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"Marsden, Brian"
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Windsor Castle : a thousand years of a royal palace
\"As England's largest castle and premier royal residence, Windsor Castle is of outstanding importance: historically, architecturally, artistically and in the life of the nation. This authoritative history of the Castle, the first to be published in 100 years, draws upon new research and primary sources to present a general account of Windsor Castle and its immediate environs from around AD700 to the present day, setting this iconic building against the background of wider social, political and cultural events in the life of the monarchy and the nation. Not only is the book richly illustrated with historical drawings, watercolours and photographs from the Royal Collection and elsewhere, it also includes newly commissioned photography and 3D reconstructions of the Castle at key points in its development, showing how this historic site has changed and evolved over 13 centuries\"-- Provided by publisher.
Book
A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density
In macromolecular crystallography, the rigorous detection of changed states (for example, ligand binding) is difficult unless signal is strong. Ambiguous (‘weak’ or ‘noisy’) density is experimentally common, since molecular states are generally only fractionally present in the crystal. Existing methodologies focus on generating maximally accurate maps whereby minor states become discernible; in practice, such map interpretation is disappointingly subjective, time-consuming and methodologically unsound. Here we report the PanDDA method, which automatically reveals clear electron density for the changed state—even from inaccurate maps—by subtracting a proportion of the confounding ‘ground state’; changed states are objectively identified from statistical analysis of density distributions. The method is completely general, implying new best practice for all changed-state studies, including the routine collection of multiple ground-state crystals. More generally, these results demonstrate: the incompleteness of atomic models; that single data sets contain insufficient information to model them fully; and that accuracy requires further map-deconvolution approaches.
Building a ligand into a weak region of an electron density map of a protein is a subjective process. Here, the authors present a new method to obtain a clear electron density for a bound ligand based on multi-crystal experiments and 3D background correction.
Journal Article
Mapping tenascin-C interaction with toll-like receptor 4 reveals a new subset of endogenous inflammatory triggers
Pattern recognition underpins innate immunity; the accurate identification of danger, including infection, injury, or tumor, is key to an appropriately targeted immune response. Pathogen detection is increasingly well defined mechanistically, but the discrimination of endogenous inflammatory triggers remains unclear. Tenascin-C, a matrix protein induced upon tissue damage and expressed by tumors, activates toll-like receptor 4 (TLR4)-mediated sterile inflammation. Here we map three sites within tenascin-C that directly and cooperatively interact with TLR4. We also identify a conserved inflammatory epitope in related proteins from diverse families, and demonstrate that its presence targets molecules for TLR detection, while its absence enables escape of innate immune surveillance. These data reveal a unique molecular code that defines endogenous proteins as inflammatory stimuli by marking them for recognition by TLRs.
Although detection of pathogens by pattern recognition receptors is increasingly well defined, recognition of endogenous triggers remains poorly understood. By examining the interface between tenascin-C and TLR4, the authors identify a molecular code that identifies endogenous proteins as inflammatory stimuli.
Journal Article
A data science roadmap for open science organizations engaged in early-stage drug discovery
The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.
Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI’s potential in the field.
Journal Article
Structures of ABCB10, a human ATP-binding cassette transporter in apo- and nucleotide-bound states
ABCB10 is one of the three ATP-binding cassette (ABC) transporters found in the inner membrane of mitochondria. In mammals ABCB10 is essential for erythropoiesis, and for protection of mitochondria against oxidative stress. ABCB10 is therefore a potential therapeutic target for diseases in which increased mitochondrial reactive oxygen species production and oxidative stress play a major role. The crystal structure of apo-ABCB10 shows a classic exporter fold ABC transporter structure, in an open-inwards conformation, ready to bind the substrate or nucleotide from the inner mitochondrial matrix or membrane. Unexpectedly, however, ABCB10 adopts an open-inwards conformation when complexed with nonhydrolysable ATP analogs, in contrast to other transporter structures which adopt an open-outwards conformation in complex with ATP. The three complexes of ABCB10/ATP analogs reported here showed varying degrees of opening of the transport substrate binding site, indicating that in this conformation there is some flexibility between the two halves of the protein. These structures suggest that the observed plasticity, together with a portal between two helices in the transmembrane region of ABCB10, assist transport substrate entry into the substrate binding cavity. These structures indicate that ABC transporters may exist in an open-inwards conformation when nucleotide is bound. We discuss ways in which this observation can be aligned with the current views on mechanisms of ABC transporters.
Journal Article
Linking genotypes database with locus-specific database and genotype–phenotype correlation in phenylketonuria
The wide range of metabolic phenotypes in phenylketonuria is due to a large number of variants causing variable impairment in phenylalanine hydroxylase function. A total of 834 phenylalanine hydroxylase gene variants from the locus-specific database PAHvdb and genotypes of 4181 phenylketonuria patients from the BIOPKU database were characterized using FoldX, SIFT Blink, Polyphen-2 and SNPs3D algorithms. Obtained data was correlated with residual enzyme activity, patients' phenotype and tetrahydrobiopterin responsiveness. A descriptive analysis of both databases was compiled and an interactive viewer in PAHvdb database was implemented for structure visualization of missense variants. We found a quantitative relationship between phenylalanine hydroxylase protein stability and enzyme activity (r(s) = 0.479), between protein stability and allelic phenotype (r(s) = -0.458), as well as between enzyme activity and allelic phenotype (r(s) = 0.799). Enzyme stability algorithms (FoldX and SNPs3D), allelic phenotype and enzyme activity were most powerful to predict patients' phenotype and tetrahydrobiopterin response. Phenotype prediction was most accurate in deleterious genotypes (≈ 100%), followed by homozygous (92.9%), hemizygous (94.8%), and compound heterozygous genotypes (77.9%), while tetrahydrobiopterin response was correctly predicted in 71.0% of all cases. To our knowledge this is the largest study using algorithms for the prediction of patients' phenotype and tetrahydrobiopterin responsiveness in phenylketonuria patients, using data from the locus-specific and genotypes database.
Journal Article
Pathogenic Interleukin-10 Receptor Alpha Variants in Humans — Balancing Natural Selection and Clinical Implications
Balancing natural selection is a process by which genetic variants arise in populations that are beneficial to heterozygous carriers, but pathogenic when homozygous. We systematically investigated the prevalence, structural, and functional consequences of pathogenic
IL10RA
variants that are associated with monogenic inflammatory bowel disease. We identify 36 non-synonymous and non-sense variants in the
IL10RA
gene. Since the majority of these
IL10RA
variants have not been functionally characterized, we performed a systematic screening of their impact on STAT3 phosphorylation upon IL-10 stimulation. Based on the geographic accumulation of confirmed pathogenic
IL10RA
variants in East Asia and in Northeast China, the distribution of infectious disorders worldwide, and the functional evidence of IL-10 signaling in the pathogenesis, we identify
Schistosoma japonicum
infection as plausible selection pressure driving variation in
IL10RA
. Consistent with this is a partially augmented IL-10 response in peripheral blood mononuclear cells from heterozygous variant carriers. A parasite-driven heterozygote advantage through reduced IL-10 signaling has implications for health care utilization in regions with high allele frequencies and potentially indicates pathogen eradication strategies that target IL-10 signaling.
Graphical abstract
Journal Article
Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at
https://github.com/oxpig/Fragmenstein
.
Scientific contribution
This work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.
Graphical Abstract
Journal Article
Persistence of SARS-CoV-2 antibodies over 18 months following infection: UK Biobank COVID-19 Serology Study
BackgroundLittle is known about the persistence of antibodies after the first year following SARS-CoV-2 infection. We aimed to determine the proportion of individuals that maintain detectable levels of SARS-CoV-2 antibodies over an 18-month period following infection.MethodsPopulation-based prospective study of 20 000 UK Biobank participants and their adult relatives recruited in May 2020. The proportion of SARS-CoV-2 cases testing positive for immunoglobulin G (IgG) antibodies against the spike protein (IgG-S), and the nucleocapsid protein (IgG-N), was calculated at varying intervals following infection.ResultsOverall, 20 195 participants were recruited. Their median age was 56 years (IQR 39–68), 56% were female and 88% were of white ethnicity. The proportion of SARS-CoV-2 cases with IgG-S antibodies following infection remained high (92%, 95% CI 90%–93%) at 6 months after infection. Levels of IgG-N antibodies following infection gradually decreased from 92% (95% CI 88%–95%) at 3 months to 72% (95% CI 70%–75%) at 18 months. There was no strong evidence of heterogeneity in antibody persistence by age, sex, ethnicity or socioeconomic deprivation.ConclusionThis study adds to the limited evidence on the long-term persistence of antibodies following SARS-CoV-2 infection, with likely implications for waning immunity following infection and the use of IgG-N in population surveys.
Journal Article
Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK
Following primary SARS-CoV-2 vaccination, whether boosters or breakthrough infections provide greater protection against SARS-CoV-2 infection is incompletely understood. Here we investigated SARS-CoV-2 antibody correlates of protection against new Omicron BA.4/5 (re-)infections and anti-spike IgG antibody trajectories after a third/booster vaccination or breakthrough infection following second vaccination in 154,149 adults ≥18 y from the United Kingdom general population. Higher antibody levels were associated with increased protection against Omicron BA.4/5 infection and breakthrough infections were associated with higher levels of protection at any given antibody level than boosters. Breakthrough infections generated similar antibody levels to boosters, and the subsequent antibody declines were slightly slower than after boosters. Together our findings show breakthrough infection provides longer-lasting protection against further infections than booster vaccinations. Our findings, considered alongside the risks of severe infection and long-term consequences of infection, have important implications for vaccine policy.
The duration and strength of protection against SARS-CoV-2 infection resulting from a booster vaccine dose or breakthrough infection are not well understood. This study uses data from the UK COVID-19 Infection Survey to investigate correlates of protection against Omicron BA.4/5 infection and assess antibody responses to booster vaccination and breakthrough infections.
Journal Article