Biology subjects

Malhotra, S.

Publications and source records attributed to Malhotra, S..

At least 19 recordsLinked to original sources

bioRxiv2026

HANSEN: An Integrated Structural and Functional Proteome Resource for Structure-Guided Drug Discovery in Mycobacterium leprae

Leprosy remains a leading infectious cause of preventable disability, yet its causative agent, Mycobacterium leprae (M. leprae), is structurally under-characterised. Only ten Protein Data Bank (PDB) entries represent seven of its 1,603 protein-coding genes. We present HANSEN, a proteome-wide structural and functional resource for M. leprae. Monomeric and oligomeric models were generated with AlphaFold 3, Boltz-1, Boltz-2 and Chai-1, and annotated with per-residue confidence, predicted aligned error and, for assemblies, interface confidence. Ligand-binding pockets were predicted with AF2BIND, P2Rank and fpocket, template-derived ligands were modelled within oligomeric complexes, residue-level B-cell epitope propensity was estimated with DiscoTope-3.0, and gene essentiality was transferred from Mycobacterium tuberculosis transposon-sequencing labels. These features are integrated in a relational database with interactive visualisation and combined into a calibrated Target Priority Score that ranks all 1,603 proteins into four tiers and recovers established antimycobacterial targets. HANSEN (https://hansen-leprosy.medschl.cam.ac.uk/home) provides a practical basis for target prioritisation and structure-guided drug discovery in leprosy.

bioinformatics↗

bioRxiv2026

A dual-function variant on chromosome 17 regulates circRNA expression and splicing in multiple sclerosis

Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system with a complex etiology. Recent genomic studies highlight the contribution of expression quantitative trait loci (eQTLs) in modulating gene expression and disease susceptibility. Given the emerging role of circular RNAs (circRNAs) in MS, we hypothesized that genetic variants may regulate circRNA expression through circRNA-specific eQTLs (circ-eQTLs). We performed a cis-circ-eQTL analysis integrating circRNA expression and whole-genome genotyping data from 30 MS patients and 18 healthy controls using a linear regression model adjusted for disease status and sex. Candidate circ-eQTLs were prioritized based on MS-associated regions and known splicing QTLs (sQTLs) from GTEx and validated in an independent cohort (67 MS, 64 controls). Association analysis in a larger cohort (2831 MS, 3191 controls) evaluated two candidate variants for MS risk. We identified 42,077 significant cis-circ-eQTLs and validated three. Two SNPs, rs7214410 and rs11079784, modulated hsa_circ_0106983 expression, and rs7214410 also acted as an sQTL affecting EFCAB13 splicing. rs7214410 showed stronger association with MS than rs11079784. Our findings reveal extensive genetic regulation of circRNA expression and highlight rs7214410 as a dual-function variant refining the MS susceptibility locus on chromosome 17.

genetics↗

bioRxiv2026

DPI-score: A deep learning-based metric for assessing protein-protein interfaces in cryo-EM derived assemblies

Advances in cryoEM have led to a surge in high-resolution structures; however, model building and refinement at resolutions (>=3 [A]) and in regions with variable local resolution remain challenging, making validation essential. CryoEM derived assemblies often contain extensive protein-protein interfaces, however, most existing validation metrics focus on density fit or overall geometry without directly assessing interface quality. To address this, we present DPI-Score, a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived complexes. The method uses only raw structural coordinates of interface atoms, without requiring engineered features, and achieves 87.53% validation accuracy. DPI-Score was applied to 29,120 interfaces from 6,011 fitted entries with resolutions worse than 3 [A] in the Electron Microscopy Data Bank. Here, we show that DPI-Score provides complementary information to existing validation metrics and can identify interface errors in modelled assemblies that are not detected by density-based scores alone.

bioinformatics↗

bioRxiv2026

Stitching genomics data to protein structures: Virulence factors in non-O157 Shiga toxin-producing Escherichia coli

The genetic diversity of the Shiga toxin-producing Escherichia coli (STEC) sub-species group poses major challenges for assigning pathogenic potential, and for accurate diagnostics. Although existing risk-based frameworks work for the predominant serotypes e.g. STEC O157 or O26, they are less useful for more diverse genotypes, with variation in the genetic complement of virulence factors that is responsible for disease outcome. Moreover, genetic diversity occurs at the virulence factor allele level. We investigated the functional potential of genetic variation at a whole genome level and at an allele variant level, based on the premise that genomic variability underlies phenotypic traits including disease outcomes. We analysed 286 non-O157 STEC genomes for their virulence factor complement and determined co-occurrence patterns. Genetic variation in the well-characterized virulence factor intimin (eae) and its receptor (tir) was analyzed at a sequence level, and by modelling the three-dimensional protein-protein complex using AlphaFold3 and homology modelling. Different subtypes of Eae were shown to preserve their interaction with the receptor Tir by retaining the interactions at the protein-protein interface. The study shows that virulence factor characterisation in association with 3-D modelling of protein complexes can aid genomic analysis for risk assessment, and inform on functional mechanisms for pathogenicity.

bioinformatics↗

bioRxiv2025

Automated filtering of particle images in single particle cryoEM

Continued exponential growth in the number of structures resolved by single particle cryoEM, as seen in the last decade, requires ever more effective data analysis workflows. Datasets are rarely homogeneous, demanding a multistep procedure for discarding outliers. Since individual particles are very noisy, either 2D or 3D averages are normally used for discrimination. This becomes challenging when the 2D classes themselves are heterogeneous, leading to selection of contaminants or discarding useful rare views/poses. The 3D model-based discrimination requires trustworthy 3D maps and a correct assignment of Euler angles, which in turn depends on the quality of the initial data and might not be available at the very early stages of the analysis. We propose a novel deep-learning approach for improving quality of single particle datasets. The two-stage procedure consists of denoising single particle images using Variational AutoEncoder framework followed by particle quality filtering based on the score inferred for every particle by Domain Adaptation Neural Network trained on a large data set of categorised 2D averages. This approach allows an automated scoring of noisy raw images using data patterns learned from the high signal-to-noise ratio, externally derived 2D classes. Consequently, a higher quality data set enters computationally expensive steps of the data analysis, reducing the need for protracted and expensive calculations. Importantly, our method does not require any prior knowledge about the data or existence of a 3D model, making it universally applicable. Tests on publicly available datasets demonstrated that our approach largely outperformed 2D class-based particle discrimination. Smaller subsets of the top-scoring particles selected with our method were required to obtain the author-reported 3D model resolution. When applied to the user data in the automated on-the-fly data processing pipeline, the method rescued 30% of cases, which otherwise would not reach confidence threshold required for making decision to proceed to the 3D model refinement. It also led to general improvements in the quality of the 3D models for many datasets which were selected for the high-resolution processing.

biophysics↗

bioRxiv2023

Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying [~]0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the dark proteome. This dark proteome is highly enriched for certain (e.g. CLE, CEP, IDA, PSY) but not other (e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life (e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

plant biology↗

bioRxiv2022

SUMO - In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

bioinformatics↗

bioRxiv2022

The pharmacokinetics and pharmacodynamics of 4-methylumbelliferone and its glucuronide metabolite in mice

Hyaluronan (HA) is an extracellular matrix glycosaminoglycan, with important roles in chronic inflammation, cancer and autoimmunity. 4-methylumbelliferone (4-MU), a small molecule inhibitor of HA synthases, is widely used to study HAs interactions with the surrounding tissues and the immune cells. There is substantial experimental and therapeutic interest in using oral 4-MU to inhibit HA synthesis, but pharmacokinetic and pharmacodynamic data on treatment routes have been lacking. Moreover, it recently became clear that the main metabolite of 4-MU, 4-methlyumbelliferyl glucuronide (4-MUG), is bioactive. We therefore sought to define the pharmacokinetics and pharmacodynamics of 4-MU and its active metabolite 4-MUG in mice. Single dose mouse studies showed that 4-MU administered intravenously (i.v.) resulted in 100-fold higher 4-MU exposure compared to oral (p.o.) administration. The 4-MU ratio AUC i.v./AUC p.o. was 96/1. 4-MUG exposures were much higher than 4-MU exposures after both 4-MU i.v. and p.o. administration, but only small differences in 4-MUG exposure were seen after 4-MU i.v. versus p.o. administration. The 4-MUG metabolite was also administered as a single dose both i.v. and p.o. and showed a 25.9% bioavailability. Compared to 4-MUG p.o. dosing, 1.14 higher 4-MUG exposures were seen after 4-MU p.o. dosing. 4-MU exposure after 4-MUG p.o. administration was minimal but similar to 4-MU exposure after 4-MU p.o. administration. In mice treated for several weeks with 4-MU in chow, the 4-MU concentration immediately drops after treatment was stopped, whereas the 4-MUG concentration showed a peak 1 hour after treatment stop. In a build-up study, 4-MU and 4-MUG treatment in mice lead to a plateau of 4-MU concentration starting at 4 days post treatment start. These 4-MU and 4-MUG concentration findings in vivo will inform future clinical studies and experimental work with 4-MU.

pharmacology and toxicology↗

bioRxiv2022

Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data

ObjectiveStrong magnetic fields from magnetic resonance (MR) scanners induce a Lorentz force that contributes to vertigo and persistent nystagmus. Prior studies have reported a predominantly horizontal direction for healthy subjects in a 7 Tesla (T) MR scanner, with slow phase velocity (SPV) dependent on head orientation. Less is known about vestibular signal behavior for subjects in a weaker, 3T magnetic field, the standard strength used in the Human Connectome Project (HCP). The purpose of this study is to characterize the form and magnitude of nystagmus induced at 3T. MethodsForty-two subjects were studied after being introduced head-first, supine into a Siemens Prisma 3T scanner. Eye movements were recorded in four separate acquisitions over 20 minutes. A biometric eye model was fit to the recordings to derive rotational eye position and then SPV. An anatomical template of the semi-circular canals was fit to the T2 anatomical image from each subject, and used to derive the angle of the B0 magnetic field with respect to the vestibular apparatus. ResultsRecordings from 37 subjects yielded valid measures of eye movements. The population-mean SPV {+/-} SD for the horizontal component was -1.38 {+/-} 1.27 deg/sec, and vertical component was -0.93 {+/-} 1.44 deg/sec, corresponding to drift movement in the rightward and downward direction. Although there was substantial inter-subject variability, persistent nystagmus was present in half of subjects with no significant adaptation over the 20 minute scanning period. The amplitude of vertical drift was correlated with the roll angle of the vestibular system, with a non-zero vertical SPV present at a 0 degree roll. InterpretationNon-habituating vestibular signals of varying amplitude are present in resting state data collected at 3T.

neuroscience↗

bioRxiv2022

A Low-Cost, 3D-Printed Biosensor for Rapid Detection of Escherichia coli

Detection of bacterial pathogens is significant in the fields of food safety, medicine, public health, etc. If bacterial pathogens are not treated promptly, antimicrobial resistance is possible and can lead to morbidity and mortality. Current bacterial detection methodologies rely on laboratory-based techniques that pose limitations such as long turnaround detection times, expensive costs, inadequate accuracy, and required trained specialists. Here, we describe a cost-effective and portable 3D-printed electrochemical biosensor that facilitates rapid detection of certain Escherichia coli (E. coli) strains (DH5, BL21, TOP10, and JM109) within 15 minutes using 500 L of sample and costs $2.50 per test. The sensor displayed an excellent limit of detection (LOD) of 53 cfu, limit of quantification (LOQ) of 270 cfu, and showed cross-reactivity with strains BL21 and JM109 due to shared epitopes. This advantageous diagnostic device is a potential candidate for high-frequency testing at point of care as well as applicable to various fields where pathogen detection is of interest.

bioengineering↗

bioRxiv2022

Identification of bacteria strains using the Recombinase Polymerase Amplification assay on a miniaturized solid-state pH sensor

Rapid identification of bacteria based on nucleic acid amplification allows dealing with the detection of pathogens in clinical, food, and environmental samples. Amplification product must be detected and analyzed by external devices or integrated complicated optical systems. Here, we developed a solid-state pH electrode based on iridium oxide (IrO2) films to measure released hydrogen ions (H+) from isothermal nucleic acid (NA) amplification of bacterial samples. By recombinase polymerase amplification (RPA), we achieved rapid (< 15 min) and sensitive (<30 copies) detection with an accuracy of about 0.03 pH. The RPA-based hydrogen ion sensing assay shows higher specificity, sensitivity, and efficiency as the same polymerase chain reaction (PCR) methods. We initially used the RPA-based sensor to detect E. coli species in laboratory samples. Among, 27 random laboratory samples of E. coli samples, 6 were found to be DH5alpha, 9 BL21, 3 HB101, 6 TOP10, and 3 JM109. The electrical detection of amplification provides generally applicable techniques for the detection of nucleic acid amplification, enabling molecular diagnostic tests in the field and integrating data transmission to the mobile device. These results can be future developed into an efficient tool for rapid on-site detection of bacterial pathogens in clinical samples.

bioengineering↗

bioRxiv2021

The pathobiology of Mycobacterium abscessus revealed through phenogenomic analysis

The medical and scientific response to emerging pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance, and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories 1-5. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example 6, we show that combining high dimensional phenotyping with whole genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Using in vitro and in vivo phenotyping, we discovered three distinct clusters of isolates, each associated with a different clinical outcome. We combined genome-wide association studies (GWAS) with proteome-wide computational structural modelling 7 to define likely causal variants, and employed direct coupling analysis (DCA) 8 to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings, defining a novel secretion system controlling virulence in M. abscessus, and illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.

microbiology↗

bioRxiv2021

The pore conformation of lymphocyte perforin

Perforin is a pore-forming protein that facilitates rapid killing of pathogen-infected or cancerous cells by the immune system. Perforin is released from cytotoxic lymphocytes, together with pro-apoptotic granzymes, to bind to the plasma membrane of the target cell where it oligomerises and forms pores. The pores allow granzyme entry, which rapidly triggers the apoptotic death of the target cell. Here we present a 4 [A] resolution cryo-EM structure of the perforin pore, revealing new inter- and intra-molecular interactions stabilising the pore. During assembly and pore formation, the helix-turn-helix motif at the bend in the central {beta}-sheet moves away from the bend to form an intermolecular contact. Cryo-electron tomography shows that prepores form on the membrane surface with minimal conformational changes. Our findings suggest the sequence of conformational changes underlying oligomerisation and membrane insertion, and explain how several pathogenic mutations affect function.

biochemistry↗

bioRxiv2020

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

bioinformatics↗

bioRxiv2020

Inferring metabolic rewiring in embryonic neural development using single cell data

Metabolism is intricately linked with cell fate changes. Much of this understanding comes from detailed metabolomics studies averaged across a population of cells which may be composed of multiple cell types. Currently, there are no quantitative techniques sensitive enough to assess metabolomics broadly at the single cell level. Here we present scMetNet, a technique that interrogates metabolic rewiring at the single cell resolution and we apply it to murine embryonic development. Our method first confirms the key metabolic pathways, categorized into bioenergetic, epigenetic and biosynthetic, that change as embryonic neural stem cells differentiate and age. It then goes beyond to identify specific sub-networks, such as the cholesterol and mevalonate biosynthesis pathway, that drive the global metabolic changes during neural cortical development. Having such contextual information about metabolic rewiring provides putative mechanisms driving stem cell differentiation and identifies potential targets for regulating neural stem cell and neuronal biology.

systems biology↗

bioRxiv2020

A clinically-relevant polymorphism in the Na+/taurocholate cotransporting polypeptide (NTCP) occurs at a rheostat position

Conventionally, most amino acid substitutions at important protein positions are expected to abolish function. However, in several soluble-globular proteins, we identified a class of non-conserved positions for which various substitutions produced progressive functional changes; we consider these evolutionary “rheostats”. Here, we report a strong rheostat position in the integral membrane protein, Na+/taurocholate cotransporting polypeptide (NTCP), at the site of a pharmacologically-relevant polymorphism (S267F). Functional studies were performed for all 20 substitutions (“S267X”) with three substrates (taurocholate, estrone-3-sulfate and rosuvastatin). The S267X set showed strong rheostatic effects on overall transport, and individual substitutions showed varied effects on transport kinetics (Km and Vmax). However, the outcomes were substrate dependent, indicating altered specificity. To assess protein stability, we measured surface expression and used the Rosetta software suite to model structure and stability changes of S267X. Although buried near the substrate binding site, S267X substitutions were easily accommodated in the NTCP structure model. Across the modest range of changes, calculated stabilities correlated with surface-expression differences, but neither parameter correlated with altered transport. Thus, substitutions at rheostat position 267 had wide-ranging effects on the phenotype of this integral membrane protein. We further propose that polymorphic positions in other proteins might be locations of rheostat positions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

Combining Information from Crosslinks and Monolinks in the Modelling of Protein Structures

Monolinks are produced in a Chemical Crosslinking Mass Spectrometry experiment and are more abundant than crosslinks. They convey residue exposure information, but so far have not been used in the modelling of protein structures. Here we present the Monolink Depth Score (MoDS), for assessing structural models based on the depth of monolinked residues, corresponding to their distance to the nearest bulk water. Using simulated and reprocessed experimental data from the Proteomic Identification Database, we compare the performance of MoDS to MNXL - our previously-developed score for assessing models based on crosslinking data. Our results show that MoDS can be used to effectively score model structures based on monolinks, and that combining it with MNXL leads to overall higher scoring performance. The work strongly supports the use of monolink data in the context of integrative structure determination. We also present XLM-Tools, a programme to assist in this effort, available at: https://github.com/Topf-Lab/XLM-Tools.

bioinformatics↗

bioRxiv2019

Computational Saturation Mutagenesis of the β subunit of RNA polymerase in Mycobacterium leprae

In contrast to the situation with tuberculosis, rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic methods. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations. The functional consequences of missense mutations within the {beta}-subunit of RNA polymerase in Mycobacterium leprae (M. leprae) contribute to phenotypic rifampin resistance outcomes in leprosy. Here we report in-silico saturation mutagenesis of all residues in the {beta}-subunit of RNA polymerase to all other 19 amino acid types and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on {beta}-subunit-RNA and rifampin affinities using state-of-the-art structure, sequence and normal mode analysis-based methods. A total of 21,394 mutations were analysed, and it was noted that mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and concomitant decrease in depth of the mutant residues. The mutations at residues S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall stability, nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the experimentally identified rifampin-resistant mutations in M. leprae indicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations conferring rifampin resistance in leprosy.\n\nAUTHOR SUMMARYEmergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses threat to the leprosy control and elimination measures globally. In the absence of an effective in-vitro system to detect and monitor phenotypic rifampin resistance in leprosy, most of the diagnosis relies on detecting mutations in the drug resistance determining regions of the rpoB gene that encodes the {beta} subunit of RNA polymerase in M. leprae. Few labs in the world perform mouse food pad propagation of M. leprae in the presence of drugs (rifampin) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinical resistance outcomes in leprosy. Here we propose an in-silico saturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or can arise in the {beta} subunit of RNA polymerase in M. leprae. Most of the predicted mutations may not occur in M. leprae due to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a choice for small molecule binding and structure guided drug discovery.

bioinformatics↗