Biomedical subjects

Jose A Rodriguez

Publications and source records attributed to Jose A Rodriguez.

4 recordsLinked to original sources

PubMed2026

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles.

Recent progress in gas dynamic virtual nozzle (GDVN) technologies in combination with high-brilliance synchrotron and X-ray free-electron lasers (XFELs) has allowed the visualization of protein dynamics in crystallo by mixing macromolecular protein crystals with a substrate using tunable mixing times on the order of milliseconds to seconds prior to serial X-ray diffraction data collection. This has become the method of choice for high-resolution structure determination of intermediate states. However, such experiments require large counts of crystals of proper sizes for high-resolution data collection, and premium beam times for screening efforts. Cryogenic microcrystal electron diffraction (MicroED) represents a complementary technique that may be a more accessible avenue for time-resolved nanocrystallography compared with serial X-ray diffraction experiments. MicroED can produce full diffraction datasets from just a few submicrometre-thick crystals, and the approach is more readily accessible, requiring standard cryogenic transmission electron microscopy (TEM) equipment available at many universities and institutes. Cryogenic MicroED, like other forms of cryo-EM, begins with rapidly freezing biological material on electron microscopy grids. In the case of MicroED, micro- to nano-crystals (<500 nm thick) are deposited onto electron microscopy grids and plunge-frozen for subsequent electron diffraction data collection. Here, we have incorporated GDVN technology developed originally for XFEL experiments into the freezing process as a first step towards time-resolved studies. We describe the limited deposition efficiency of the model MicroED protein proteinase K on TEM grids using GDVNs, preceding sample vitrification and successful MicroED data collection. We discuss both the initial results from such experiments and the methodological challenges in developing this approach into a reliable workflow for millisecond-to-second time-resolved structural studies of macromolecules. Our results promise a strategy to deposit crystals on grids using GDVNs and determine high-resolution structures by MicroED, constituting a first step towards development of time-resolved MicroED experiments.

MicroED↗

PubMed2026

Cryo-EM provides insight into how the Staphylococcus aureus IsdH receptor removes hemin from the hemoglobin:haptoglobin complex.

Staphylococcus aureus extracts hemin from human hemoglobin (Hb) to overcome host-imposed iron limitation. How it recovers Hb-bound hemin from the hemoglobin:haptoglobin (Hb:Hp) complex, the major circulating form of Hb outside red blood cells, remains unclear. Here we use cryo-electron microscopy, biophysical measurements, and solution kinetics to define how the S. aureus IsdH surface receptor extracts hemin from Hb:Hp. A 3.1 Å cryo-EM structure of Hb:Hp bound by full-length IsdH reveals that its N-terminal NEAT domain (N1) anchors it to αHb, whereas its downstream N2N3 extraction unit engages βHb to remove its hemin. The receptor engages Hb:Hp differently than isolated Hb, because N-linked glycans on haptoglobin bias the extraction unit toward βHb, sterically occluding its access to αHb while still permitting engagement by N1. Kinetic assays show that IsdH actively accelerates hemin release from Hb:Hp. Three-dimensional variability analysis indicates that this likely occurs via a dynamic interface in which receptor motions reposition the extraction unit relative to βHb, collectively supporting a model in which IsdH transiently perturbs the F-helix to promote hemin extraction. Alignment of that model with a previously determined CD163:Hb:Hp structure shows how IsdH may disrupt Hb:Hp recognition by macrophage and monocyte CD163 receptors, helping to explain how it may hinder clearance of Hb:Hp from circulation. In aggregate, these results help define the structural basis for hemin extraction from Hb:Hp and how IsdH may subvert receptor-mediated clearance of the Hb:Hp complex.

Journal Article↗

PubMed2025

Leveraging bioorthogonal conjugation for alpha synuclein fibril surveillance.

Alpha synuclein (α-syn) amyloid fibrils are associated with various neurodegenerative diseases. To better understand the molecular and cellular basis for α-syn fibril persistence and spread, we implemented a fluorophore labeling strategy to surveil pre-formed α-syn fibrils in solution and in cells. We leveraged amber codon mediated incorporation of a tetrazine-based artificial amino acid (TetV2.0) to install a cyclooctene-conjugated Janeliaflour, JF549, at four sites on human α-syn: residues 4, 60, 96 and 136. Fast coupling occurred under mild buffer conditions and in the presence of the disease-associated cofactor and cytotoxic lipid, psychosine. Labeled fibrils retained their polymorphic features, seeded the growth of new fibrils in vitro, and induced the seeding of positive puncta in α-syn FRET biosensor HEK293T cells. This allowed simultaneous tracking of exogenous and endogenous α-syn aggregates in biosensor cells, and their localization within the cells. In doing so, our approach facilitates more detailed mechanistic investigation of α-syn aggregates.

Synuclein↗

PubMed2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes.

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Machine Learning↗