Biology subjects

Joseph, A. P.

Publications and source records attributed to Joseph, A. P..

11 recordsLinked to original sources

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↗

bioRxiv2025

TIF1γ drives oral cancer recurrence by the transcriptional regulation of self-renewal genes, such as HES1

TIF1{gamma} is an E3 ubiquitin ligase and key mediator of the noncanonical TGF-{beta} signaling pathway. Initially characterized for its developmental functions, TIF1{gamma} is essential for maintaining the pluripotency of adult stem cells, including long-term hematopoietic stem cells. Although TGF-{beta} signaling contributes to cancer progression and recurrence, TIF1{gamma} has traditionally been regarded as a tumor suppressor due to its inhibition of oncogenes involved in epithelial-mesenchymal transition. However, clinical reports have associated high TIF1{gamma} expression at advanced cancer stages with poor prognosis. To elucidate the mechanism underlying this paradox, we identify a previously unrecognized role of TIF1{gamma} in promoting the self-renewal capacity of oral cancer cells, thereby contributing to disease recurrence. Phosphoproteomic profiling of self-renewal-enriched cells revealed activation of a noncanonical TGF-{beta} pathway. Using extreme limiting dilution assays, an ALDH1A1-DsRed2 cancer stem cell reporter, multiple oral cancer cell lines, primary 3D cultures on alginate matrix, and orthotopic mouse models, we demonstrate that TIF1{gamma} depletion significantly reduces self-renewal and prolongs disease-free survival. Immunoprecipitation (IP)-LC/MS/MS analysis identified transcriptional regulators within the TIF1{gamma} interactome, including TRRAP and H2A.Z, which were validated by IP and FRET assays. ChIP and IP studies further revealed that during self-renewal enrichment, TRRAP acetylates H2A.Z, decreasing the chromatin occupancy of its unacetylated form. Acetylated H2A.Z is subsequently recognized by TIF1{gamma}, which monoubiquitinates H2B at promoters of self-renewal genes such as HES1, initiating transcription. In alignment with findings from mouse neocortical development, where a Notch-independent Hes1-expressing (NIHes1) population defines primitive quiescent stem cells, we show that TIF1{gamma} acts as an acetylation reader specifically at the NIHES1 promoter region of HES1. TIF1{gamma} depletion drives NIHES1 cells toward a Notch-dependent HES1 (NDHES1) identity. RNA-seq confirmed reversal of 100 NIHES1-specific genes following TIF1{gamma} loss, along with downregulation of pluripotency-associated genes found in embryonic stem cells, supporting a critical role for TIF1{gamma} in maintaining primitive cancer stem cell states. Consistent with our in vivo findings, primary oral cancer samples showed that increased frequencies of TIF1{gamma}+/TRRAP+/H2A.Z- cells strongly predict recurrence. Given that the histone acetylation-reader function of TIF1{gamma} drives poor prognosis, our findings suggest the TIF1{gamma} bromodomain as a potential therapeutic target requiring further investigation.

cancer biology↗

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↗

bioRxiv2025

Structural basis of DNA recombination catalysis and regulation by phiC31 integrase

Large serine integrases catalyse the integration and excision of bacteriophage (phage) DNA genomes into and from the genomes of their bacterial hosts by site-specific recombination1. These recombination reactions are notable for their unidirectionality: integrase recognises and efficiently recombines short (40-50-bp) sequences in the phage (attP) and bacterial (attB) genomes, but is inactive on the product sites attL and attR flanking the inserted prophage, ensuring stable integration. The reverse reaction (excision) only occurs in the presence of a second phage-encoded protein, the Recombination Directionality Factor (RDF). Their strict recombination directionality has made serine integrases versatile tools in emerging genome-editing technologies2-5; however, the structural basis of directionality remains. Here we report structures of {phi}C31 integrase, the most-studied and most widely used member of the serine integrase family, in complexes with its DNA recombination sites, with and without its RDF. These structures correspond to four key mechanism steps: integration of the phage DNA genome (attP x attB); excision of the integrated prophage DNA, mediated by integrase and RDF (attL x attR); inhibition of integrase-catalysed excision in the absence of RDF (attL dimer complex); and inhibition of integrase-catalysed integration in the presence of RDF (attB dimer complex). Our data provide a mechanistic understanding of how the serine integrase recombination system establishes and regulates phage lysogeny, and lay the foundation for future development of integrase-based genome-editing technologies.

biochemistry↗

bioRxiv2024

Polyunsaturated Fatty Acid - mediated Cellular Rejuvenation for Reversing Age-related Vision Decline

The retina is uniquely enriched in polyunsaturated fatty acids (PUFAs), which are primarily localized in cell membranes, where they govern membrane biophysical properties such as diffusion, permeability, domain formation, and curvature generation. During aging, alterations in lipid metabolism lead to reduced content of very long-chain PUFAs (VLC-PUFAs) in the retina, and this decline is associated with normal age-related visual decline and pathological age-related macular degeneration (AMD). ELOVL2 (Elongation of very-long-chain fatty acids-like 2) encodes a transmembrane protein that produces precursors to docosahexaenoic acid (DHA) and VLC-PUFAs, and methylation level of its promoter is currently the best predictor of chronological age. Here, we show that mice lacking ELOVL2-specific enzymatic activity (Elovl2C234W) have impaired contrast sensitivity and slower rod response recovery following bright light exposure. Intravitreal supplementation with the direct product of ELOVL2, 24:5n-3, in aged animals significantly improved visual function and reduced accumulation of ApoE, HTRA1 and complement proteins in sub-RPE deposits. At the molecular level, the gene expression pattern observed in retinas supplemented with 24:5n-3 exhibited a partial rejuvenation profile, including decreased expression of aging-related genes and a transcriptomic signature of younger retina. Finally, we present the first human genetic data showing significant association of several variants in the human ELOVL2 locus with the onset of intermediate AMD, underlying the translational significance of our findings. In sum, our study identifies novel therapeutic opportunities and defines ELOVL2 as a promising target for interventions aimed at preventing age-related vision loss.

molecular biology↗

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

Making the invisible enemy visible

During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

molecular biology↗

bioRxiv2020

TopoStats - an automated tracing program for AFM images

We present TopoStats, a Python toolkit for automated editing and analysis of Atomic Force Microscopy images. The program automates identification and tracing of individual molecules in circular and linear conformations without user input. TopoStats was able to identify and trace a range of molecules within AFM images, finding, on average, 90% of all individual molecules and molecular assemblies within a wide field of view, and without the need for prior processing. DNA minicircles of varying size, DNA origami rings and pore forming proteins were identified and accurately traced with contour lengths of traces typically within 10 nm of the predicted contour length. TopoStats was also able to reliably identify and trace linear and enclosed circular molecules within a mixed population. The program is freely available via GitHub (https://github.com/afm-spm/TopoStats) and is intended to be modified and adapted for use if required.

biophysics↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2018

CryoEM reveals how the complement membrane attack complex ruptures lipid bilayers

The membrane attack complex (MAC) is one of the immune systems first responders. Complement proteins assemble on target membranes to form pores that lyse pathogens and impact tissue homeostasis of self-cells. How MAC disrupts the membrane barrier remains unclear. Here we use electron cryo-microscopy and flicker spectroscopy to show that MAC interacts with lipid bilayers in two distinct ways. Whereas C6 and C7 associate with the outer leaflet and reduce the energy for membrane bending, C8 and C9 traverse the bilayer increasing membrane rigidity. CryoEM reconstructions reveal plasticity of the MAC pore and demonstrate how C5b6 acts as a platform, directing assembly of a giant {beta}-barrel whose structure is supported by a glycan scaffold. Our work provides a structural basis for understanding how {beta}-pore forming proteins breach the membrane and reveals a mechanism for how MAC kills pathogens and regulates cell functions.

biophysics↗

bioRxiv2017

PBxplore: A Tool To Analyze Local Protein Structure And Deformability With Protein Blocks

Proteins are highly dynamic macromolecules. A classical way to analyze their inner flexibility is to perform molecular dynamics simulations. In this context, we present the advantage to use small structural prototypes, namely the Protein Blocks (PBs). PBs give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, they allow analyzes of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is a suite of tools to analyze the dynamics and deformability of protein structures using PBs. It is able to process large amount of data such as those produced by molecular dynamics simulations. It produces various outputs with text and graphics, such as frequencies, entropy and information logo. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.

bioinformatics↗