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.

State-of-the-Art Data Management: Improving the Reproducibility, Consistency, and Traceability of Structural Biology and in Vitro Biochemical Experiments.

Efficient and comprehensive data management is an indispensable component of modern scientific research and requires effective tools for all but the most trivial experiments. The LabDB system developed and used in our laboratory was originally designed to track the progress of a structure determination pipeline in several large National Institutes of Health (NIH) projects. While initially designed for structural biology experiments, its modular nature makes it easily applied in laboratories of various sizes in many experimental fields. Over many years, LabDB has transformed into a sophisticated system integrating a range of biochemical, biophysical, and crystallographic experimental data, which harvests data both directly from laboratory instruments and through human input via a web interface. The core module of the system handles many types of universal laboratory management data, such as laboratory personnel, chemical inventories, storage locations, and custom stock solutions. LabDB also tracks various biochemical experiments, including spectrophotometric and fluorescent assays, thermal shift assays, isothermal titration calorimetry experiments, and more. LabDB has been used to manage data for experiments that resulted in over 1200 deposits to the Protein Data Bank (PDB); the system is currently used by the Center for Structural Genomics of Infectious Diseases (CSGID) and several large laboratories. This chapter also provides examples of data mining analyses and warnings about incomplete and inconsistent experimental data. These features, together with its capabilities for detailed tracking, analysis, and auditing of experimental data, make the described system uniquely suited to inspect potential sources of irreproducibility in life sciences research.

Regioselectivity of hyoscyamine 6ß-hydroxylase-catalysed hydroxylation as revealed by high-resolution structural information and QM/MM calculations.

Hyoscyamine 6ß-hydroxylase (H6H) is a bifunctional non-heme 2-oxoglutarate/Fe2+-dependent dioxygenase that catalyzes the two final steps in the biosynthesis of scopolamine. Based on high resolution crystal structures of H6H from Datura metel, detailed information on substrate binding was obtained that provided insights into the onset of the enzymatic process. In particular, the role of two prominent residues was revealed - Glu-116 that interacts with the tertiary amine located on the hyoscyamine tropane moiety and Tyr-326 that forms CH-π hydrogen bonds with the hyoscyamine phenyl ring. The structures were used as the basis for QM/MM calculations that provided an explanation for the regioselectivity of the hydroxylation reaction on the hyoscyamine tropane moiety (C6 vs. C7) and quantified contributions of active site residues to respective barrier heights.

Molstack: A platform for interactive presentations of electron density and cryo-EM maps and their interpretations.

In the Special Issue on Tools for Protein Science in 2018, we presented Molstack: a concept of a cloud-based platform for sharing electron density maps and their interpretations. Molstack is a web platform that allows the interactive visualization of density maps through the simultaneous presentation of multiple datasets and models in a way that allows for easy pairwise comparison. We anticipated that the users of this conceptually simple platform would find many different uses for their projects, and we were not mistaken. We have observed researchers use Molstack to present experimental evidence for their models in the form of electron density maps, omit maps, and anomalous difference density maps. Users also employed Molstack to present alternative interpretations of densities, including rerefinements and speculative interpretations. While we anticipated these types of projects to be the main use cases, we were pleased to see Molstack used to display superpositions of different models, as a tool for story-driven presentations, and for collaboration as well. Here, we present developments in the platform that were driven by user feedback, highlight several cases that used Molstack to enhance the publication, and discuss possible directions for the platform.

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years.

It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.

Testosterone meets albumin - the molecular mechanism of sex hormone transport by serum albumins.

Serum albumin is the most abundant protein in mammalian blood plasma and is responsible for the transport of metals, drugs, and various metabolites, including hormones. We report the first albumin structure in complex with testosterone, the primary male sex hormone. Testosterone is bound in two sites, neither of which overlaps with the previously suggested Sudlow site I. We determined the binding constant of testosterone to equine and human albumins by two different methods: tryptophan fluorescence quenching and ultrafast affinity extraction. The binding studies and similarities between residues comprising the binding sites on serum albumins suggest that testosterone binds to the same sites on both proteins. Our comparative analysis of albumin complexes with hormones, drugs, and other biologically relevant compounds strongly suggests interference between a number of compounds present in blood and testosterone transport by serum albumin. We discuss a possible link between our findings and some phenomena observed in human patients, such as low testosterone levels in diabetic patients.

Automatic recognition of ligands in electron density by machine learning.

Motivation: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting. Results: Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes. Availability and implementation: Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information: Supplementary data are available at Bioinformatics online.

A Gcn5-Related N-Acetyltransferase (GNAT) Capable of Acetylating Polymyxin B and Colistin Antibiotics in Vitro.

Deeper exploration of uncharacterized Gcn5-related N-acetyltransferases has the potential to expand our knowledge of the types of molecules that can be acylated by this important superfamily of enzymes and may offer new opportunities for biotechnological applications. While determining native or biologically relevant in vivo functions of uncharacterized proteins is ideal, their alternative or promiscuous in vitro capabilities provide insight into key active site interactions. Additionally, this knowledge can be exploited to selectively modify complex molecules and reduce byproducts when synthetic routes become challenging. During our exploration of uncharacterized Gcn5-related N-acetyltransferases from Pseudomonas aeruginosa, we identified such an example. We found that the PA3944 enzyme acetylates both polymyxin B and colistin on a single diaminobutyric acid residue closest to the macrocyclic ring of the antimicrobial peptide and determined the PA3944 crystal structure. This finding is important for several reasons. (1) To the best of our knowledge, this is the first report of enzymatic acylation of polymyxins and thus reveals a new type of substrate that this enzyme family can use. (2) The enzymatic acetylation offers a controlled method for antibiotic modification compared to classical promiscuous chemical methods. (3) The site of acetylation would reduce the overall positive charge of the molecule, which is important for reducing nephrotoxic effects and may be a salvage strategy for this important class of antibiotics. While the physiological substrate for this enzyme remains unknown, our structural and functional characterization of PA3944 offers insight into its unique noncanonical substrate specificity.

Refining the macromolecular model - achieving the best agreement with the data from X-ray diffraction experiment.

Refinement of macromolecular X-ray crystal structures involves using complex software with hundreds of different settings. The complexity of underlying concepts and the sheer amount sof instructions may make it difficult for less experienced crystallographers to achieve optimal results in their refinements. This tutorial review offers guidelines for choosing the best settings for the reciprocal-space refinement of macromolecular models and provides practical tips for manual model correction. To help aspiring crystallographers navigate the process, some of the most practically important concepts of protein structure refinement are described. Among the topics covered are the use and purpose of R-free, geometrical restraints, restraints on atomic displacement parameters (ADPs), refinement weights, various parametrizations of ADPs (full anisotropic refinement and TLS), and omit maps. We also give practical tips for manual model correction in Coot, modelling of side-chains with poor or missing density, and ligand identification, fitting, and refinement.

Crystal structure of thebaine 6-O-demethylase from the morphine biosynthesis pathway.

Thebaine 6-O-demethylase (T6ODM) from Papaver somniferum (opium poppy), which belongs to the non-heme 2-oxoglutarate/Fe(II)-dependent dioxygenases (ODD) family, is a key enzyme in the morphine biosynthesis pathway. Initially, T6ODM was characterized as an enzyme catalyzing O-demethylation of thebaine to neopinone and oripavine to morphinone. However, the substrate range of T6ODM was recently expanded to a number of various benzylisoquinoline alkaloids. Here, we present crystal structures of T6ODM in complexes with 2-oxoglutarate (T6ODM:2OG, PDB: 5O9W) and succinate (T6ODM:SIN, PDB: 5O7Y). Both metal and 2OG binding sites display similarity to other proteins from the ODD family, but T6ODM is characterized by an exceptionally large substrate binding cavity, whose volume can partially explain the promiscuity of this enzyme. Moreover, the size of the cavity allows for binding of multiple molecules at once, posing a question about the substrate-driven specificity of the enzyme.

Molstack-Interactive visualization tool for presentation, interpretation, and validation of macromolecules and electron density maps.

Our understanding of the world of biomolecular structures is based upon the interpretation of macromolecular models, of which ∼90% are an interpretation of electron density maps. This structural information guides scientific progress and exploration in many biomedical disciplines. The Protein Data Bank's web portals have made these structures available for mass scientific consumption and greatly broaden the scope of information presented in scientific publications. The portals provide numerous quality metrics; however, the portion of the structure that is most vital for interpretation of the function may have the most difficult to interpret electron density and this ambiguity is not reflected by any single metric. The possible consequences of basing research on suboptimal models make it imperative to inspect the agreement of a model with its experimental evidence. Molstack, a web-based interactive publishing platform for structural data, allows users to present density maps and structural models by displaying a collection of maps and models, including different interpretation of one's own data, re-refinements, and corrections of existing structures. Molstack organizes the sharing and dissemination of these structural models along with their experimental evidence as an interactive session. Molstack was designed with three groups of users in mind; researchers can present the evidence of their interpretation, reviewers and readers can independently judge the experimental evidence of the authors' conclusions, and other researchers can present or even publish their new hypotheses in the context of prior results. The server is available at http://molstack.bioreproducibility.org.

Detect, correct, retract: How to manage incorrect structural models.

The massive technical and computational progress of biomolecular crystallography has generated some adverse side effects. Most crystal structure models, produced by crystallographers or well-trained structural biologists, constitute useful sources of information, but occasional extreme outliers remind us that the process of structure determination is not fail-safe. The occurrence of severe errors or gross misinterpretations raises fundamental questions: Why do such aberrations emerge in the first place? How did they evade the sophisticated validation procedures which often produce clear and dire warnings, and why were severe errors not noticed by the depositors themselves, their supervisors, referees and editors? Once detected, what can be done to either correct, improve or eliminate such models? How do incorrect models affect the underlying claims or biomedical hypotheses they were intended, but failed, to support? What is the long-range effect of the propagation of such errors? And finally, what mechanisms can be envisioned to restore the validity of the scientific record and, if necessary, retract publications that are clearly invalidated by the lack of experimental evidence? We suggest that cognitive bias and flawed epistemology are likely at the root of the problem. By using examples from the published literature and from public repositories such as the Protein Data Bank, we provide case summaries to guide correction or improvement of structural models. When strong claims are unsustainable because of a deficient crystallographic model, removal of such a model and even retraction of the affected publication are necessary to restore the integrity of the scientific record.

Databases, Repositories, and Other Data Resources in Structural Biology.

Structural biology, like many other areas of modern science, produces an enormous amount of primary, derived, and "meta" data with a high demand on data storage and manipulations. Primary data come from various steps of sample preparation, diffraction experiments, and functional studies. These data are not only used to obtain tangible results, like macromolecular structural models, but also to enrich and guide our analysis and interpretation of various biomedical problems. Herein we define several categories of data resources, (a) Archives, (b) Repositories, (c) Databases, and (d) Advanced Information Systems, that can accommodate primary, derived, or reference data. Data resources may be used either as web portals or internally by structural biology software. To be useful, each resource must be maintained, curated, as well as integrated with other resources. Ideally, the system of interconnected resources should evolve toward comprehensive "hubs", or Advanced Information Systems. Such systems, encompassing the PDB and UniProt, are indispensable not only for structural biology, but for many related fields of science. The categories of data resources described herein are applicable well beyond our usual scientific endeavors.

CheckMyMetal: a macromolecular metal-binding validation tool.

Metals are essential in many biological processes, and metal ions are modeled in roughly 40% of the macromolecular structures in the Protein Data Bank (PDB). However, a significant fraction of these structures contain poorly modeled metal-binding sites. CheckMyMetal (CMM) is an easy-to-use metal-binding site validation server for macromolecules that is freely available at http://csgid.org/csgid/metal_sites. The CMM server can detect incorrect metal assignments as well as geometrical and other irregularities in the metal-binding sites. Guidelines for metal-site modeling and validation in macromolecules are illustrated by several practical examples grouped by the type of metal. These examples show CMM users (and crystallographers in general) problems they may encounter during the modeling of a specific metal ion.

A public database of macromolecular diffraction experiments.

The low reproducibility of published experimental results in many scientific disciplines has recently garnered negative attention in scientific journals and the general media. Public transparency, including the availability of `raw' experimental data, will help to address growing concerns regarding scientific integrity. Macromolecular X-ray crystallography has led the way in requiring the public dissemination of atomic coordinates and a wealth of experimental data, making the field one of the most reproducible in the biological sciences. However, there remains no mandate for public disclosure of the original diffraction data. The Integrated Resource for Reproducibility in Macromolecular Crystallography (IRRMC) has been developed to archive raw data from diffraction experiments and, equally importantly, to provide related metadata. Currently, the database of our resource contains data from 2920 macromolecular diffraction experiments (5767 data sets), accounting for around 3% of all depositions in the Protein Data Bank (PDB), with their corresponding partially curated metadata. IRRMC utilizes distributed storage implemented using a federated architecture of many independent storage servers, which provides both scalability and sustainability. The resource, which is accessible via the web portal at http://www.proteindiffraction.org, can be searched using various criteria. All data are available for unrestricted access and download. The resource serves as a proof of concept and demonstrates the feasibility of archiving raw diffraction data and associated metadata from X-ray crystallographic studies of biological macromolecules. The goal is to expand this resource and include data sets that failed to yield X-ray structures in order to facilitate collaborative efforts that will improve protein structure-determination methods and to ensure the availability of `orphan' data left behind for various reasons by individual investigators and/or extinct structural genomics projects.

Protein purification and crystallization artifacts: The tale usually not told.

The misidentification of a protein sample, or contamination of a sample with the wrong protein, may be a potential reason for the non-reproducibility of experiments. This problem may occur in the process of heterologous overexpression and purification of recombinant proteins, as well as purification of proteins from natural sources. If the contaminated or misidentified sample is used for crystallization, in many cases the problem may not be detected until structures are determined. In the case of functional studies, the problem may not be detected for years. Here several procedures that can be successfully used for the identification of crystallized protein contaminants, including: (i) a lattice parameter search against known structures, (ii) sequence or fold identification from partially built models, and (iii) molecular replacement with common contaminants as search templates have been presented. A list of common contaminant structures to be used as alternative search models was provided. These methods were used to identify four cases of purification and crystallization artifacts. This report provides troubleshooting pointers for researchers facing difficulties in phasing or model building.

Serum albumins-unusual allergens.

BACKGROUND: Albumins are multifunctional proteins present in the blood serum of animals. They can bind and transport a wide variety of ligands which they accommodate due to their conformational flexibility. Serum albumins are highly conserved both in amino acid sequence and three-dimensional structure. Several mammalian and avian serum albumins (SAs) are also allergens. Sensitization to one of the SAs coupled with the high degree of conservation between SAs may result in cross-reactive antibodies in allergic individuals. Sensitivity to SA generally begins with exposure to an aeroallergen, which can then lead to cross-sensitization to serum albumins present in food. SCOPE OF REVIEW: This review focuses on the allergenicity of SAs presented in a structural context. MAJOR CONCLUSIONS: SA allergenicity is unusual taking into account the high sequence identity and similarity between SA from different species and human serum albumin. Cross-reactivity of human antibodies towards different SAs is one of the most important characteristics of these allergens. GENERAL SIGNIFICANCE: Establishing a relationship between sequence and structure of different SAs and their interactions with antibodies is crucial for understanding the mechanisms of cross-sensitization of atopic individuals. Structural information can also lead to better design and production of recombinant SAs to replace natural proteins in allergy testing and desensitization. Therefore, structural analyses are important for diagnostic and treatment purposes. This article is part of a Special Issue entitled Serum Albumin.

Structural and immunologic characterization of bovine, horse, and rabbit serum albumins.

Serum albumin (SA) is the most abundant plasma protein in mammals. SA is a multifunctional protein with extraordinary ligand binding capacity, making it a transporter molecule for a diverse range of metabolites, drugs, nutrients, metals and other molecules. Due to its ligand binding properties, albumins have wide clinical, pharmaceutical, and biochemical applications. Albumins are also allergenic, and exhibit a high degree of cross-reactivity due to significant sequence and structure similarity of SAs from different organisms. Here we present crystal structures of albumins from cattle (BSA), horse (ESA) and rabbit (RSA) sera. The structural data are correlated with the results of immunological studies of SAs. We also analyze the conservation or divergence of structures and sequences of SAs in the context of their potential allergenicity and cross-reactivity. In addition, we identified a previously uncharacterized ligand binding site in the structure of RSA, and calcium binding sites in the structure of BSA, which is the first serum albumin structure to contain metal ions.

Alternaria alternata allergen Alt a 1: a unique ß-barrel protein dimer found exclusively in fungi.

BACKGROUND: Alternaria species is one of the most common molds associated with allergic diseases, and 80% of Alternaria species-sensitive patients produce IgE antibodies to a major protein allergen, Alt a 1. The structure and function of Alt a 1 is unknown. OBJECTIVE: We sought to obtain a high-resolution structure of Alt a 1 using x-ray crystallography and to investigate structural relationships between Alt a 1 and other allergens and proteins reported in the Protein Data Bank. METHODS: X-ray crystallography was used to determine the structure of Alt a 1 by using a custom-designed set of crystallization conditions. An initial Alt a 1 model was determined by the application of a Ta(6)Br(12)(2+) cluster and single-wavelength anomalous diffraction. Bioinformatic analyses were used to compare the Alt a 1 sequence and structure with that of other proteins. RESULTS: Alt a 1 is a unique ß-barrel comprising 11 ß-strands and forms a "butterfly-like" dimer linked by a single disulfide bond with a large (1345 Å(2)) dimer interface. Intramolecular disulfide bonds are conserved among Alt a 1 homologs. Currently, the Alt a 1 structure has no equivalent in the Protein Data Bank. Bioinformatics analyses suggest that the structure is found exclusively in fungi. Four previously reported putative IgE-binding peptides have been located on the Alt a 1 structure. CONCLUSIONS: Alt a 1 has a unique, dimeric ß-barrel structure that appears to define a new protein family with unknown function found exclusively in fungi. The location of IgE antibody-binding epitopes is in agreement with the structural analysis of Alt a 1. The Alt a 1 structure will allow mechanistic structure/function studies and immunologic studies directed toward new forms of immunotherapy for Alternaria species-sensitive allergic patients.

Crystal structures of putative phosphoglycerate kinases from B. anthracis and C. jejuni.

Phosphoglycerate kinase (PGK) is indispensable during glycolysis for anaerobic glucose degradation and energy generation. Here we present comprehensive structure analysis of two putative PGKs from Bacillus anthracis str. Sterne and Campylobacter jejuni in the context of their structural homologs. They are the first PGKs from pathogenic bacteria reported in the Protein Data Bank. The crystal structure of PGK from Bacillus anthracis str. Sterne (BaPGK) has been determined at 1.68 Å while the structure of PGK from Campylobacter jejuni (CjPGK) has been determined at 2.14 Å resolution. The proteins' monomers are composed of two domains, each containing a Rossmann fold, hinged together by a helix which can be used to adjust the relative position between two domains. It is also shown that apo-forms of both BaPGK and CjPGK adopt open conformations as compared to the substrate and ATP bound forms of PGK from other species.