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When biomedical crises strike, structural biologists worldwide respond by determining the structures of relevant proteins and their complexes, resulting in an avalanche of data that can be overwhelming without a resource designed to classify, annotate and validate them. An advanced information system is necessary to extract and infer knowledge from a deluge of uncurated and disjointed data and publications., 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.

In 2014–2017 an investigation was carried out into the occurrence of fungal storage diseases of five apple varieties (Red Jonaprince, Gala, Golden Delicious, Gloster and Ligol) in the Sandomierz orchard region. The fruit was stored at a CA cold storage room with ULO controlled atmosphere for six months. Occurrence of eight storage diseases was found. The most frequently occurring disease was bull’s eye rot and the losses caused thereby were even 24% of the affected fruit. The cultivars most susceptible to this disease were the Golden Delicious and Ligol apples; the least susceptible were the Gloster ones. The apples were significantly less affected by the fungi that cause brown rot, grey mould rot, blue mould rot and apple scab. Very seldom were the symptoms of calyx end rot, mouldy core and core rot, and anthracnose. Varying severity of infection of the varieties was noted in each season of observation.

The COVID‐19 pandemic has triggered numerous scientific activities aimed at understanding the SARS‐CoV‐2 virus and ultimately developing treatments. Structural biologists have already determined hundreds of experimental X‐ray, cryo‐EM, and NMR structures of proteins and nucleic acids related to this coronavirus, and this number is still growing. To help biomedical researchers, who may not necessarily be experts in structural biology, navigate through the flood of structural models, we have created an online resource, covid19.bioreproducibility.org, that aggregates expert‐verified information about SARS‐CoV‐2‐related macromolecular models. In this paper, we describe this web resource along with the suite of tools and methodologies used for assessing the structures presented therein. This article is protected by copyright. All rights reserved.

The ripening process of two grape varieties in the vineyard located in the Sandomierz region was examined. In 2015, the ‘Regent’ and the ‘Sibera’ varieties reached physiological ripeness on 30th September and 3rd October, respectively. On both harvest dates, in addition to soluble solids content, titratable acidity, and pH, the macronutrient content was also determined. In the phase of physiological ripeness, the ‘Sibera’ variety showed higher acidity (1.02 g · 100 mL−1) compared with the ‘Regent’ (0.87 g · 100 mL−1). A higher soluble solids content was found in the ‘Regent’ (20.4°Brix), and slightly lower in the ‘Sibera’ (18.1°Brix). The must of the ‘Regent’ had a higher pH (3.5). This variety also had higher macronutrient contents (K, Ca and Mg). Grapes harvested after 3 weeks (late harvest) showed higher values of sugars, pH, K, and P for both varieties. However, their titratable acidity content, and Ca and Mg contents decreased. Microscopic examination showed differences in mycelial growth of Botrytis cinerea on grape bunches for both of the varieties left for the late harvest. The mycelial growth promoted faster dehydration of the ‘Sibera’ berries. Spot chemical analyses of ‘Sibera’ berry peel performed using EDS (energy dispersive spectroscopy) showed the occurrence of elevated contents of potassium, phosphorus, sulphur, magnesium and silicon. Around skin cracks, sugar crystals as well as hard-to-identify microcrystals were formed containing potassium. On the surface of the ‘Regent’ berries, potassium, and traces of phosphorus, silicon, magnesium, sulphur and calcium were found.

Intense X-rays available at powerful synchrotron beamlines provide macromolecular crystallographers with an incomparable tool for investigating biological phenomena on an atomic scale. The resulting insights into the mechanism's underlying biological processes have played an essential role and shaped biomedical sciences during the last 30 years, considered the "golden age" of structural biology. In this review, we analyze selected aspects of the impact of synchrotron radiation on structural biology. Synchrotron beamlines have been used to determine over 70% of all macromolecular structures deposited into the Protein Data Bank (PDB). These structures were deposited by over 13,000 different research groups. Interestingly, despite the impressive advances in synchrotron technologies, the median resolution of macromolecular structures determined using synchrotrons has remained constant throughout the last 30 years, at about 2 A. Similarly, the median times from the data collection to the deposition and release have not changed significantly. We describe challenges to reproducibility related to recording all relevant data and metadata during the synchrotron experiments, including diffraction images. Finally, we discuss some of the recent opinions suggesting a diminishing importance of X-ray crystallography due to impressive advances in Cryo-EM and theoretical modeling. We believe that synchrotrons of the future will increasingly evolve towards a life science center model, where X-ray crystallography, Cryo-EM, and other experimental and computational resources and knowledge are encompassed within a versatile research facility. The recent response of crystallographers to the COVID-19 pandemic suggests that X-ray crystallography conducted at synchrotron beamlines will continue to play an essential role in structural biology and drug discovery for years to come.

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.

The structure of serum albumin in complex with dexamethasone reveals why the drug may not always help COVID-19 patients., 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.

The aim of the study was to determine the effect of the amount of nitrogen dosed in supercritical state to polymer melt on structure and physical properties of large-scale molded pieces from polyamide PA 6 GF30, obtained in the process of microcellular injection molding with gas dosage using MuCell® method. Apart from the reduction of the density at a level of about 15 % for samples with the highest nitrogen content, the increase of the impact resistance of samples PA 6 GF30 was observed. The other investigated mechanical properties were maintained at a good level.

The current-voltage curves of a double strand DNA nanowire connected to armchair graphene nanoribbon leads are investigated using the tight-binding Hamiltonian model and the Landauer-Buttiker formalism. The nonlinear behavior of the current-voltage for different lengths of the DNA nanowire is calculated and interpreted through the corresponding density of states and transmission probability. The behavior of the current-voltage changes as the temperature or width of the leads varies and is also affected by the randomization of parallel and perpendicular hopping terms in the DNA. Around zero bias of the leads, the current-voltage depends on the dimerization effects of parallel hopping in the device.

Vibrio cholerae, the causative pathogen of the life-threatening infection cholera, encodes two copies of β-ketoacyl-acyl carrier protein synthase III (vcFabH1 and vcFabH2). vcFabH1 and vcFabH2 are pathogenic proteins associated with fatty acid synthesis, lipid metabolism, and potential applications in biofuel production. Our biochemical assays characterize vcFabH1 as exhibiting specificity for acetyl-CoA and CoA thioesters with short acyl chains, similar to that observed for FabH homologs found in most gram-negative bacteria. vcFabH2 prefers medium chain-length acyl-CoA thioesters, particularly octanoyl-CoA, which is a pattern of specificity rarely seen in bacteria. Structural characterization of one vcFabH1 and six vcFabH2 structures determined in either apo form or in complex with acetyl-CoA/octanoyl-CoA indicate that the substrate-binding pockets of vcFabH1 and vcFabH2 are of different sizes, accounting for variations in substrate chain-length specificity. An unusual and unique feature of vcFabH2 is its C-terminal fragment that interacts with both the substrate-entrance loop and the dimer interface of the enzyme. Our discovery of the pattern of substrate specificity of both vcFabH1 and vcFabH2 can potentially aid the development of novel antibacterial agents against V. cholerae. Additionally, the distinctive substrate preference of FabH2 in V. cholerae and related facultative anaerobes conceivably make it an attractive component of genetically engineered bacteria used for commercial biofuel production.

The possibility of the s-wave superconductive state in armchair graphene nanoribbons is studied within the attractive Hubbard model, standard BCS theory, and Green’s function approach. Bogoliubov de Gennes equations are derived for this system in the singlet state. A nonzero critical temperature is found which depends on the width of the system. This critical temperature decreases as the width of the system increases and for sufficiently large widths, its value reaches a constant amount which could be taken as the critical temperature of the graphene sheet. The critical temperature also depends on band-filling which, around half band-filling, shows a maximum. However, at half-filling, it drops to a lower value less than its maximum.

In the framework of the Green's functions approach, random tight-binding model and using the coherent potential approximation, electronic characteristics of the bilayer graphene are investigated by exploring various forms of substitutional doping of a single or both layers of the system by either boron and (or) nitrogen atoms. The results for displacement of the Fermi level resemble the behavior of acceptor or donor doping in a conventional semiconductor, dependent on the impurity type and concentration. The particular pattern of doping of just one layer with one impurity type is most efficient for opening a gap within the energy bands which could be tuned directly by impurity concentration. Doping both layers at the same time, each with one impurity type, leads to an anomaly whereby the gap decreases with increasing impurity concentration.

The Kubo formula is used to extract the electrical conductivity (EC) of different diameters of doped zigzag carbon nanotubes and their corresponding unzipped armchair graphene nanoribbons, as a function of temperature and chemical potential, within the tight-binding Hamiltonian model and Green's functions approach. The results reveal more sensitivity to temperature for semiconducting systems in addition to a decrease in EC of all systems with increasing cross-sections.

Nearly 80% of all burns include the hands of affected individuals. Skin grafting is the gold standard in burns treatment, but in the case of the burn wound bed, it may require the necessity of utilising skin substitutes to facilitate closure. The aim of this study is to assess the impact of a porcine-derived wound dressing (Oasis™) for application to hand burns compared to a synthetic dressing (Suprathel™). Comparative assessments were made, including the time to heal, quality of healing and pain intensity. A retrospective, unblinded, matching pair case-control of hand burns was performed. A control group of 24 patients was treated with Suprathel dressing, and a study group of six patients underwent application of the Oasis dressing. The wound healing process was evaluated by taking histopathological specimens and also utilising the Bates-Jensen Wound Assessment Tool. A 10-cm Visual Analogue Scale (VAS) was used for pain assessment. Other parameters measured included dressing loss because of infection and the need of rehabilitation. The progress of wound healing on the fourth day in the study group was 30%. A decrease in the level of pain was recorded on the fourth day after surgery. There was a decrease of 5% in the risk of rehabilitation in the treatment group.

Through the Green's function formalism and tight-binding Hamiltonian model calculations, the temperature dependent electronic thermal conductivity (TC) for different diameters of zigzag carbon nanotubes and their corresponding unzipped armchair graphene nanoribbons is calculated. All functional temperature dependencies bear crossovers, for which, at higher temperatures, nanotubes have a slightly higher TC than their derived nanoribbons, while below that crossover, both systems exhibit a significant coincidence over a moderate range of lower temperatures. Noticeably, TC decreases with increasing the width or diameter of the corresponding systems. Also, at low temperatures TC is proportional to the density of states around the Fermi level, and thus increasing for metal or semiconductors of narrower gap cases.

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 4,400 publications to the scientific literature. The NIH-funded Protein Structure Initiatives (PSI) alone have produced over 2,000 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.

The band structure, density of states (DOS), and electrical conductivity (EC) of different configurations of a double-strand poly-GC-poly-AT DNA-like nanowire are investigated within the tight-binding Hamiltonian model and the Green's function formalism. Three different configurations of the double-strand DNA-like nanowire were considered: infinite, finite, and cyclic. The change of behavior in the band structure due to the introduction of dimerization effects of the longitudinal hopping terms was studied. Without dimerization, the DNA-like nanowire behaves as a conductor and with dimerization, a semiconductor behavior was observed. This behavior change was also verified through the calculation of the DOS via the Green's function formalism. The effects of the finite size of the DNA-like nanowire and also the effect of length increase on the EC of the system were studied using Kubo formula. There exists a direct relationship between EC and length of DNA-like nanowire. As length increases, so does the EC of the DNA-like nanowire. This information can be useful in both theoretical research and experimental development of DNA-based molecular electronics.

We examine a magnetic moment subject to a static external magnetic field and a time dependent driving magnetic field. Taking the system in the highly anisotropic limit, like that of a thin film, we lower the complexity of the system so that it may be analyzed for transitions to chaos analytically using the Melnikov method. Guided by the calculation of the Melnikov bifurcation functions we computationally calculated the Lyapunov characteristic exponents for several lines in parameter space and find that the Melnikov method is a good predictor of chaotic motion for sufficiently small external fields. Motion plots and Poincare sections are also plotted for several parameter space points to observe the transitions to and from chaos around the calculated Melnikov bifurcations functions.

The electronic behavior of the long disordered DNA nanowires in both dry and wet conditions is investigated through the band structure and density of states of a tight-binding Hamiltonian model for π-electrons of the backbone, using Green׳s functions approach. For a chosen set of parameters in the dry case, semiconducting behavior is reproduced. It is also shown that for sufficiently long strands, the order of the base pairs has no noticeable effect on the energy band-gap. Moreover, this semiconducting duplex shows metallic tendencies when interacting with the environment of polar molecules.

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.

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.

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.

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 accessibleviathe 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.

The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them "correctly." (In this case, "correct" was defined as placing more than 70 % of the alpha carbon atoms in the model within 2 Å of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25 % sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers.

The Landau–Lifshitz–Gilbert equation is analyzed in the case of a configuration involving easy plane isotropy under the influence of a sinusoidally oscillating magnetic field and a demagnetizing field. Through the use of numerical techniques, chaotic behavior is found and analyzed. By reducing the system to a discrete map (numerically), bifurcation diagrams for the system are computed. The system is found to exhibit a period doubling cascade route to chaos, and it obeys certain convergence rules for chaotic transitions outlined by Feigenbaum. A connection is drawn between the route to chaos and the geometry of the system, and comparisons are made with similar systems. Within the chaotic regime, windows of arbitrarily large period are suspected to exist, and explicitly illustrated and discussed for a period three window.

We examine the motion of a spin subject to a static external field, dynamic demagnetizing fields, and a relatively weak oscillatory driving field. More specifically, we study the initial transient behavior just after all these fields are turned on and present analytic calculations based on an approximation scheme for the motion of the spin. We find that two modes – a mode at the driving frequency and a mode at the natural resonant frequency – play an important role in the initial transient. Because of the nonlinear nature of the spin equations of motion, one also finds, only in the initial transient, modes with doubled frequencies of the initial two modes and modes involving the sum and difference frequencies of the initial two modes. We also examine large amplitude transients in specific cases through numerical calculations.

While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in “traditional” structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.

A nonredundant set of 9081 protein crystal structures in the Protein Data Bank was used to examine the solvent content, the number of polypeptide chains, and the oligomeric states of proteins in crystals as a function of crystal symmetry (as classified by crystal systems and space groups). It was found that there is a correlation between solvent content and crystal symmetry. Surprisingly, proteins crystallizing in lower symmetry systems have lower solvent content compared to those crystallizing in higher symmetry systems. Nevertheless, there is no universal correlation between solvent content and preferences of macromolecules to crystallize in certain space groups. Crystal symmetry as a function of oligomeric state was examined, where trimers, tetramers, and hexamers were found to prefer to crystallize in systems where the oligomer symmetry could be incorporated in the crystal symmetry. Our analysis also shows that the frequency distribution within the enantiomorphous pairs of space groups does not differ significantly, in contrast to previous reports.

Structural characterization of the protein universe is the main mission of Structural Genomics (SG) programs. However, progress in gene sequencing technology, set in motion in the 1990s, has resulted in rapid expansion of protein sequence space — a twelvefold increase in the past seven years. For the SG field, this creates new challenges and necessitates a reassessment of its strategies. Nevertheless, despite the growth of sequence space, at present nearly half of the content of the Swiss-Prot database and over 40% of Pfam protein families can be structurally modeled based on structures determined so far, with SG projects making an increasingly significant contribution. The SG contribution of new Pfam structures nearly doubled from 27.2% in 2003 to 51.6% in 2006.

Structural genomics (SG) initiatives are currently attempting to achieve the high-throughput determination of protein structures on a genome-wide scale. Here we analyze the SG target data that have been publicly released over a period of 16 months to assess the potential of the SG initiatives. We use statistical techniques most commonly applied in epidemiology to describe the dynamics of targets through the experimental SG pipeline. There is no clear bottleneck among the key stages of cloning, expression, purification and crystallization. An SG target will progress through each of these steps with a probability of approximately 45%. Around 80% of targets with diffraction data will yield a crystal structure, and 20% of targets with HSQC spectra will yield an NMR structure. We also find the overlaps among SG targets: 61% of SG protein sequences share at least 30% sequence identity with one or more other SG targets. There is no significant difference in average structure quality among SG structures and other structures in the PDB determined by 'traditional' methods, but on average SG structures are deposited to the PDB twice as quickly after X-ray data collection. Copyright 2004 Wiley-Liss, Inc, English15211505