Biology subjects

Rigden, D. J.

Publications and source records attributed to Rigden, D. J..

At least 19 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↗

bioRxiv2026

OpenAc4C: A gateway to decode the landscape, regulation and pathogenesis of N4-acetylcytidine (ac4C) epitranscriptome

N4-acetylcytidine (ac4C) is an ancient and highly conserved chemical marker found in all domains of life. Recent advancements in sequencing techniques have enabled the functional analysis of ac4C occurrence by accurately capturing its locations and levels, shedding light on its significant regulatory potential and emerging role in diseases. The OpenAc4C, the first comprehensive knowledgebase exclusively designed for unraveling the ac4C epitranscriptome across diverse species, spanning vertebrates, mammals, insects, fungi, plants, bacteria, archaea, and viruses. By mining a large array of ac4C epitranscriptome datasets with deep learning-based pipelines, OpenAc4C features a collection of 536,745 ac4C sites identified from four distinct next-generation sequencing (NGS)-based techniques, alongside novel insights from Oxford Nanopore direct RNA sequencing (ONT)-based samples, encompassing a total of 33 species. Beyond the ac4C landscape, a total of 536,986 ac4C-affecting variants were identified in seven species. Among them, 4,766 pathogenic ac4C-SNPs may drive ac4C dysregulation with implications for disease pathogenesis. In addition, OpenAc4C offers a user-friendly graphical interface and a web-based analysis platform for comprehensive querying and interactive exploration of the database collections. Together, OpenAc4C will serve as a valuable integrated resource to facilitate studies of ac4C modification. It is freely accessible at: www.rnamd.org/ac4cportal.

bioinformatics↗

bioRxiv2025

Can AlphaFold3 predict unseen amyloid polymorphs?

Amyloid fibrils are highly ordered, aggregation-prone protein assemblies implicated in numerous neurodegenerative diseases. Their formation arises from protein misfolding, producing structurally distinct polymorphs that have historically been difficult to predict computationally. While AlphaFold2 excels at predicting monomeric globular proteins, it struggles with protein complexes. Here, we show that AlphaFold3 provides a new opportunity to model amyloid structure and capture polymorph heterogeneity. Using TM-score based clustering of known -synuclein fibril structures, we establish a polymorph nomenclature and demonstrate that AlphaFold3 can reproduce known fibril architectures and predict alternative conformations. These results highlight AlphaFold3s potential for studying amyloid proteins, enabling systematic investigation of amyloid polymorphism.

bioinformatics↗

bioRxiv2025

The promise of AlphaFold for gene structure annotation

BackgroundAs sequencing technology improves, more genomes become available. Most lack annotation, automated methods are error prone, and few genomes are ever manually curated due to time and cost. Protein structure prediction software may provide new angles for assessing and improving gene models without requiring experimental data. In this paper, we explore whether scores from protein structure prediction can aid in scoring gene model quality. We chose three species (Fusarium graminearum, Toxoplasma gondii, and Aspergillus fumigatus) from the VEuPathDB database which have collectively undergone more than 1000 manual curation events. We modelled translations of the gene models with AlphaFold 3, before and after curation, collecting various scores. Then we carried out structure searching of the PDB with Foldseek and sequence-based domain identification using InterProScan. We profiled the scores produced by these methods to identify those best for gene model assessment. ResultsAlphaFold 3 scores strongly favoured manually improved over pre-improvement models, supporting 75% of manually-curated changes in F. graminearum, 65% in T. gondii, and 84% in A. fumigatus (the lower percentage in T. gondii attributed to a high level of disorder). Further, combining scores across multiple tools (AlphaFold 3, Foldseek and InterProScan) provided additional improvements in model scoring. ConclusionOverall, the most discriminative scores combined outputs of AlphaFold 3 and Foldseek. Our results therefore highlight the potential of scores derived from deep learning-based protein structure prediction for scoring gene models in the absence of experimental data. Future work should focus on intrinsically disordered regions and developing integrated tools to apply this approach.

bioinformatics↗

bioRxiv2025

ANUBI: A Platform for Affinity Optimization of Proteins and Peptides in Drug Design

The increasing availability of computational power is opening unprecedented opportunities in computational biology and drug design. Computer simulations based on physical models can now reproduce or replace critical biophysical experiments such as binding affinity evaluations in the drug screening process. Here we present ANUBI (ANUBI Nexus for Understanding Binding Interactions), a software package that automates sequence space exploration and binding free energy calculations to optimize protein or peptide drug candidates for improved target binding. Starting from a user-provided molecular model of the drug-target interaction, ANUBI systematically evaluates point mutations in selected regions using Monte Carlo methodology, retaining favorable mutations based on calculated binding affinity differences. We demonstrate that this approach efficiently samples sequence space, generating dozens of optimized variants in timeframes comparable to experimental approaches at substantially reduced cost. As proof of concept, we applied ANUBI to an antibody-antigen complex and a peptide-protein interaction, identifying variants with significantly improved predicted binding energy (approximately 20 kcal/mol, calculated using the MMPBSA method), within 20 days of computation on a single GPU. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/683353v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@17fa242org.highwire.dtl.DTLVardef@75cf1dorg.highwire.dtl.DTLVardef@70888aorg.highwire.dtl.DTLVardef@14412d_HPS_FORMAT_FIGEXP M_FIG TOC GRAPHIC C_FIG

biophysics↗

bioRxiv2025

Predicting Protein Crystal Solvent Content from Patterson Maps Using Machine Learning

Estimating the solvent content of protein crystals is fundamental to identifying the correct symmetry and phasing of the unit cell. Typically, the number of molecules in the asymmetric unit is not known and probabilistic methods are used based on statistics derived from the Protein Data Bank (PDB). These methods tend to predict the number of molecules incorrectly in around 20% of cases, which can significantly impede the structure solution pipeline. Here multiple machine learning approaches are investigated to predict solvent content using Patterson Maps. Several architectures are shown to give a significant improvement over current approaches, with prediction errors being reduced by over 50%. In addition, the potential of embedded representations of Patterson Maps for clustering is demonstrated, which could lead to new approaches for identifying similar structures when processing novel data.

molecular biology↗

bioRxiv2025

The role of Akkermansia muciniphila sulfatases in colonic mucinutilisation

Akkermansia muciniphila, an obligate mucin degrader, is a major member of the human colonic microbiota and has been associated positive health outcomes. Mucins are complex glycoproteins that contain heavily sulfated O-glycans and form the protective colonic mucus layer. Bacterial carbohydrate sulfatases are required to metabolise these heavily sulfated mucin glycans and excessive bacterial foraging has been associated with several diseases. Sulfatases have been linked with inflammatory bowel disease, making these microbiota enzymes potential drug targets. A. muciniphila expresses carbohydrate sulfatases that can act on colonic mucins yet their roles in its metabolism remain opaque. Our data reveal that A. muciniphila requires glycopeptides/protein forms of colonic mucin for metabolism and its sulfatases have unique adaptations compared to Bacteroides species. Localisation studies reveal that desulfation of N-acetyl-D-glucosamine, but not D-galactose, is exclusively periplasmic. A cell surface sulfatase has a novel carbohydrate binding module that binds to colonic mucin. This paints a contrasting picture of sulfated mucin metabolism by Akkermansia muciniphila versus Bacteroides species. These data will be important for understanding the contexts for Akkermansia muciniphilas positive health correlations.

biochemistry↗

bioRxiv2025

AlphaFold-driven discovery of ORP-PIP phosphatase interactions using new generation confidence scores

Non-vesicular lipid transport contributes to the regulation of membrane composition and organelle function at membrane contact sites. OSBP-related proteins (ORPs) are central to this process, yet their interaction networks remain incompletely defined. Here, we systematically screened potential interactions between ORPs and phosphoinositide 3-, 4-, and 5-phosphatases (PIPs) using AlphaPulldown2, AlphaFold2-Multimer, and AlphaFold3. We established a pipeline for model generation by combining AlphaFold2-Multimer predictions (including five-replicates) with an AlphaPulldown2 interaction screen across around 200 protein pairs, and with AlphaFold3 predictions including lipid-bound and multimeric assemblies. Interface confidence was assessed for consistency using the weighted ipTM+pTM metric, actifpTM, new generation ipSAE scoring, and FoldSeek-Multimer clustering. We further evaluated the protein pairs biological plausibility based on subcellular localisation data, in silico membrane insertion, evolutionary conservation via ConSurf, and protein binding interface analysis using the deep learning tool PeSTo. This integrative pipeline uncovered functionally conserved binding modes in the SAC1 lipid phosphatase with the ORP family, particularly with ORP11, and predicted functionally relevant protein-lipid interfaces.

bioinformatics↗

bioRxiv2025

From paleness to albinism: Contribution of OCA2 exon 10 skipping to hypopigmentation

Pathogenic variants in the OCA2 gene result in oculocutaneous albinism. In humans and several other mammalian species, OCA2 is transcribed into two coding mRNAs, a major transcript that encodes the full-length protein and a minor transcript that skips in-frame exon 10 and has no identified function. By contrast, rodents and non-mammalian vertebrates only express the major transcript. Several rare variants of human OCA2 have been reported to be pathogenic due to increased exon 10 skipping. To understand the vulnerability of human OCA2 to exon 10 skipping and its impact on pigmentation, we first functionally tested a series of rare missense variants spread across exon 10. We found that each variant significantly influences the skipping ratio either positively or negatively. By combining human and murine sequences in functional assays, we found that the skipping ratio depends on particular exonic and intronic combinations. Next, we focused on the most frequent exonic single nucleotide variant of human OCA2, rs1800404-T (c.1065G>A/p.Ala355=), located in exon 10. We show that this variant significantly promotes exon 10 skipping on its own and exhibits an additive effect in cis to previously identified splicing variants likely contributing to their pathogenicity. Association studies reveal that rs1800404-T is tightly associated with lighter skin and hair pigmentation in a representative European population, as has been observed in other populations around the world. Our structural models of the skipped transcript-derived protein support causality by predicting that it could exert a dominant-negative effect, which is consistent with a dose-dependent hypopigmentation response in both pathological and physiological contexts. Overall, the modulation of OCA2 exon 10 skipping by both benign and pathogenic variants provides a basis for improving the genetic diagnosis of albinism, and paves the way for research into the molecular and evolutionary mechanisms behind human pigmentation diversity.

genetics↗

bioRxiv2025

AlphaFold 3-enabled in silico exploration of PGAM1 interactions in cancer

Cancer cell metabolism is commonly reprogrammed to favour glycolysis over oxidative phosphorylation, even under aerobic conditions, a phenomenon known as the Warburg effect. A key enzyme implicated in this shift is phosphoglycerate mutase 1 (PGAM1), which catalyses the conversion of 3-phosphoglycerate to 2-phosphoglycerate. The human enzyme is dependent on cofactor 2,3-bisphosphoglycerate which can phosphorylate and thereby activate the enzyme at histidine 11 (H11). A recently characterised moonlighting activity of pyruvate kinase M2 (PKM2), in its monomeric or dimeric forms, leads to phosphoenolpyruvate (PEP)-dependent phosphorylation of PGAM1 at the same position. Crucially, this phosphorylation is dependent on prior tyrosine 119 (Y119) phosphorylation of PGAM1 by Src kinase, itself activated by oncogenic growth factors. Using AlphaFold 3 (AF3), this study models the conformational changes induced by PGAM1 Y119 phosphorylation and investigates the molecular basis for its role in facilitating PEP-dependent phosphorylation at H11. Structural comparisons suggest that Y119 phosphorylation induces rearrangement of the C-terminal tail of PGAM1, opening the catalytic site around H11 to enable binding of phosphoenolpyruvate (PEP). Use of molecular docking (Webina, SwissDock, and DiffDock) found that AF3 generated models of PGAM1 with Y119 phosphorylation showed enhanced binding of PEP in comparison to non-phosphorylated PGAM1. However, extensive protein-protein docking (ClusPro, AF3 multimer generation) failed to identify configurations of PGAM1 and PKM2 where catalytic sites were proximal. Overall, this study supports a model in which phosphorylation of PGAM1 at Y119 enables access to the active site for PEP, thus enhancing its enzymatic activation. These findings underscore the critical role of post-translational modifications in modulating protein function and exemplify the utility of AF3 in predicting PTM-induced structural changes relevant to cancer metabolism.

bioinformatics↗

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

m6AConquer: a Data Resource for Unified Quantification and Integration of m6A Detection Techniques

N6-methyladenosine (m6A) is the most prevalent RNA modification in mammalian cells and the most extensively studied epitranscriptomic mark. More than 10 m6A detection techniques have been proposed to measure m6A stoichiometry at either the site or peak level. However, these detection techniques are processed through heterogeneous pipelines, using different computational filters and reference features, leading to difficulties in fully harnessing data integration and analysis across orthogonal m6A detection techniques. Our m6AConquer (Consistent Quantification of External m6A RNA Modification Data) tackles this challenge by establishing a consistent multi-omics data-sharing standard, summarizing quantitative m6A data from 10 detection techniques using a unified reference feature set. Furthermore, we standardize site calling and m6A count matrix normalization procedures across platforms through a computational framework that accounts for over-dispersion in m6A levels. Available m6A detection techniques can be categorized into four types: antibody-assisted, chemical-assisted, enzyme-assisted, and direct-RNA sequencing. We leverage this categorization to develop a reproducibility-based integration framework that enables the reliable detection of high-confidence m6A sites confirmed across orthogonal techniques. Empirical evaluations report that both the site-calling and the integration framework outperform common alternatives, enhancing biological relevance. We apply interpretable machine learning models on our integrated high-confidence sites, and the result consistently identify proximity to intron-exon junctions as the driving predictor of m6A site coordinates across different techniques, demonstrating the high quality of the data curated in m6AConquer. m6AConquer webserver is freely accessible under: http://rnamd.org/m6aconquer. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/612173v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@f9d1a8org.highwire.dtl.DTLVardef@1339b41org.highwire.dtl.DTLVardef@115ececorg.highwire.dtl.DTLVardef@7580aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Investigating Medin Cleavage Accessibility in MfgE8: Conformational Insights Derived from Molecular Dynamics Simulations and AlphaFold2 Models

Recent studies have indicated that the human amyloidogenic protein medin is associated with a range of vascular diseases, including aortic aneurysms, vascular dementia, and Alzheimers disease. Medin accumulates in the vasculature with age, leading to endothelial dysfunction through oxidative and nitrative stress and inducing pro-inflammatory activation. Medin is a cleavage product from the C2 domain of MfgE8. The exact mechanism of medin production from MfgE8 is unknown, with crystal structures of homologous C2 domains suggesting that the cleavage sites are buried, requiring a conformational transition for medin production. Molecular dynamics simulations can explore a wide range of conformations, from small-scale bond rotations to large-scale changes like protein folding or ligand binding. This study employed a combination of full-atom and coarse-grained molecular dynamics simulations, along with CONCOORD- and AlphaFold2-generated models, to investigate MfgE8 conformations and their implications for medin cleavage site accessibility. The simulations revealed that MfgE8 tends to adopt a compact conformation with the RGD motif, important for cell attachment within the N-terminal domain, and the medin region in the C-terminal domain close in proximity. Formation of this compact structure is facilitated by interdomain electrostatic interactions that promote stability and in turn decrease the solvent-accessible surface area of the medin region and particularly the C-terminal medin cleavage site. This data enhances current knowledge on medin generation to propose that alterations in local environmental conditions, possibly through changes in glycosylation or other post-translational modifications are required to induce MfgE8 to unfold partially or fully: this would result in enhanced accessibility of the cleavage sites and therefore enable medin generation.

bioinformatics↗

bioRxiv2024

Using deep learning predictions reveals a large number of register errors in PDB deposits

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. We have previously established a novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold 2. It is particularly well-suited to the detection of register errors. Importantly, the new approach is orthogonal to traditional methods based on stereochemistry or map-model agreement, and is resolution-independent. Here we identify thousands of likely register errors by scanning 3-5[A] resolution structures in the PDB. Unlike most methods, application of our approach yields suggested corrections to the register of affected regions which we show, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterised, but we expect our approach to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping ensure the accuracy of future deposits.

bioinformatics↗

bioRxiv2024

In the AlphaFold era, when is experimental phasing of protein crystals still required?

The availability of highly accurate protein structure predictions from AlphaFold 2 (AF2) and similar tools has hugely expanded the applicability of Molecular Replacement (MR) for crystal structure solution. Many structures solve routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here we address the question using a large set of PDB deposits that were solved by SAD. A large majority (87%) solve using unedited or minimally edited AF2 predictions. A further 17 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using SliceNDice, although different splitting methods succeed on slightly different sets of cases. We also find that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set did not yield to any form of MR tested here, offering valuable hints as to the number and characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

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↗

bioRxiv2021

MrParse: Finding homologues in the PDB and the EBI AlphaFold database for Molecular Replacement and more

Crystallographers have an array of search model options for structure solution by Molecular Replacement (MR). Well-established options of homologous experimental structures and regular secondary structure elements or motifs are increasingly supplemented by computational modelling. Such modelling may be carried out locally or use pre-calculated predictions retrieved from databases such as the EBI AlphaFold database. MrParse is a new pipeline to help streamline the decision process in MR by consolidating bioinformatic predictions in one place. When reflection data are provided, MrParse can rank any homologues found using eLLG which indicates the likelihood that a given search model will work in MR. In-built displays of predicted secondary structure, coiled-coil and transmembrane regions further inform the choice of MR protocol. MrParse can also identify and rank homologues in the EBI AlphaFold database, a function that will also interest other structural biologists and bioinformaticians.

bioinformatics↗