Journal of Applied Crystallography

Time-resolved crystallography can be inaccessible for membrane proteins and other difficult-to-crystallize targets for which suitable crystals are scarce. Here we present an acquisition strategy that records consecutive diffraction images during a small crystal rotation synchronized to a single activation pulse. Matching image segments are merged across crystals to reconstruct successive time windows, enabling a single crystal ensemble to capture an extended structural trajectory. Using the Mesh&Collect workflow at a synchrotron, we obtained ten consecutive 7.5-ms time windows at 2 [A] resolution from 1,255 microcrystals of a bacteriorhodopsin triple mutant at room temperature. An additional 790 crystals extended the observation window to 500 ms. Rotation-based processing, retrospective time binning and dose control maximize information extracted from each crystal, while compatible measurements can be pooled across acquisitions. This approach makes millisecond time-resolved crystallography practical for crystal-limited targets at synchrotrons, with further development potentially extending the temporal resolution down to 1 ms.

Proteins are molecular machines whose dynamics govern their function. Using time-resolved crystallography, the basis of protein mechanics can be uncovered by physical perturbation on protein crystals. Perturbations are often vector fields that, in crystals, break symmetry. Lacking existing tools for analyzing symmetry breaking, we built open-source software to do so. We applied this method to three time-resolved crystallography experiments and analyzed the effect of vectorial perturbations on macromolecular crystal structure. We demonstrate that our method can handle symmetry reduction in all 65 Sohncke space groups.

Carbohydrates are essential molecules involved in a wide range of biological processes. While their structural representation in the Protein Data Bank (PDB) has been hindered due to their highly diverse and flexible nature, advancements on X-ray crystallography and CryoEM are accelerating the obtaining of their experimental structures, which in turn supports improved analyses of their structural properties, such as the role of their non-chair conformations on biological processes. In this context, the current work provides a comprehensive and up to date overview of carbohydrate structures within the PDB through classification, quantification, and characterization of monosaccharides and glycan chains, incorporating Cremer-Pople puckering coordinates to evaluate pyranose conformations. Our findings show that, even with a steep increase in CryoEM structures including more complex oligosaccharides, carbohydrate data remains concentrate dinsmallglycanchains, with a about 4% of non-chair conformation sintopresolution structures. Free energy landscapes for the pyranose ring in the presence and absence of nine different binding proteins, including both enzymes and non-enzymes, reinforce the preferred stability of chairs and the rarity of conformational induction upon binding.

Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.

Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.

The visualisation of large biological samples in cryo-electron tomography requires both few-nanometre resolution and a wide, ideally >10 micrometrre, field of view. For a single image, increasing magnification trades increased resolution for a smaller field of view. Montaging, where high-magnification images are stitched together to increase field of view, addresses the trade-off but is dose-inefficient with round beams that are standard in transmission electron microscopy (TEM), since this illumination shape does not tessellate. Our workflow greatly simplifies montaging schemes for dose-sensitive specimens, allowing large montages of any shape - from individual cells or other features of interest up to whole lamellae - to be collected at high resolution without compromising electron dose efficiency. We demonstrate the workflow by acquiring montage tomograms of whole yeast (Saccharomyces cerevisiae) cells in cryo-FIB lamellae and of human neurons.

Surface plasmon resonance (SPR) and biolayer interferometry (BLI) are well-established label-free analytical technologies used to determine biomolecular interaction rates (ka, kd) and equilibrium constants (KD). While assays historically rely on multi-cycle kinetics (MCK), this approach requires intermediate surface regeneration, risking sensor degradation and increasing reagent consumption. Single-cycle kinetics (SCK) avoids regeneration by sequentially injecting increasing analyte concentrations. However, implementing SCK workflows on biosensor platforms like the OctetRR8 BLI instrument (Sartorius) remains a challenge due to the lack of built-in or online data analysis tools. To address this, we introduce SCKAnalyzer, a free, user-friendly web application designed for processing and fitting SCK datasets. SCKAnalyzer accepts column-based text files and native raw BLI data (.frd format) from Sartorius instruments, performing data pre-processing and non-linear fitting to a 1:1 interaction model. Additionally, it offers advanced simulation capabilities that allow variable association and dissociation durations across sequential injection steps, a feature absent in existing platforms such as BiacoreTMSimul8. The performance of SCKAnalyzer was experimentally validated using a 3-step SCK method (SCK3) for DNA/RNA duplex formation on a Biacore T200, as well as 4-step (SCK4) and 5-step (SCK5) methods for DNA/DNA duplex formation on an OctetRR8 BLI instrument. Binding parameters (ka, kd, KD) derived from SCKAnalyzer showed excellent agreement with values from established proprietary softwares such as Biacore T200 Evaluation Software v3.2.1 and BIAevaluation v4.1.1. SCKAnalyzer provides a practical alternative to MCK, reducing probe consumption and extending SCK utility across biosensor platforms. SCKAnalyzer is available at sck.iecb.u-bordeaux.fr (source code: github.com/synthaze/sckanalyzer).

Atomistic simulations of intrinsically disordered proteins (IDPs) are notoriously sensitive to force field inaccuracies, either regarding the protein or the water model, yielding inaccurate observables such as compactness, secondary structure propensities, or kinetics. The currently most widely used IDP force fields are Amber99sb-disp (A99disp) and Charmm36m (C36m). A99disp includes a new water model and thus optimized both, the protein and the water interactions. In contrast, C36m used the Tip3p water model and optimized only protein interactions. In many cases, C36m+Tip3p underestimates radii of gyration compared to FRET or SAXS experiments. Such overly compact structural ensembles are believed to arise from an imbalance between protein-protein, protein-water, and water-water interactions, which might be due to Tip3p inaccuracies. Here, we aim at re-balancing these interactions by combining C36m with the Optimal Point Charge (OPC) water model. Recently, this C36m+OPC combination showed improved accuracy for the disordered domain of the measles virus nucleoprotein. Here we present a systematic assessment, comparing C36m+OPC, C36m+Tip3p, C36m+Tip4p, C22*, A03ws, A99sb-ws, and A99disp for five IDPs, as well as a subset of those for five globular proteins, a set of disordered AGQ-repeat peptides, and the fast folding miniprotein CLN025. We compared extensive MD simulations (> 8.5 ms) with SAXS, NMR, circular dichroism, photo-induced electron transfer (PET), T-jump infrared spectroscopy, and X-ray crystallography measurements. We found that combining C36m with OPC improved accuracy over C36m+Tip3p for IDP ensembles without compromising its accuracy for globular proteins. While also the kinetics of the AGQ-peptides were more accurate for C36m+OPC, those of CLN025 folding were less accurate. Overall, C36m+OPC showed similar accuracy as A99sb-ws and A99disp, the latter is currently considered among the most accurate protein force fields.

Structure-aware proteolysis prediction often ranks candidate cleavage sites using solvent accessibility from static protein structures, yet this assumption is rarely tested against native-state cleavage data. We examined this problem using AprX, a serralysin-family protease that readily hydrolyses caseins but shows lower activity toward native whey proteins. Across six bovine milk proteins, sequence compatibility and local scissile-bond accessibility did not separate caseins from whey proteins. In contrast, accessibility fell progressively in whey proteins as the required contiguous substrate window increased, reaching 0% across the working P4-P4' footprint, while all evaluable casein windows remained accessible in isolated-chain ensembles. Local remodelling showed that whey segments could still adopt AprX-compatible backbone geometry. An external thermolysin benchmark rejected a rigid whole-protein clash metric, but did not alter the contiguous-accessibility result. These findings identify footprint-scale accessibility as a distinct structural variable and show why single-residue solvent exposure can overestimate protease accessibility in folded proteins.

Single-particle cryo-electron microscopy (cryo-EM) plays an increasingly important role in structure-based drug discovery (SBDD) by revealing target protein-ligand interactions at the molecular level, but its application has largely focused on large- and medium-sized protein complexes. Many therapeutically important targets are smaller than 50 kDa and remain highly challenging for cryo-EM analysis because of low signal-to-noise ratios and difficulties in particle alignment. Here, we describe cryoFAST, an integrated workflow that couples ligand-compatible Fab discovery and structure-guided computational maturation with biochemical triage, fiducial-complex assembly, and single-particle cryo-EM. Through phage-display screening and/or AI-guided design, we developed high-affinity Fab binders of approximately 50 kDa that stabilize the target without disrupting ligand-target interactions and then assembled Fab-target complexes for cryo-EM analysis. Using the therapeutically relevant extracellular domain (~20 kDa) of interleukin-4 receptor (IL-4R) as a proof of concept, we determined its cryo-EM structure at 2.74 Angstrom resolution and established a framework for elucidating ligand-binding modes. This workflow provides a practical, efficient, and potentially broadly applicable route to the structural characterization of small protein targets that are otherwise challenging to study. By extending the applicability of cryo-EM to high-value but technically challenging small targets, cryoFAST may open new opportunities for structure-based drug discovery.

Cryogenic electron microscopy (cryo-EM) has enabled rapid progress in structural biology throughout the last decade. However, cryo-EM sample preparation still represents a major bottleneck in high-resolution structure determination. Many proteins and macromolecular complexes interact with the air-water interface during sample preparation, which can cause protein denaturation, complex disassembly, and preferred particle orientation. Development of methods to alleviate these issues is an active field of investigation. However, a universally applicable solution to this problem has not been found so far. Here, we describe the simple and cost-effective usage of synthetic peptides with defined amino acid sequence to protect cryo-EM samples from complex disassembly and reduce preferred orientation. We show benefits during cryo-EM grid preparation of a large E3 ubiquitin ligase termed the anaphase-promoting complex (APC/C), and the small CDK11:cyclin L:SAP30BP complex.

Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.

Serial-sectioning electron microscopy allows the exploration of the 3D ultrastructure of cells, tissues or whole organisms. Sample preparation is a critical step for this technique, which determines the quality of downstream imaging and analysis. Here we optimize a high-pressure freezing sample-preparation workflow for larvae of the marine annelid Platynereis dumerilii. We tested several fixation and staining methods and screened a large number of specimens by synchrotron X-ray imaging to evaluate specimen integrity and contrast. We further evaluated selected blocks by array tomography SEM to select the best performing protocols. Our protocols and robust workflow could be adapted to similar marine planktonic organisms including ciliated invertebrate larvae.

Microplastic pollution from homopolymers such as polypropylene (PP), polystyrene (PS), and polyethylene (PE) poses growing health and environmental concerns, highlighting the need for robust polymer-degrading biocatalysts. We previously identified a naturally occurring cysteine-dependent hydrolase (CsdH) from Rhodococcus opacus, featuring a unique Cys160-Lys127-Asp39 catalytic triad and providing a novel mechanism for polyethylene terephthalate (PET) degradation. In this study, we have determined crystal structures of CsdH in complex with PP, PS, PE, and styrene and demonstrated the potential of the enzyme to directly degrade these homopolymers into their respective low molecular weight degradation products using LC-MS. Structural analysis reveals that the enzyme retains a conserved principal access tunnel while undergoing subtle substrate-dependent changes to accommodate different substrates. The enzyme exhibited broad plastic degradation activity with the highest weight loss observed for PE, followed by PP and PS. SEM analysis reveals surface roughness, cracks and grooves, confirming polymer degradation. Further, these results corroborate through FTIR and TGA analysis. These findings establish a strong foundation for enzyme engineering and aim at developing efficient microplastic degradation.

Preferred particle orientation, which results from the adsorption of proteins to the air-water interface, is a frequently encountered issue in single-particle cryo-electron microscopy (cryo-EM). Particles then present only a limited number of views, which are oversampled during data acquisition at the expense of throughput, while other views may be missing altogether. In severe cases, a high-resolution reconstruction becomes unattainable. We have recently established that ultrasonic excitation of the sample during vitrification improves the angular distribution by detaching particles from the air-water interface and reshuffling their orientations. Here, we describe an implementation of this principle for a plunge freezing apparatus, in which the ultrasonic transducer is integrated into the head of the pair of tweezers holding the sample, and show that this design is able to reduce preferred orientation. We demonstrate this for hemagglutinin, the canonical example of preferred orientation, and show that the fraction of side and tilted views increases with the excitation amplitude. This allows us to obtain a complete reconstruction of the trimer at 2.5 [A].

In eukaryotic cells, the positive transcription elongation factor b (P-TEFb) plays a critical role in the transition of RNAPII from the paused state to actively transcribing mode. Although three different cyclin (Cyc) T (CycT1, T2a, and T2b) interact with CDK9 to form functional P-TEFb, a majority of P-TEFb complexes are comprised of the CycT1 and CDK9. To analyze the role of the CycT1 subunit of P-TEFb on cellular transcription, we established HEK293T cells lacking CycT1 proteins (CycT1-KO cells). While no apparent growth defects were observed with CYcT1-KO cells, the level of CDK9 was decreased and no compensatory over-expression of CycT2 was observed, suggesting that the reduced level of functional P-TEFb complexes is sufficient for the normal cell growth. Reporter gene assays indicate that NF-kappaB-dependent transcription induced by PMA was impaired in CycT1-KO cells although the function of NF-kappaB (P65) per se was not affected. On the other hand, AP1-dependent transactivation induced by PMA was unaffected in CycT1-KO cells. Moreover, transcription stimulated by JQ1, a strong P-TEFb inducer, was reduced in CycT1-KO cells. Data of biochemical analysis indicate that P-TEFb with CycT1 (P-TEFb (CDK9:CycT1)) was efficiently released from 7SKsnRNP by JQ1 while P-TEFb (CDK9:CycT2) was not affected by JQ1. Transcriptome analysis indicated that the lack of CycT1 had a minor effect on the steady-state transcription. However, expression of JQ1-dependent genes was severely reduced in CycT1-KO cells. Interestingly, referring to gene expression profiles of 33 different types of cancer revealed that 11 out of 13 top JQ1-dependent genes were also highly upregulated in Pancreatic Adenocarcinoma. From these results, we conclude that although many genes can be regulated by both P-TEFb (CDK9:CycT1) and P-TEFb (CDK9:CycT2), CycT1 plays a critical role in regulating highly inducible genes, which are also aberrantly regulated in a particular type of cancer.

The iTHRIV Scholars Mentored Career Development Program initiated an Internal Grant Review (IGR) program in 2020 for current and recently graduated Scholars seeking funding through K and R awards from the NIH. The IGR program is designed to replicate the NIH review process and provide Scholars the opportunity to receive valuable feedback on their applications prior to NIH submission. A key characteristic of the program is the integration of REDCap, enabling automation and year-round offering while improving tracking and reporting efforts and capabilities. Results of the program have been overall positive, both in proposal development and participant feedback. IGR is a sustainable, valuable resource for early-career faculty competing for limited resources in the pursuit to become independently funded clinical translational scientists.

Directed evolution enables the engineering of proteins with novel or improved functions, yet existing approaches often require extensive manual intervention and are difficult to sustain over long evolutionary trajectories. Here we introduce PRIME (Protein-primed Replication for In vivo Mutagenesis and Evolution), a plasmid-based orthogonal DNA replication system for continuous directed evolution in Escherichia coli. PRIME harnesses protein-primed DNA replication to establish an autonomous replicon that operates independently of the host genome. Coupling this system to an error-prone DNA polymerase enables targeted diversification of constructs encoded on the orthogonal plasmid while preserving genomic integrity. Using only standard laboratory equipment, we demonstrate the evolution of a construct expressing msfGFP with an 11.8-fold increase in fluorescence. The platform is fully compatible with standard molecular biology workflows and requires no specialised instrumentation. We further demonstrate its portability across bacterial hosts by establishing PRIME in Pseudomonas putida. PRIME thus provides a broadly accessible framework for continuous in vivo evolution, with wide-ranging applications in protein engineering and synthetic biology.

To resolve how the gut microbiota shapes human health, it is essential to quantify the exchange of chemicals between microbes and the host. Much as the dose of a drug determines its pharmacological effect, the magnitude of a specific chemical flux determines its impact. Here, we establish from first principles how fluxes can be estimated by integrating key parameters from human digestive physiology and microbial metabolism. We apply this framework to three cases: the exchange of fermentation products, the production of toxins by bacterial pathogens, and nitrogen homeostasis. Our analysis identifies three directly measurable host-level parameters which, in concert with microbial activity, shape flux magnitudes: intestinal transit time, absolute microbial abundance in feces, and fecal mass loss. The simultaneous quantification of these parameters provides a feasible yet essential step toward a mechanistic and quantitative description of gut microbiota-host interactions across health and disease.