in catalytic mechanisms or be essential for the structural integrity of proteins and nucleic acids. However, their unique nature in macromolecules can make them difficult to model and refine, and a substantial portion of metal ions in the PDB are misidentified or poorly refined. CheckMyMetal (CMM) is a validation tool that has gained widespread acceptance as an essential tool for researchers working on metal-macromolecule complexes. CMM can be used during structure determination or to validate metal binding sites in structural models within the PDB. The functionalities of CMM have recently been greatly enhanced and provide researchers with additional information that can guide modeling decisions. The new version of CMM shows metals in the context of electron density maps and allows for on-the-fly refinement of metal binding sites. The improvements should increase the reproducibility of biomedical research. The web server is available at https://cmm.minorlab.org.

Organism-specific differences in the binding of ketoprofen to serum albumin.

Serum albumin is a circulatory transport protein that has a highly conserved sequence and structure across mammalian organisms. Its ligand-binding properties are of importance as albumin regulates the pharmacokinetics of many drugs. Due to the high degree of structural conservation between mammalian albumins, nonhuman albumins such as bovine serum albumin or animal models are often used to understand human albumin-drug interactions. Ketoprofen is a popular nonsteroidal anti-inflammatory drug that is transported by albumin. Here, it is revealed that ketoprofen exhibits different binding-site preferences when interacting with human serum albumin compared with other mammalian albumins, despite the conservation of binding sites across species. The reasons for the observed differences were explored, including identifying ketoprofen binding determinants at specific sites and the influence of fatty acids and other ligands on drug binding. The presented results reveal that the drug-binding properties of albumins cannot easily be predicted based only on a complex of albumin from another organism and the conservation of drug sites between species. This work shows that understanding organism-dependent differences is essential for assessing the suitability of particular albumins for structural or biochemical studies.

Tip60 acetylation of histone H3K4 temporally controls chromosome passenger complex localization.

The Chromosome Passenger Complex (CPC) generates chromosome autonomous signals that regulate mitotic events critical for genome stability. Tip60 is a lysine acetyltransferase that is a tumor suppressor and is targeted for proteasomal degradation by oncogenic papilloma viruses. Mitotic regulation requires the localization of the CPC to inner centromeres, which is driven by the Haspin kinase phosphorylating histone H3 on threonine 3 (H3T3ph). Here we describe how Tip60 acetylates histone H3 at lysine 4 (H3K4ac) to block both the H3T3ph writer and the reader to ensure that this mitotic signaling cannot begin before prophase. Specifically, H3K4ac inhibits Haspin phosphorylation of H3T3 and prevents binding of the Survivin subunit to H3T3ph. Tip60 acetylates H3K4 during S/G2 at centromeres. Inhibition of Tip60 allows the CPC to bind centromeres in G2 cells, and targeting of Tip60 to centromeres prevents CPC localization in mitosis. The H3K4ac mark is removed in prophase by HDAC3 to initiate the CPC localization cascade. Together, our results suggest that Tip60 and HDAC3 temporally control H3K4 acetylation to precisely time the targeting of the CPC to inner centromeres.

New ligand-binding sites identified in the crystal structures of ß-lactoglobulin complexes with desipramine.

The homodimeric ß-lactoglobulin belongs to the lipocalin family of proteins that transport a wide range of hydrophobic molecules and can be modified by mutagenesis to develop specificity for novel groups of ligands. In this work, new lactoglobulin variants, FAF (I56F/L39A/M107F) and FAW (I56F/L39A/M107W), were produced and their interactions with the tricyclic drug desipramine (DSM) were studied using X-ray crystallography, calorimetry (ITC) and circular dichroism (CD). The ITC and CD data showed micromolar affinity of the mutants for DSM and interactions according to the classical one-site binding model. However, the crystal structures unambiguously showed that the FAF and FAW dimers are capable of binding DSM not only inside the ß-barrel as expected, but also at the dimer interface and at the entrance to the binding pocket. The presented high-resolution crystal structures therefore provide important evidence of the existence of alternative ligand-binding sites in the ß-lactoglobulin molecule. Analysis of the crystal structures highlighted the importance of shape complementarity for ligand recognition and selectivity. The binding sites identified in the crystal structures of the FAF-DSM and FAW-DSM complexes together with data from the existing literature are used to establish a systematic classification of the ligand-binding sites in the ß-lactoglobulin molecule.

Optimal structure determination from sub-optimal diffraction data.

Herein we present the newest version of the HKL-3000 system that integrates data collection, data reduction, phasing, model building, refinement, and validation. The system significantly accelerates the process of structure determination and has proven its high value for the determination of very high-quality structures. The heuristic for choosing the best approach for every step of structure determination for various quality samples and diffraction data has been optimized. The latest modifications increase the likelihood of a successful structure determination with challenging data. The HKL-3000 is a successor of HKL and HKL-2000 programs. The use of the HKL family of programs has been reported for over 73,000 PDB deposits, that is, almost 50% of macromolecular structures determined with X-ray diffraction.

virusMED: an atlas of hotspots of viral proteins.

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Detecting anomalies in X-ray diffraction images using convolutional neural networks.

Our understanding of life is based upon the interpretation of macromolecular structures and their dynamics. Almost 90% of currently known macromolecular models originated from electron density maps constructed using X-ray diffraction images. Even though diffraction images are critical for structure determination, due to their vast amounts and noisy, non-intuitive nature, their quality is rarely inspected. In this paper, we use recent advances in machine learning to automatically detect seven types of anomalies in X-ray diffraction images. For this purpose, we utilize a novel X-ray beam center detection algorithm, propose three different image representations, and compare the predictive performance of general-purpose classifiers and deep convolutional neural networks (CNNs). In benchmark tests on a set of 6,311 X-ray diffraction images, the proposed CNN achieved between 87% and 99% accuracy depending on the type of anomaly. Experimental results show that the proposed anomaly detection system can be considered suitable for early detection of sub-optimal data collection conditions and malfunctions at X-ray experimental stations.

Dr. Alexander Wlodawer-celebrating five decades of service to the structural biology community.

This 75th birthday tribute to our Editorial Board member Alexander Wlodawer recounts his decades-long service to the community of structural biology researchers. His former and current colleagues tell the story of his upbringing and education, followed by an account of his dedication to quality and rigor in crystallography and structural science. The FEBS Journal Editor-in-Chief Seamus Martin further highlights Alex's outstanding contributions to the journal's success over many years.

Rapid response to emerging biomedical challenges and threats.

As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

Gcn5-Related N-Acetyltransferases (GNATs) With a Catalytic Serine Residue Can Play Ping-Pong Too.

Enzymes in the Gcn5-related N-acetyltransferase (GNAT) superfamily are widespread and critically involved in multiple cellular processes ranging from antibiotic resistance to histone modification. While acetyl transfer is the most widely catalyzed reaction, recent studies have revealed that these enzymes are also capable of performing succinylation, condensation, decarboxylation, and methylcarbamoylation reactions. The canonical chemical mechanism attributed to GNATs is a general acid/base mechanism; however, mounting evidence has cast doubt on the applicability of this mechanism to all GNATs. This study shows that the Pseudomonas aeruginosa PA3944 enzyme uses a nucleophilic serine residue and a hybrid ping-pong mechanism for catalysis instead of a general acid/base mechanism. To simplify this enzyme's kinetic characterization, we synthesized a polymyxin B substrate analog and performed molecular docking experiments. We performed site-directed mutagenesis of key active site residues (S148 and E102) and determined the structure of the E102A mutant. We found that the serine residue is essential for catalysis toward the synthetic substrate analog and polymyxin B, but the glutamate residue is more likely important for substrate recognition or stabilization. Our results challenge the current paradigm of GNAT mechanisms and show that this common enzyme scaffold utilizes different active site residues to accomplish a diversity of catalytic reactions.

Recognizing and validating ligands with CheckMyBlob.

Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org.