Biology subjects

Elliott, L. G.

Publications and source records attributed to Elliott, L. G..

5 recordsLinked to original sources

bioRxiv2026

Phosphorylation at S345 Converts HIF-2α from a Transcription Factor to an RNA binding protein

Oxygen sensing and adaptation are vital for metazoan survival. At the cellular level the response to hypoxia is characterised by a switch in transcriptional programming, primarily mediated by hypoxia-inducible factors (HIFs). HIFs comprise an obligatory heterodimer between an oxygen-sensitive HIF- subunit and an oxygen-insensitive HIF-1{beta} subunit, which together regulate gene expression in response to hypoxia. Among the three HIF- proteins in vertebrates (HIF-1, HIF-2, and HIF-3), post-translational modifications (PTMs) play a crucial role in modulating their stability, localisation, and activity. While many modifications have been investigated on HIF-1, our knowledge of HIF-2 regulation is very limited. Here, we investigated the function of the recently identified HIF-2 S345 phosphorylation site and demonstrate that this disrupts the interaction between HIF-2 and HIF-1{beta}, thereby silencing the HIF-2 transcriptional response. Additionally, our findings suggest that this modification redirects HIF-2 from its normal transcription factor function towards a role in mRNA fate control, as indicated by mass spectrometry interactome analysis and RNA-immunoprecipitation. Our discoveries highlight a new regulatory mechanism of HIF-2 activity, where S345 phosphorylation impedes HIF-2/HIF-1{beta} heterodimer formation, creating a switch in HIF-2 function. As HIF-2 is the only HIF- isoform with a clinically approved inhibitor, these insights are crucial for advancing therapeutic strategies targeting hypoxia signalling.

molecular biology↗

bioRxiv2025

ABCFold: easier running and comparison of AlphaFold 3, Boltz-1 and Chai-1

MotivationThe latest generation of deep learning-based structure prediction methods enable accurate modelling of most proteins and many complexes. However, preparing inputs for the locally installed software is not always straightforward, and the results of local runs are not always presented in an ideally accessible fashion. Furthermore, it is not yet clear whether the latest tools perform equivalently for all types of target. ResultsABCFold facilitates the use of AlphaFold 3, Boltz-1, and Chai-1 with a standardised input to predict atomic structures, with Boltz-1 and Chai-1 being installed on runtime (if required). MSAs can be generated internally using either the JackHMMER MSA search within AlphaFold 3, or with the MMseqs2 API. Alternatively, users can provide their own custom MSAs. This therefore allows AlphaFold 3 to be installed and run without downloading the large databases needed for JackHMMER. There are also straightforward options to use templates, including custom templates. Results from all packages are treated in a unified fashion, enabling easy comparison of results from different methods. A variety of visualisation options are available which include information on steric clashes. Availability and implementationABCFold is coded in Python and JavaScript. All scripts and associated documentation are available from https://github.com/rigdenlab/ABCFold or https://pypi.org/project/ABCFold/1.0.0/. Contactdrigden@liverpool.ac.uk

bioinformatics↗

bioRxiv2024

AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic beta-solenoid structures for repeat proteins

AlphaFold 2 has revolutionised protein structure prediction but, like any new tool, its performance on specific classes of targets, especially those potentially under- represented in its training data, merits attention. Prompted by a highly confident prediction for a biologically meaningless, scrambled repeat sequence, we assessed AF2 performance on sequences comprised perfect repeats of random sequences of different lengths. AF2 frequently folds such sequences into {beta}-solenoids which, while ascribed high confidence, contain unusual and implausible features such as internally stacked and uncompensated charged residues. A number of sequences confidently predicted as {beta}-solenoids are predicted by other advanced methods as intrinsically disordered. The instability of some predictions is demonstrated by Molecular Dynamics. Importantly, other Deep Learning-based structure prediction tools predict different structures or {beta}-solenoids with much lower confidence suggesting that AF2 alone has an unreasonable tendency to predict confident but unrealistic {beta}-solenoids for perfect repeat sequences. The potential implications for structure prediction of natural (near-)perfect sequence repeat proteins are also explored.

bioinformatics↗

bioRxiv2024

Deep-learning protein structure predictions suggest likely molecular functions for three uncharacterised polytopic membrane proteins from the P. falciparum apicoplast

Malaria is a burdensome disease to humanity caused chiefly by the still poorly understood parasite genus Plasmodium. Much of the pathogenic success of these and other related parasites is due to the presence of the apicoplast, a comparatively poorly characterised biosynthetic organelle containing many proteins of unknown function. Here we present AlphaFold2 protein structure predictions together with further in silico analyses to infer molecular functions for the three uncharacterised transmembrane apicoplast proteins PF3D7_0622700, PF3D7_0908100 and PF3D7_1021300. The targets PF3D7_0622700 and PF3D7_0908100 are shown herein to belong to the polytopic Major Facilitator and Cation-Proton Antiporter and Anion Transporter superfamilies respectively, confirming previous suspicions for PF3D7_0908100 of a transporter function. Importantly, our docking screens further suggest pyridoxal-5-phosphate may be transported by PF3D7_0622700, and PF3D7_0908100 likely transports a larger negatively charged metabolite. These findings will help direct experimental assays to confirm what apicoplast metabolites these proteins may transport. PF3D7_1021300 is proposed to possess a six transmembrane alpha-helix domain of a currently unknown fold which may also possess a transporter molecular function. This work highlights the power of high accuracy protein structure predictions to illuminate proteins of unknown structure and function.

bioinformatics↗

bioRxiv2022

Slice'N'Dice: Maximising the value of predicted models for structural biologists

With the advent of next generation modelling methods, such as AlphaFold2, structural biologists are increasingly using predicted structures as search models for Molecular Replacement (MR) when experimental structures of homologues are unavailable. Inaccuracy in domain-domain orientations is often a key limitation when using predicted models for MR. SliceNDice is a software package designed to address this issue by first slicing models into distinct structural units and then automatically placing the slices using Phaser. The slicing step can use AlphaFold2s predicted aligned error (PAE), or can operate via a variety of C atom clustering algorithms, extending applicability to structures of any origin. The number of splits can be selected by the user. SliceNDice is available in CCP4 8.0 and is currently being adapted for cryo-EM use cases.

bioinformatics↗