Biology subjects

McCoy, A. J.

Publications and source records attributed to McCoy, A. J..

12 recordsLinked to original sources

bioRxiv2025

Amoeboid-Mesenchymal Transition and the Proteolytic Control of Cancer Invasion Plasticity

Invasion plasticity allows malignant cells to toggle between collective, mesenchymal and amoeboid phenotypes while traversing extracellular matrix (ECM) barriers. Current dogma holds that collective and mesenchymal invasion programs trigger the mobilization of proteinases that digest structural barriers dominated by type I collagen, while amoeboid activity allows cancer cells to marshal mechanical forces to traverse tissues independently of ECM proteolysis. Here, we use cancer spheroid-3-dimensional matrix models, single-cell RNA sequencing, and human tissue explants to identify the mechanisms controlling mesenchymal versus amoeboid invasion. Unexpectedly, collective/mesenchymal- and amoeboid-type invasion programs - though distinct - are each characterized by active tunneling through ECM barriers, with expression of matrix-degradative metalloproteinases. CRISPR/Cas9-mediated targeting of a single membrane-anchored collagenase, MMP14/MT1-MMP, ablates tissue-invasive activity while co-regulating cancer cell transcriptional programs. Though changes in matrix architecture, nuclear rigidity, and metabolic stress as well as the presence of cancer-associated fibroblasts are proposed to support amoeboid activity, none of these changes restore invasive activity of MMP14-targeted cancer cells. While a requirement for MMP14 is bypassed in low-density collagen hydrogels, invasion by the proteinase-deleted cells is associated with nuclear envelope and DNA damage, highlighting a proteolytic requirement for maintaining nuclear integrity. Nevertheless, when cancer cells confront explants of live human breast tissue, MMP14 is again required to support invasive activity. Corroborating these results, spatial transcriptomic and immunohistological analyses of invasive human breast cancers identified clear expression of MMP14 in invasive cells that were further associated with degraded collagen, underlining the pathophysiologic importance of this proteinase in directing invasive activity in vivo.

cancer biology↗

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

Clickable PEG-norbornene microgels support suspension bioprinting and microvascular assembly

The development of perfusable and multiscale vascular networks remains one of the largest challenges in tissue engineering. As such, there is a need for the creation of customizable and facile methods to produce robustly vascularized constructs. In this study, secondarily crosslinkable (clickable) poly(ethylene glycol)-norbornene (PEGNB) microbeads were produced and evaluated for their ability to sequentially support suspension bioprinting and microvascular self-assembly towards the aim of engineering hierarchical vasculature. The clickable PEGNB microbead slurry exhibited mechanical behavior suitable for suspension bioprinting of sacrificial bioinks, could be UV crosslinked into a granular construct post-print, and withstood evacuation of the bioink and subsequent perfusion of the patterned void space. Endothelial and stromal cells co-embedded within jammed RGD-modified PEGNB microbead slurries assembled into capillary-scale vasculature after secondary crosslinking of the beads into granular constructs, with endothelial tubules forming within the interstitial space between microbeads and supported by the perivascular association of the stromal cells. Microvascular self-assembly was not impacted by printing sacrificial bioinks into the cell-laden microbead support bath before UV crosslinking. Collectively, these results demonstrate that clickable PEGNB microbeads are a versatile substrate for both suspension printing and microvascular culture and may be the foundation for a promising methodology to engineer hierarchical vasculature.

bioengineering↗

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↗

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

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↗