Biology subjects

Read, R. J.

Publications and source records attributed to Read, R. J..

18 recordsLinked to original sources

bioRxiv2026

Structural basis of coiled-coil domain-mediated inhibition of A. baumannii AbTir NAD+ nucleosidase activity

TIR (Toll/interleukin-1 receptor) domains are central components of innate immunity and cell-death pathways across diverse forms of life. In bacteria, TIR domain-containing proteins participate in diverse antiphage defence systems, several of which rely on NADase activity carried out by TIR domains. This nucleosidase activity is often regulated by additional domains that modulate activation and specificity in response to infection. AbTir, a TIR domain-containing protein from Acinetobacter baumannii, contains an N-terminal CC domain that negatively regulates its NADase activity, but the structural basis of this regulation remains unknown. Here, we used X-ray crystallography to investigate the molecular basis of CC domain-mediated regulation in AbTir. We determined the crystal structure of full-length AbTir in complex with a nanobody, revealing a symmetric AbTir TIR dimer in which only one of the two symmetry-related CC helices is accommodated at the dimer surface, breaking the overall symmetry of the full-length protein. Structural comparisons with other bacterial TIR domains suggest that such TIR dimers likely represent an inactive state, in which the NAD+-binding site has not yet formed. Phylogenetic analysis places AbTir within a family of bacterial TIR proteins associated with defence-related genomic loci, although phage-spot assays performed in the surrogate host Escherichia coli did not detect measurable antiphage activity. Together, these findings reveal an unusual structural mechanism of CC domain-mediated inhibition of a bacterial TIR NADase and expand our understanding of the mechanistic diversity of bacterial TIR proteins.

biochemistry↗

bioRxiv2025

Xtricorder: A likelihood-enhanced self-rotation function and application to a machine-learning enhanced Matthews prediction of asymmetric unit copy number

Analysis of crystallographic diffraction data before phasing gives the crystallographer a first look at the nature of the problem and the context in which the structure determination will be performed. We here report the development of Xtricorder, an application that targets analysis of crystallographic data specifically for likelihood-based phasing. As well as porting many of the analyses previously available but relatively inaccessible in our Phaser codebase, Xtricorder offers a likelihood-enhanced self-rotation function. A novel and intuitive graphical representation of the self-rotation function presents the results for user inspection, and has the added advantage that, in an adapted form, is appropriate for training a convolutional neural network to enhance the standard Matthews analysis and more accurately predict the number of copies in the asymmetric unit. We investigate the usefulness of the likelihood-enhanced self-rotation function in first look analyses, exploring the circumstances under which the self-rotation function results are useful, and discuss the application to AI-generated structure prediction. Synopsis Xtricorder is a new tool for analysing crystallographic data prior to phasing, featuring a likelihood-enhanced self-rotation function and graphical output that aids both user interpretation and machine learning-based prediction of asymmetric unit content.

molecular biology↗

bioRxiv2025

AlphaFold as a Prior: Experimental StructureDetermination Conditioned on a Pretrained Neural Network

Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist in capturing sidechain packing, condition-dependent conformational dynamics, and biomolecular interactions, primarily due to scarcity of high-quality training data. Emerging techniques, including cryo-electron tomography (cryo-ET) and high-throughput crystallography, promise vast new sources of structural data, but translating experimental observations into mechanistically interpretable atomic models remains a key bottleneck. Here, we address these challenges by improving the efficiency of structural analysis through combining experimental measurements with a landmark protein structure prediction method - AlphaFold2. We present an augmentation of AlphaFold2, ROCKET, that refines its predictions using cryo-EM, cryo-ET, and X-ray crystallography data, and demonstrate that this approach captures biologically important structural variation that AlphaFold2 does not. By performing structure optimization in the space of coevolutionary embeddings, rather than Cartesian coordinates, ROCKET automates difficult modeling tasks, such as flips of functional loops and domain rearrangements, that are beyond the scope of current state-of-the-art methods and, in some instances, even manual human modeling. The ability to efficiently sample these barrier-crossing rearrangements unlocks a new horizon for scalable and automated model building. Crucially, ROCKET does not require retraining of AlphaFold2 and is readily adaptable to multimers, ligand-cofolding, and other data modalities. Conversely, our differentiable crystal-lographic and cryo-EM target functions are capable of augmenting other structure prediction methods. ROCKET thus provides an extensible framework for the integration of experimental observables with biomolecular machine learning.

biophysics↗

bioRxiv2024

Likelihood-based interactive local docking into cryo-EM maps in ChimeraX

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 [A]. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation search step enhances signal-to-noise. A Fourier-space likelihood-based local search approach, based on the previously-published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed. SynopsisLikelihood-based cryo-EM docking using our emplace_local software is faster and more sensitive than our related software, em_placement, when the approximate location of a component is known, and is available conveniently through a plugin to the ChimeraX visualization software.

biophysics↗

bioRxiv2022

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterise, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalises a previous measure of the information gained by making the cryo-EM reconstruction.

biophysics↗

bioRxiv2022

Likelihood-based docking of models into cryo-EM maps

Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimised strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores, computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps. SynopsisExploiting analogies to crystallographic molecular replacement, a strategy for docking into cryo-EM maps is informed by calculations of expected log-likelihood-gain scores.

biophysics↗

bioRxiv2022

AlphaFold predictions: great hypotheses but no match for experiment

AI-based methods such as AlphaFold have revolutionized structural biology, often making it possible to predict protein structures with high accuracy. The accuracies of these predictions vary, however, and they do not include ligands, covalent modifications or other environmental factors. Here we focus on very-high-confidence parts of AlphaFold predictions, evaluating how well they can be expected to describe the structure of a protein in a particular environment. We compare predictions with experimental crystallographic maps of the same proteins for 102 crystal structures. In many cases, those parts of AlphaFold predictions that were predicted with very high confidence matched experimental maps remarkably closely. In other cases, these predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. Overall, C atoms in very-high-confidence parts of AlphaFold predictions differed from corresponding crystal structures by a median of 0.6 [A], and about 10% of these differed by more than 2 [A], each about twice the values found for pairs of crystal structures containing the same components but determined in different space groups. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

biochemistry↗

bioRxiv2022

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with AI-based structure prediction methods such as AlphaFold. Here we present an automatic procedure requiring only sequence information and crystallographic data that uses AlphaFold predictions to produce an electron density map and a structural model. Iterating through cycles of structure prediction is a key element of our procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. We applied this procedure to X-ray data for 215 structures released by the Protein Data Bank in a recent 6-month period. In 87% of cases our procedure yielded a model with at least 50% of C atoms matching those in the deposited models within 2[A]. Predictions from our iterative template-guided prediction procedure were more accurate than those obtained without templates. We suggest a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization.

biochemistry↗

bioRxiv2022

Improving AlphaFold modeling using implicit information from experimental density maps

Machine learning prediction algorithms such as AlphaFold1 and RoseTTAFold2 can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy3-6. We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.

biochemistry↗

bioRxiv2021

Assessing the utility of CASP14 models for molecular replacement

The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real-world application. In CASP7, the metric for molecular replacement assessment involved full likelihood-based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood-based rigid-body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined likelihood (LLG) score. This enabled multi-copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative-expected-LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X-ray, NMR or cryo-EM. We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing.

bioinformatics↗

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗

bioRxiv2021

Possible Implications of AlphaFold2 for Crystallographic Phasing by Molecular Replacement

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high- coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. We here use the data from CASP14 to explore the prospect for changes in phasing methods, and in particular to explore the prospects for molecular replacement phasing using in silico models. SynopsisWe discuss the implications of the AlphaFold2 protein structure modelling software for crystallographic phasing strategies.

biophysics↗

bioRxiv2021

Likelihood-based estimation of substructure content from single-wavelength anomalous diffraction (SAD) intensity data

SAD phasing can be challenging when the signal-to-noise ratio is low. In such cases, having an accurate estimate of substructure content can determine whether or not the substructure of anomalous scatterer positions can successfully be determined. We propose a likelihood-based target function to accurately estimate the strength of the anomalous scattering contribution directly from measured intensities, determining a complex correlation parameter relating the Bijvoet mates as a function of resolution. This gives a novel measure of intrinsic anomalous signal. The SAD likelihood target function also accounts for correlated errors in the measurement of intensities from Bijvoet mates, which can arise from the effects of radiation damage. When the anomalous signal is assumed to come primarily from a substructure comprised of one anomalous scatterer with a known value of f" and when the protein composition of the crystal is estimated correctly, the refined complex correlation parameters can be interpreted in terms of the atomic content of the primary anomalous scatterer, before the substructure is known. The maximum likelihood estimation of substructure content was tested on a curated database of 357 SAD cases with useful anomalous signal. The prior estimates of substructure content are highly correlated to the content determined by phasing calculations, with a correlation coefficient (on a log-log basis) of 0.72. SynopsisAn intensity-based likelihood method is provided to estimate scattering from an anomalous substructure considering the effect of measurement errors in Bijvoet pairs and correlations between those errors.

biophysics↗

bioRxiv2020

Adaptive conformational restraints for interactive model rebuilding in Cartesian and torsion space

When building atomic models into weak and/or low-resolution density, a common strategy is to restrain its conformation to that of a higher-resolution model of the same or similar sequence. When doing so, it is important to avoid over-restraining to the reference model in the face of disagreement with the experimental data. The most common strategy for this is the use of "top-out" potentials. These act like simple harmonic restraints within a defined range, but gradually weaken when the deviation between the model and reference grows larger than a defined transition point. In each current implementation, the rate at which the potential flattens beyond the transition region follows a fixed form - although the form chosen varies between implementations. A restraint potential with a tuneable rate of flattening would provide greater flexibility to encode the confidence in any given restraint. Here we describe two new such potentials: a Cartesian distance restraint derived from a recent generalisation of common loss functions, and a periodic torsion restraint based on a renormalisation of the von Mises distribution. Further, we describe their implementation as user-adjustable/switchable restraints in ISOLDE, and demonstrate their use in some real-world examples. SynopsisNew forms of adaptive or top-out distance and torsion restraints are described, suitable for restraining a model to match a reference structure during interactive rebuilding. In addition, their implementation in ISOLDE is described along with some illustrative example applications.

biophysics↗

bioRxiv2020

Density modification of cryo-EM maps

Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between macromolecule and solvent are visible and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here model bias is reduced by using ensemble models that allow estimation of model uncertainty. A test of model bias is presented suggesting that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map. SynopsisThe prerequisites for density modification of maps from electron cryomicroscopy are examined and a procedure for incorporating model-based information is presented.

biochemistry↗

bioRxiv2019

Improvement of cryo-EM maps by density modification

A density modification procedure for improving maps produced by single-particle electron cryo-microscopy is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Two key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in electron cryo-microscopy, and that half-maps with independent errors are available in electron cryo-microscopy. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density modification process. The applicability of density modification theory to electron cryo-microscopy was evaluated using half-maps for apoferritin at a resolution of 3.1 [A] and a matched 1.8 [A] reference map. Error estimates for the map obtained by density modification were found to closely agree with true errors as estimated by comparison with the reference map. The density modification procedure was applied to a set of 104 datasets where half-maps, a full map and a model all had been deposited. The procedure improved map-model correlation and increased the visibility of details in the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged.

biochemistry↗

bioRxiv2019

An oligomeric state-dependent switch in FICD regulates AMPylation and deAMPylation of the chaperone BiP

AMPylation is an inactivating modification that matches the activity of the major endoplasmic reticulum (ER) chaperone BiP to the burden of unfolded proteins. A single ER-localised Fic protein, FICD (HYPE), catalyses both AMPylation and deAMPylation of BiP. However, the basis for the switch in FICDs activity is unknown. We report on the transition of FICD from a dimeric enzyme, that deAMPylates BiP, to a monomer with potent AMPylation activity. Mutations in the dimer interface or in residues tracing an inhibitory relay from the dimer interface to the enzymes active site favour BiP AMPylation in vitro and in cells. Mechanistically, monomerisation relieves a repressive effect allosterically-propagated from the dimer interface to the inhibitory Glu234, thereby permitting AMPylation-competent binding of MgATP. Whereas, a reciprocal signal propagated from the nucleotide binding site, provides a mechanism for coupling the oligomeric-state and enzymatic activity of FICD to the energy status of the ER.\n\nImpact StatementUnique amongst known chaperones, the endoplasmic reticulum (ER)-localized Hsp70, BiP, is subject to transient inactivation under conditions of low ER stress by reversible, covalent modification - AMPylation. The enzyme responsible for this modification, FICD, is in fact a bifunctional enzyme with a single active site capable of both AMPylation and deAMPylation. Here we elucidate, by biochemical, biophysical and structural means, the mechanism by which this enzyme is able to switch enzymatic modality: by regulation of its oligomeric state. The oligomeric state-dependent reciprocal regulation of FICD activity is, in turn, sensitive to the ATP/ADP ratio. This allosteric pathway potentially facilitates the sensing of unfolded protein load in the ER and permits the transduction of this signal into a post-translational buffering of ER chaperone activity.

biochemistry↗

bioRxiv2018

Real-space refinement in Phenix for cryo-EM and crystallography

This article describes the implementation of real-space refinement in the phenix.real_space_refine program from the Phenix suite. Use of a simplified refinement target function enables fast calculation, which in turn makes it possible to identify optimal data-restraints weight as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. Re-refinement of 385 cryo-EM derived models available in the PDB at resolutions of 6 [A] or better shows significant improvement of models and the fit of these models to the target maps.\n\nSynopsisA description of the implementation of real-space refinement in the phenix.real_space_refine program from the Phenix suite and its application to re-refinement of cryo-EM derived models.

bioinformatics↗