Data management in the modern structural biology and biomedical research environment

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.

XRRpred: Accurate Predictor of Crystal Structure Quality from Protein Sequence

MOTIVATION

X-ray crystallography was used to produce nearly 90% of protein structures. These efforts were supported by numerous sequence-based tools that accurately predict crystallizable proteins. However, protein structures vary widely in their quality, typically measured with resolution and R-free. This impacts the ability to use these structures for some applications including rational drug design and molecular docking and motivates development of methods that accurately predict structure quality.

RESULTS

We introduce XRRpred, the first predictor of the resolution and R-free values from protein sequences. XRRpred relies on original sequence profiles, hand-crafted features, empirically selected and parametrized regressors, and modern resampling techniques. Using an independent test dataset, we show that XRRpred provides accurate predictions of resolution and R-free. We demonstrate that XRRpred's predictions correctly model relationship between the resolution and R-free and reproduce structure quality relations between structural classes of proteins. We also show that XRRpred significantly outperforms indirect alternative ways to predict the structure quality that include predictors of crystallization propensity and an alignment-based approach. XRRpred is available as a convenient webserver that allows batch predictions and offers informative visualization of the results.

AVAILABILITY

http://biomine.cs.vcu.edu/servers/XRRPred/.

Digital Management Systems in Academic Health Sciences Laboratories: A Scoping Review

Good laboratory practices (GLP) increase the quality and traceability of results in health sciences research. However, factors such as high staff turnover, insufficient resources, and a lack of training for managers may limit their implementation in research and academic laboratories. This Scoping Review aimed to identify digital tools for managing academic health sciences and experimental medicine laboratories and their relationship with good practices. Following the PRISMA-ScR 2018 criteria, a search strategy was conducted until April 2021 in the databases PUBMED, Web of Sciences, and Health Virtual Library. A critical appraisal of the selected references was conducted, followed by data charting. The search identified twenty-one eligible articles, mainly originated from high-income countries, describing the development and/or implementation of thirty-two electronic management systems. Most studies described software functionalities, while nine evaluated and discussed impacts on management, reporting both improvements in the workflow and system limitations during implementation. In general, the studies point to a contribution to different management issues related to GLP principles. In conclusion, this review identified evolving evidence that digital laboratory management systems may represent important tools in compliance with the principles of good practices in experimental medicine and health sciences research.

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.

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.

Molecular determinants of vascular transport of dexamethasone in COVID-19 therapy

Dexamethasone, a widely used corticosteroid, has recently been reported as the first drug to increase the survival chances of patients with severe COVID-19. Therapeutic agents, including dexamethasone, are mostly transported through the body by binding to serum albumin. Here, the first structure of serum albumin in complex with dexamethasone is reported. Dexamethasone binds to drug site 7, which is also the binding site for commonly used nonsteroidal anti-inflammatory drugs and testosterone, suggesting potentially problematic binding competition. This study bridges structural findings with an analysis of publicly available clinical data from Wuhan and suggests that an adjustment of the dexamethasone regimen should be further investigated as a strategy for patients affected by two major COVID-19 risk factors: low albumin levels and diabetes.

Molecular determinants of vascular transport of dexamethasone in COVID-19 therapy

Dexamethasone, a widely used corticosteroid, has recently been reported as the first drug to increase the survival chances of patients with severe COVID-19. Therapeutic agents, including dexamethasone, are mostly transported through the body by binding to serum albumin. Herein, we report the first structure of serum albumin in complex with dexamethasone. We show that it binds to Drug Site 7, which is also the binding site for commonly used nonsteroidal anti-inflammatory drugs and testosterone, suggesting potentially problematic binding competition. This study bridges structural findings with our analysis of publicly available clinical data from Wuhan and suggests that an adjustment of dexamethasone regimen should be considered for patients affected by two major COVID-19 risk-factors: low albumin levels and diabetes.

Albumin-Based Transport of Nonsteroidal Anti-Inflammatory Drugs in Mammalian Blood Plasma

Every day, hundreds of millions of people worldwide take nonsteroidal anti-inflammatory drugs (NSAIDs), often in conjunction with multiple other medications. In the bloodstream, NSAIDs are mostly bound to serum albumin (SA). We report the crystal structures of equine serum albumin complexed with four NSAIDs (ibuprofen, ketoprofen, etodolac, and nabumetone) and the active metabolite of nabumetone (6-methoxy-2-naphthylacetic acid, 6-MNA). These compounds bind to seven drug-binding sites on SA. These sites are generally well-conserved between equine and human SAs, but ibuprofen binds to both SAs in two drug-binding sites, only one of which is common. We also compare the binding of ketoprofen by equine SA to binding of it by bovine and leporine SAs. Our comparative analysis of known SA complexes with FDA-approved drugs clearly shows that multiple medications compete for the same binding sites, indicating possibilities for undesirable physiological effects caused by drug-drug displacement or competition with common metabolites. We discuss the consequences of NSAID binding to SA in a broader scientific and medical context, particularly regarding achieving desired therapeutic effects based on an individual's drug regimen.

Reporting on the future of integrative structural biology ORAU workshop

Integrative and hybrid methods have the potential to bridge long-standing knowledge gaps in structural biology. These methods will have a prominent role in the future of the field as we make advances toward a complete, unified representation of biology that spans the molecular and cellular scales. The Department of Physics and Astronomy at Clemson University hosted The Future of Integrative Structural Biology workshop on April 29, 2017 and partially sponsored by partially sponsored by a program of the Oak Ridge Associated Universities (ORAU). The workshop brought experts from multiple structural biology disciplines together to discuss near-term steps toward the goal of a molecular atlas of the cell. The discussion focused on the types of structural data that should be represented, how this data should be represented, and how the time domain might be incorporated into such an atlas. The consensus was that an explorable, map-like Virtual Cell, containing both spatial and temporal data bridging the atomic and cellular length scales obtained by multiple experimental methods, represents the best path toward a complete atlas of the cell.

Structural and biochemical analysis of Bacillus anthracis prephenate dehydrogenase reveals an unusual mode of inhibition by tyrosine via the ACT domain

Tyrosine biosynthesis via the shikimate pathway is absent in humans and other animals, making it an attractive target for next-generation antibiotics, which is increasingly important due to the looming proliferation of multidrug-resistant pathogens. Tyrosine biosynthesis is also of commercial importance for the environmentally friendly production of numerous compounds, such as pharmaceuticals, opioids, aromatic polymers, and petrochemical aromatics. Prephenate dehydrogenase (PDH) catalyzes the penultimate step of tyrosine biosynthesis in bacteria: the oxidative decarboxylation of prephenate to 4-hydroxyphenylpyruvate. The majority of PDHs are competitively inhibited by tyrosine and consist of a nucleotide-binding domain and a dimerization domain. Certain PDHs, including several from pathogens on the World Health Organization priority list of antibiotic-resistant bacteria, possess an additional ACT domain. However, biochemical and structural knowledge was lacking for these enzymes. In this study, we successfully established a recombinant protein expression system for PDH from Bacillus anthracis (BaPDH), the causative agent of anthrax, and determined the structure of a BaPDH ternary complex with NAD⁺ and tyrosine, a binary complex with tyrosine, and a structure of an isolated ACT domain dimer. We also conducted detailed kinetic and biophysical analyses of the enzyme. We show that BaPDH is allosterically regulated by tyrosine binding to the ACT domains, resulting in an asymmetric conformation of the BaDPH dimer that sterically prevents prephenate binding to either active site. The presented mode of allosteric inhibition is unique compared to both the competitive inhibition established for other PDHs and to the allosteric mechanisms for other ACT-containing enzymes. This study provides new structural and mechanistic insights that advance our understanding of tyrosine biosynthesis in bacteria. ENZYMES: Prephenate dehydrogenase from Bacillus anthracis (PDH): EC database ID: 1.3.1.12. DATABASES: Coordinates and structure factors have been deposited in the Protein Data Bank (PDB) with accession numbers PDB ID: 6U60 (BaPDH complex with NAD⁺ and tyrosine), PDB ID: 5UYY (BaPDH complex with tyrosine), and PDB ID: 5V0S (BaPDH isolated ACT domain dimer). The diffraction images are available at http://proteindiffraction.org with DOIs: https://doi.org/10.18430/M35USC, https://doi.org/10.18430/M35UYY, and https://doi.org/10.18430/M35V0S.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

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.

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity

X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.

Structural biology meets data science: does anything change?

Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.

Structural, Biochemical, and Evolutionary Characterizations of Glyoxylate/Hydroxypyruvate Reductases Show Their Division into Two Distinct Subfamilies

The d-2-hydroxyacid dehydrogenase (2HADH) family illustrates a complex evolutionary history with multiple lateral gene transfers and gene duplications and losses. As a result, the exact functional annotation of individual members can be extrapolated to a very limited extent. Here, we revise the previous simplified view on the classification of the 2HADH family; specifically, we show that the previously delineated glyoxylate/hydroxypyruvate reductase (GHPR) subfamily consists of two evolutionary separated GHRA and GHRB subfamilies. We compare two representatives of these subfamilies from Sinorhizobium meliloti (SmGhrA and SmGhrB), employing a combination of biochemical, structural, and bioinformatics approaches. Our kinetic results show that both enzymes reduce several 2-ketocarboxylic acids with overlapping, but not equivalent, substrate preferences. SmGhrA and SmGhrB show highest activity with glyoxylate and hydroxypyruvate, respectively; in addition, only SmGhrB reduces 2-keto-d-gluconate, and only SmGhrA reduces pyruvate (with low efficiency). We present nine crystal structures of both enzymes in apo forms and in complexes with cofactors and substrates/substrate analogues. In particular, we determined a crystal structure of SmGhrB with 2-keto-d-gluconate, which is the biggest substrate cocrystallized with a 2HADH member. The structures reveal significant differences between SmGhrA and SmGhrB, both in the overall structure and within the substrate-binding pocket, offering insight into the molecular basis for the observed substrate preferences and subfamily differences. In addition, we provide an overview of all GHRA and GHRB structures complexed with a ligand in the active site.

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background

Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope.

Results

We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/fDETECT/.

Conclusions

fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-of-the-art tools and is especially suitable for the analysis of large protein sets.

Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.

Generating enzyme and radical-mediated bisubstrates as tools for investigating Gcn5-related N-acetyltransferases

Gcn5-related N-acetyltransferases (GNATs) are found in all kingdoms of life and catalyze important acyl transfer reactions in diverse cellular processes. While many 3D structures of GNATs have been determined, most do not contain acceptor substrates in their active sites. To expand upon existing crystallographic strategies for improving acceptor-bound GNAT structures, we synthesized peptide substrate analogs and reacted them with CoA in PA4794 protein crystals. We found two separate mechanisms for bisubstrate formation: (a) a novel X-ray induced radical-mediated alkylation of CoA with an alkene peptide and (b) direct alkylation of CoA with a halogenated peptide. Our approach is widely applicable across the GNAT superfamily and can be used to improve the success rate of obtaining liganded structures of other acyltransferases.

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.

Advanced Methods of Protein Crystallization

This chapter provides a review of different advanced methods that help to increase the success rate of a crystallization project, by producing larger and higher quality single crystals for determination of macromolecular structures by crystallographic methods. For this purpose, the chapter is divided into three parts. The first part deals with the fundamentals for understanding the crystallization process through different strategies based on physical and chemical approaches. The second part presents new approaches involved in more sophisticated methods not only for growing protein crystals but also for controlling the size and orientation of crystals through utilization of electromagnetic fields and other advanced techniques. The last section deals with three different aspects: the importance of microgravity, the use of ligands to stabilize proteins, and the use of microfluidics to obtain protein crystals. All these advanced methods will allow the readers to obtain suitable crystalline samples for high-resolution X-ray and neutron crystallography.

Insight into the 3D structure and substrate specificity of previously uncharacterized GNAT superfamily acetyltransferases from pathogenic bacteria

Members of the Gcn5-related N-acetyltransferase (GNAT) superfamily catalyze the acetylation of a wide range of small molecule and protein substrates. Due to their abundance in all kingdoms of life and diversity of their functions, they are implicated in many aspects of eukaryotic and prokaryotic physiology. Although numerous GNATs have been identified thus far, many remain structurally and functionally uncharacterized. The elucidation of their structures and functions is critical for broadening our knowledge of this diverse and important superfamily. In this work, we present the structural and kinetic analyses of two previously uncharacterized bacterial acetyltransferases - SACOL1063 from Staphylococcus aureus strain COL and CD1211 from Clostridium difficile strain 630. Our structures of SACOL1063 show substantial flexibility of a loop that is likely responsible for substrate recognition and binding compared to structures of other homologs. In the CoA complex structure, we found two CoA molecules bound in both the canonical AcCoA/CoA-binding site and the acceptor-substrate-binding site. Our work also provides initial clues regarding the substrate specificity of these two enzymes; however, their native function(s) remain unknown. We found both proteins act as N- rather than O-acetyltransferases and preferentially acetylate l-threonine. The combination of structural and kinetic analyses of these two previously uncharacterized GNATs provides fundamental knowledge and a framework on which future studies can be built to elucidate their native functions.

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.

Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care?

Recently, information availability has become more elaborate and widespread, and treatment decisions are based on a multitude of factors, including imaging, molecular or pathological markers, surgical results, and patient’s preference. In this context, the term “Big Data” evolved also in health care. The “hype” is heavily discussed in literature. In interdisciplinary medical specialties, such as radiation oncology, not only heterogeneous and voluminous amount of data must be evaluated but also spread in different styles across various information systems. Exactly this problem is also referred to in many ongoing discussions about Big Data – the “three V’s”: volume, velocity, and variety. We reviewed 895 articles extracted from the NCBI databases about current developments in electronic clinical data management systems and their further analysis or postprocessing procedures. Few articles show first ideas and ways to immediately make use of collected data, particularly imaging data. Many developments can be noticed in the field of clinical trial or analysis documentation, mobile devices for documentation, and genomics research. Using Big Data to advance medical research is definitely on the rise. Health care is perhaps the most comprehensive, important, and economically viable field of application.

The impact of structural genomics: the first quindecennial

The period 2000-2015 brought the advent of high-throughput approaches to protein structure determination. With the overall funding on the order of $2 billion (in 2010 dollars), the structural genomics (SG) consortia established worldwide have developed pipelines for target selection, protein production, sample preparation, crystallization, and structure determination by X-ray crystallography and NMR. These efforts resulted in the determination of over 13,500 protein structures, mostly from unique protein families, and increased the structural coverage of the expanding protein universe. SG programs contributed over 4400 publications to the scientific literature. The NIH-funded Protein Structure Initiatives alone have produced over 2000 scientific publications, which to date have attracted more than 93,000 citations. Software and database developments that were necessary to handle high-throughput structure determination workflows have led to structures of better quality and improved integrity of the associated data. Organized and accessible data have a positive impact on the reproducibility of scientific experiments. Most of the experimental data generated by the SG centers are freely available to the community and has been utilized by scientists in various fields of research. SG projects have created, improved, streamlined, and validated many protocols for protein production and crystallization, data collection, and functional analysis, significantly benefiting biological and biomedical research.

X-ray crystallography over the past decade for novel drug discovery - where are we heading next?

INTRODUCTION

Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology.

AREAS COVERED

This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions.

EXPERT OPINION

X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.

100 Years later: Celebrating the contributions of x-ray crystallography to allergy and clinical immunology

Current knowledge of molecules involved in immunology and allergic disease results from the significant contributions of x-ray crystallography, a discipline that just celebrated its 100th anniversary. The histories of allergens and x-ray crystallography are intimately intertwined. The first enzyme structure to be determined was lysozyme, also known as the chicken food allergen Gal d 4. Crystallography determines the exact 3-dimensional positions of atoms in molecules. Structures of molecular complexes in the disciplines of immunology and allergy have revealed the atoms involved in molecular interactions and mechanisms of disease. These complexes include peptides presented by MHC class II molecules, cytokines bound to their receptors, allergen-antibody complexes, and innate immune receptors with their ligands. The information derived from crystallographic studies provides insights into the function of molecules. Allergen function is one of the determinants of environmental exposure, which is essential for IgE sensitization. Proteolytic activity of allergens or their capacity to bind LPSs can also contribute to allergenicity. The atomic positions define the molecular surface that is accessible to antibodies. In turn, this surface determines antibody specificity and cross-reactivity, which are important factors for the selection of allergen panels used for molecular diagnosis and the interpretation of clinical symptoms. This review celebrates the contributions of x-ray crystallography to clinical immunology and allergy, focusing on new molecular perspectives that influence the diagnosis and treatment of allergic diseases.

Data to knowledge: how to get meaning from your result

Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

The future of crystallography in drug discovery

INTRODUCTION

X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule-ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery.

AREAS COVERED

This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of 'Big Data' analysis, biochemical experimental design and structure-based drug discovery.

EXPERT OPINION

Although X-ray crystallography is one of the most detailed 'microscopes' available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses.