Biology subjects

Tejero, R.

Publications and source records attributed to Tejero, R..

6 recordsLinked to original sources

bioRxiv2026

The NMR Exchange Format (NEF): Specification and Applications

The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEFs growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.

biochemistry↗

bioRxiv2024

3D Brain Vascular Niche Model Captures Invasive Behavior and Gene Signatures of Glioblastoma

Glioblastoma (GBM) is a lethal brain cancer with no effective treatment; understanding how GBM cells respond to tumor microenvironment remains challenging as conventional cell cultures lack proper cytoarchitecture while in vivo animal models present complexity all at once. Developing a culture system to bridge the gap is thus crucial. Here, we employed a multicellular approach using human glia and vascular cells to optimize a 3-dimensional (3D) brain vascular niche model that enabled not only long-term culture of patient derived GBM cells but also recapitulation of key features of GBM heterogeneity, in particular invasion behavior and vascular association. Comparative transcriptomics of identical patient derived GBM cells in 3D and in vivo xenotransplants models revealed that glia-vascular contact induced genes concerning neural/glia development, synaptic regulation, as well as immune suppression. This gene signature displayed region specific enrichment in the leading edge and microvascular proliferation zones in human GBM and predicted poor prognosis. Gene variance analysis also uncovered histone demethylation and xylosyltransferase activity as main themes for gene adaption of GBM cells in vivo. Furthermore, our 3D model also demonstrated the capacity to provide a quiescence and a protective niche against chemotherapy. In summary, an advanced 3D brain vascular model can bridge the gap between 2D cultures and in vivo models in capturing key features of GBM heterogeneity and unveil previously unrecognized influence of glia-vascular contact for transcriptional adaption in GBM cells featuring neural/synaptic interaction and immunosuppression.

bioengineering↗

bioRxiv2024

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/575520v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@bac5eforg.highwire.dtl.DTLVardef@190cdborg.highwire.dtl.DTLVardef@9b3d83org.highwire.dtl.DTLVardef@1e38ccc_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPDB Structure Validation Report expanded to include Restraint Analysis C_LIO_LINMR Exchange Format (NEF) and NMR-STAR for distance restraint representation C_LIO_LIStandard distance and dihedral restraint formats for model vs. restraint assessment C_LIO_LIStandardized restraint formats provide interoperability between modeling programs C_LI

bioinformatics↗

bioRxiv2023

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. HighlightsO_LIAF2 models assessed against NMR data for 9 monomeric proteins not used in training. C_LIO_LIAF2 models fit NMR data almost as well as the experimentally-determined structures. C_LIO_LIRPF-DP, PSVS, and PDBStat software provide structure quality and RDC assessment. C_LIO_LIRPF-DP analysis using AF2 models suggests multiple conformational states. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/525096v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@be0a41org.highwire.dtl.DTLVardef@9336e9org.highwire.dtl.DTLVardef@fa1d24org.highwire.dtl.DTLVardef@8f4efe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracy rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

biochemistry↗

bioRxiv2019

Plexin-B2 is a key regulator of cell mechanics during multicellular organization

During multicellular organization, individual cells need to constantly respond to environmental cues and adjust contractile and adhesive forces in order to maintain tissue integrity. The signaling pathways linking biochemical cues and tissue mechanics are unclear. Here, we show that Plexin-B2 regulates mechanochemical integration during multicellular organization. In human embryonic stem cells (hESCs), Plexin-B2 controls cell shape and tissue geometry in both 2D epithelial colony and 3D spheroid aggregates by regulating actomyosin contractility and junctional/cell-matrix adhesive properties. Atomic force microscopy (AFM) directly demonstrates that Plexin-B2 modulates cell stiffness in hESC colonies, which in turn impacts cell proliferation and cell fate specification through {beta}-catenin signaling and YAP mechanosensing. YAP also functions as a mechanoregulator downstream of Plexin-B2, thus forming a mechanochemical integrative loop. In human neuroprogenitor cells (hNPCs), Plexin-B2 similarly controls cell stiffness and tensile forces, as revealed by AFM and FRET tension sensor studies. Strikingly, Plexin-B2-deficient hNPCs display accelerated neuronal differentiation. From an organogenesis perspective, Plexin-B2 maintains cytoarchitectural integrity of neuroepithelium, as modeled in cerebral organoids. On a signaling level, Plexin-B2 engages extracellular as well as intracellular Ras-GAP and RBD domains for mechanoregulation through Rap and Rac GTPases. Our data unveil a fundamental function of Plexin-B2 for mechanochemical integration during multicellular organization, and shed light on the principle of force-mediated regulation of stem cell biology and tissue morphogenesis.

developmental biology↗