Biology subjects

Simpkin, A. J.

Publications and source records attributed to Simpkin, A. J..

At least 19 recordsLinked to original sources

bioRxiv2026

reactifpTM: an accessible reimplementation of actifpTM

Motivation: The actual interface pTM score (actifpTM) is a modified version of the ipTM score that limits the calculation to only those residues at the interface. Whilst actifpTM provides an effective interface quality score, a limiting factor is that it makes use of the predicted aligned error (PAE) with probabilities, information that is generated during a ColabFold run, but not output by the package or other model prediction software. The consequent inability to generate actifpTM scores for the results of software such as AlphaFold 2 or AlphaFold 3 has limited its adoption. With reactifpTM we address this problem by providing a standalone tool that can be run on the standard outputs of most model prediction packages. Results: Using the same underlying principles as actifpTM, reactifpTM has been developed to use standard output files from model prediction software (a model and corresponding PAE) to perform an actifpTM-like calculation. ColabFold models were generated for a dataset of 1079 known interfaces in the PDB. A strong correlation was shown between actifpTM and reactifpTM for this dataset. Availability and implementation: reactifpTM is coded in Python. All scripts and associated documentation are available from https://github.com/hlasimpk/reactifptm or https://pypi.org/project/reactifptm.

bioinformatics↗

bioRxiv2026

Identification of Dynamic Genetic Influences on DNA Methylation from Birth to Adulthood

Whether and how genetic regulation of DNA methylation (DNAm) change across the lifespan remains unclear. Here, we map age-dependent methylation quantitative trait loci (longitudinal mQTLs) using linear mixed models applied to repeated blood DNAm measures from birth, childhood, adolescence and adulthood in the Avon Longitudinal Study of Parents and Children. We identify 2,210 longitudinal mQTLs (2,393 SNP-CpG pairs; 7.3% trans) and observe consistent genotype-by-age effects in two independent cohorts of diverse ancestries (Pearsons r = 0.85 in the Generation R Study; r = 0.56 in the Drakenstein Child Health Study). Longitudinal mQTLs show increasing effects with age at half of loci and associations with multiple phenotypes. CpGs with longitudinal mQTLs are more heritable and enriched in regulatory elements and pathways related to multicellular organism development and cell adhesion. These results chart dynamic genetic influences on the human methylome and provide a novel perspective on epigenetic regulation.

genomics↗

bioRxiv2026

Structure and signaling mechanism of Helicobacter pylori transducer-like protein D

Chemoreceptors, or methyl-accepting chemotaxis proteins (MCPs), are ancient and widespread prokaryotic sensors that direct taxis in response to stimuli and are attractive targets for therapeutic control of bacteria 1-4. Decades of study have yielded substantial mechanistic insight into chemoreceptor function, but the absence of high-resolution full-length structures containing ligand-binding domains (LBD) has limited understanding of how effector sensing is structurally coupled to long-range signal transduction. Here, we present the intact structure of the chemoreceptor transducer-like protein D (TlpD) from the gastric pathogen Helicobacter pylori, in complex with its ligand Zn2+, determined by X-ray crystallography in two crystal forms at 2.4-3.0 [A]. Three different conformations are captured, revealing how interactions in the ligand-binding site of the chemoreceptor zinc-binding (CZB) domain are interconnected with the distal kinase interface. Small changes at the ligand-binding site coincide with cascades of side-chain rearrangements across the dimer, distortion of the receptor coiled-coil, and conformational and dynamic shifts at the kinase interface over 140 [A] away. These near-atomic resolution structures provide a framework for understanding cooperativity and allosteric communication in chemoreceptors, and establish a representative model for a widespread class of soluble chemoreceptors important in bacterial pathogenesis 2,5.

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

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

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↗

bioRxiv2023

An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution

ImportanceDNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination. ObjectiveOur study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied. DesignA series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022). SettingMBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN. ParticipantsTwo population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic). ExposuresEight social exposures encompassing racial discrimination, economic adversity, and air pollution. Main outcomeGenome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection. ResultsWe observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health. Conclusions and RelevanceOverall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities. Key pointsO_ST_ABSQuestionC_ST_ABSCould DNAm be a mechanism by which adversity becomes embodied? FindingsTraffic-related air pollution exposure may induce epigenetic changes related to inflammatory processes and there are suggestive associations with measures of structural racism MeaningDNAm may be a biological mechanism through which structural racism and air pollution become biologically embodied

bioinformatics↗

bioRxiv2023

Deep Learning-based structural and functional annotation of Pandoravirus hypothetical proteins

Giant viruses, including Pandoraviruses, contain large amounts of genomic dark matter - genes encoding proteins of unknown function. New generation, deep learning-based protein structure modelling offers new opportunities to apply structure-based function inference to these sequences, often labelled as hypothetical proteins. However, the AlphaFold Protein Structure Database, a convenient resource covering the majority of UniProt, currently lacks models for most viral proteins. Here, we apply a panoply of predictive methods to protein structure predictions representative of large clusters of hypothetical proteins shared among four Pandoraviruses. In several cases, strong functional predictions can be made. Thus, we identify a likely nucleotidyltransferase putatively involved in viral tRNA maturation that has a BTB domain presumably involved in protein-protein interactions. We further identify a cluster of membrane channel sequences presenting three paralogous families which may, as seen in other giant viruses, induce host cell membrane depolarization. And we identify homologues of calcium-activated potassium channel beta subunits and pinpoint their likely Acanthamoeba cellular alpha subunit counterparts. Despite these successes, many other clusters remain cryptic, having folds that are either too functionally promiscuous or too novel to provide strong clues as to their role. These results suggest that significant structural and functional novelty remains to be uncovered in the giant virus proteomes.

bioinformatics↗

bioRxiv2023

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non- canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

biophysics↗

bioRxiv2022

Structural Insights into Pink-eyed Dilution Protein (Oca2)

Recent innovations in computational structural biology have opened an opportunity to revise our current understanding of the structure and function of clinically important proteins. This study centres on human Oca2 which is located on mature melanosomal membranes. Mutations of Oca2 can result in a form of oculocutanous albinism which is the most prevalent and visually identifiable form of albinism. Sequence analysis predicts Oca2 to be a member of the SLC13 transporter family but it has not been classified into any existing SLC families. The modelling of Oca2 with AlphaFold2 and other advanced methods shows that, like SLC13 members, it consists of a scaffold and transport domain and displays a pseudo inverted repeat topology that includes re-entrant loops. This finding contradicts the prevailing consensus view of its topology. In addition to the scaffold and transport domains the presence of a cryptic GOLD domain is revealed that is likely responsible for its trafficking from the endoplasmic reticulum to the Golgi prior to localisation at the melanosomes and possesses known glycosylation sites. Analysis of the putative ligand binding site of the model shows the presence of highly conserved key asparagine residues that suggest Oca2 may be a Na+/dicarboxylate symporter. Known critical pathogenic mutations map to structural features present in the repeat regions that form the transport domain. Exploiting the AlphaFold2 multimeric modelling protocol in combination with conventional homology modelling allowed the building of a plausible homodimer in both an inward- and outward-facing conformation supporting an elevator-type transport mechanism.

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↗

bioRxiv2021

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip

BackgroundDNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilising precious or limited DNA sources. Materials and methodsWe use a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. ResultsData are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; however samples with a total input as low as 40ng pass standard quality control tests, and we observe little evidence that low input DNA obscures the associations between DNAm and two phenotypes, age and smoking status. ConclusionsDNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

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↗

bioRxiv2021

Assessing the utility of CASP14 models for molecular replacement

The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real-world application. In CASP7, the metric for molecular replacement assessment involved full likelihood-based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood-based rigid-body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined likelihood (LLG) score. This enabled multi-copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative-expected-LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X-ray, NMR or cryo-EM. We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing.

bioinformatics↗

bioRxiv2021

Informing shigellosis prevention and control through pathogen genomics

Shigella spp. are the leading bacterial cause of severe childhood diarrhoea in low- and middle-income countries (LMIC), are increasingly antimicrobial resistant and have no licensed vaccine. We performed genomic analyses of 1246 systematically collected shigellae from seven LMIC to inform control and identify factors that could limit the effectiveness of current approaches. We found that S. sonnei contributes [≥]20-fold more disease than other Shigella species relative to its genomic diversity and highlight existing diversity and adaptative capacity among S. flexneri that may generate vaccine escape variants in <6 months. Furthermore, we show convergent evolution of resistance against the current recommended antimicrobial among shigellae. This demonstrates the urgent need to integrate existing genomic diversity into vaccine and treatment plans for Shigella, and other pathogens.

microbiology↗