Biology subjects

Hekkelman, M. L.

Publications and source records attributed to Hekkelman, M. L..

6 recordsLinked to original sources

bioRxiv2026

SMIM1 drives an alternative iron uptake pathway

Iron is essential for numerous biological processes, and its abundance is critical in human cells to prevent anemia or toxic overload. Although transferrin receptor-mediated iron uptake is the canonical route, human cells also possess an unstudied alternative pathway. To reveal this missing mechanism, we developed a large-scale, gain-of-function approach to assign genes to a catalogue of quantitative cellular traits. Applying this genetic strategy, we identified SMIM1, the antigen responsible for the rare Vel blood group and a transmembrane protein with unknown function, as a key regulator of iron homeostasis. We found that SMIM1 expression completely suppresses the iron deficiency caused by transferrin receptor loss. Mechanistically, SMIM1 mediates iron uptake through clathrin-dependent endocytosis of hemoglobin (Hb). SMIM1 directly binds Hb via its extracellular HKCK motif, leading to its transport to lysosomes and subsequent iron release. In the human population, genetic variants in SMIM1 influence Hb levels and erythrocyte traits. SMIM1-deficient mice display iron deficiency in early erythroid precursors in response to erythropoietic stress, underscoring the physiological relevance of this process.

cell biology↗

bioRxiv2025

DSSP 4: FAIR annotation of protein secondary structure

Protein secondary structure annotation is essential for understanding protein architecture, serving as a cornerstone for structural classification, alignment, visualisation, and machine learning applications. The Define Secondary Structure of Proteins (DSSP) algorithm has long been the standard for assigning secondary structure elements such as -helices, {beta}-sheets, and loops in protein models. Here, we introduce DSSP version 4, which recapitulates DSSP functionality in a modern computational framework, extending also to the detection of left-handed {kappa}-helices (Poly-Proline II helices). To align with the FAIR principles (Findable, Accessible, Interoperable, Reusable), DSSP 4 adopts mmCIF as its primary input and output format, while retaining compatibility with legacy PDB and DSSP formats. We applied this updated tool to analyse the distribution of secondary structure elements across the Protein Data Bank (PDB) differentiating structures from diverse experimental methods, revealing insights into the prevalence and length of secondary structure elements, including the newly annotated {kappa}-helices. The DSSP 4 software, databank, and server are freely accessible from https://pdb-redo.eu/dssp, ensuring broad utility and interoperability in structural biology research.

bioinformatics↗

bioRxiv2022

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modelling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

bioinformatics↗

bioRxiv2021

AlphaFill: enriching the AlphaFold models with ligands and co-factors

Artificial intelligence (AI) methods for constructing structural models of proteins on the basis of their sequence are having a transformative effect in biomolecular sciences. The AlphaFold protein structure database makes available hundreds of thousands of protein structures. However, all these structures lack cofactors essential for their structural integrity and molecular function (e.g. hemoglobin lacks a bound heme), key ions essential for structural integrity (e.g. zinc-finger motifs) or catalysis (e.g. Ca2+ or Zn2+ in metalloproteases), and ligands that are important for biological function (e.g. kinase structures lack ADP or ATP). Here, we present AlphaFill, an algorithm based on sequence and structure similarity, to "transplant" such "missing" small molecules and ions from experimentally determined structures to predicted protein models. These publicly available structural annotations are mapped to predicted protein models, to help scientists interpret biological function and design experiments.

bioinformatics↗

bioRxiv2020

A global Ramachandran score identifies protein structures with unlikely stereochemistry

Ramachandran plots report the distribution of the ({varphi}, {Psi}) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to "outlier", "allowed" and "favored" regions. While "zero unexplained outliers" can be considered the current "gold standard", this can be misleading if deviations from expected distributions, even within the favored region, are not considered. We therefore revisited the Ramachandran Z-score (Rama-Z), a quality metric introduced more than two decades ago, but underutilized. We describe a re-implementation of the Rama-Z score in the Computational Crystallography Toolbox along with a new algorithm to estimate its uncertainty for individual models; final implementations are available both in Phenix and in PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications.

biophysics↗

bioRxiv2018

Homology-based loop modelling yields more complete crystallographic protein structures

Inherent protein flexibility, poor or low-resolution diffraction data, or poor electron density maps, often inhibit building complete structural models during X-ray structure determination. However, advances in crystallographic refinement and model building nowadays often allow to complete previously missing parts. Here, we present algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and \"graft\" these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in our PDB-REDO pipeline, allowed to build 24,962 missing loops in the PDB. The models and the automated procedures are publically available through the PDB-REDO databank and web server (https://pdb-redo.eu). More complete protein structure models enable a higher quality public archive, but also a better understanding of protein function, better comparison between homologous structures, and more complete data mining in structural bioinformatics projects.\n\nSynopsisThousands of missing regions in existing protein structure models are completed using new methods based on homology.

bioinformatics↗