The solute carriers of the major facilitator superfamily (MFS) play significant roles in human health and disease. The melibiose transporter of Salmonella enterica serovar Typhimurium (MelBSt) catalyzes the stoichiometric symport of galactosides with Na+, H+ or Li+ and is a well studied prototype of MFS transporters. With a large body of data from extensive functional analyses using multiple transport and binding assays, structural characterizations of multiple states by X-ray crystallography and cryoEM single-particle analysis, and dynamic elucidations using hydrogen-deuterium exchange mass spectrometry (HDX-MS), the molecular recognition of the primary substrate galactoside and its coupling cation Na+, H+ or Li+ has been elucidated at the molecular level. Cooperative binding of both solutes is recognized as the core symport mechanism that supports the transporter's functions more effectively in a sugar-scarce environment. Structural and dynamic studies show that conformational dynamics significantly influence sugar binding but have minimal effect on Na+. Na+ acts as an allosteric activator, increasing sugar affinity by stabilizing the inner barrier and constraining conformational flexibility. All data are consistent with the previously constructed stepped-binding kinetic model for melibiose symport with Na+; in addition, MFS uniport and antiport mechanisms are discussed.
Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated by carbon edges, ice crystals, ethane bubbles and other high-contrast artifacts. These contaminants trigger abundant false positives in automated particle pickers, severely hampering downstream 3D reconstruction. Existing methods either avoid contamination implicitly (requiring dataset-specific tuning) or rely on rule-based filters that fail on complex contamination patterns. Here, we present QwenCryoMarker, a universal post-processing framework that converts the outputs of arbitrary particle pickers into clean, high-precision particle sets. Our pipeline consists of two core stages: (i) a visual large model (Qwen-Image-Edit-2511) fine-tuned via supervised learning to generate pixel-accurate binary contamination masks from raw micrographs and (ii) a lightweight contamination-aware filtering module that discards particles when the contamination proportion within their surrounding circular region exceeds a predefined threshold ratio. Contamination is defined as all micrograph regions unsuitable for reliable particle picking and subsequent 3D reconstruction, including carbon edges, ice crystals, ethane bubbles, miscellaneous debris and dense protein aggregates. The framework features plug-and-play deployment: it requires no per-dataset parameter tuning or extra retraining, and maintains compatibility with classical pickers (blob detection, template matching) as well as deep learning-based pickers (Topaz, crYOLO etc.). We validate QwenCryoMarker on five diverse CryoPPP benchmark datasets across four representative particle pickers. Quantitatively, our method consistently boosts precision with an average absolute gain of 0.009 and lifts the F1-score, while recall only drops slightly by an average of 0.008. Segmentation benchmarking shows our model reaches a mean intersection over union (IoU) of 0.629, surpassing that of the state-of-the-art MicrographCleaner (0.551) by 14.2%. We further compare against multiple segmentation baselines: U-Net (0.448), DeepLabV3+ (0.488), SAM (0.475), ASOCEM (0.195) and IceBreaker (0.433). A downstream reconstruction case study on EMPIAR-10017 verifies that particles filtered by QwenCryoMarker yield cleaner 2D class averages and higher resolution 3D density maps (3.88 versus 3.97 Å). Qualitative visualization also confirms that QwenCryoMarker stably eliminates false particles located on carbon films and ice crystals, independent of the upstream particle-picking algorithm. By encapsulating contamination suppression as a universal, model-agnostic post-processing module, QwenCryoMarker offers a practical, robust, easy-to-deploy toolkit that greatly improves particle-set quality without modifying existing cryo-EM workflows. The framework is fully open-source and can be seamlessly integrated into mainstream cryo-EM processing pipelines.
Adoptive cell therapy (ACT) with tumor-specific T cells can mediate durable cancer regression. The main target of tumor-specific T cells are neoantigens resulting from mutations in self-antigens over the course of malignant transformation. To understand T-cell recognition of cancer neoantigens at the atomic level, we studied a T-cell receptor (TCR 4414A) that recognizes a neoepitope arising from a driver mutation in the p53 oncogene (p53Y220D) presented by HLA-A2. Here, we report the structure of TCR 4414A bound to HLA-A2 and p53Y220D, as well as structures of unbound wild-type and mutant p53-HLA-A2 ligands. The structures reveal that the Y220D mutation induces a conformational change in the p53Y220D neoepitope that is detected by TCR 4414A, thereby rendering a normally cryptic self-peptide visible to T cells. The TCR minimizes interactions with the N- and C-terminal portions of p53Y220D, which are identical in mutant and wild-type peptides, and instead focuses on the Y220D driver mutation at the peptide center. In this way, TCR 4414A achieves highly specific recognition of mutant over wild-type p53, a critical parameter for avoiding off-target toxicities in ACT.
The Guest Editors introduce the special issue based on talks at the CCP4 Study Weekend 2024. The virtual issue is available at https://journals.iucr.org/special_issues/2026/CCP42024/.
The determination of protein-ligand complex structures by X-ray crystallography is a cornerstone of modern structure-guided drug discovery. However, the process is complex and fraught with potential pitfalls at every stage, from data collection to final model deposition. The presence of flawed or misinterpreted ligand models in the Protein Data Bank (PDB) can misdirect scientific efforts that rely on them as the basis for new hypotheses and experiments. This article outlines a practical approach for ligand validation during structure determination. We discuss the application of validation tools, such as those in Coot, MolProbity, Mogul and Buster-report, to avoid errors. By re-examining several deposited PDB entries, we illustrate key pitfalls, including (i) modelling a ligand into absent or ambiguous electron density, (ii) incorrect chemical definitions (e.g. chirality, tautomers), (iii) poor fit of parts of a ligand to the electron density and (iv) data/model mismatches during deposition. We emphasize the importance of a `null-hypothesis' approach and continuous critical assessment throughout the modelling process to improve the reliability of deposited structures.
