Biology subjects

Capponi, S.

Publications and source records attributed to Capponi, S..

11 recordsLinked to original sources

bioRxiv2025

Genome writing to dissect consequences of SVA retrotransposon disease X-Linked Dystonia Parkinsonism

Human retrotransposon insertions are often associated with diseases. In the case of the neurodegenerative X-Linked Dystonia-Parkinsonism disease, a human-specific SINE-VNTR-Alu subfamily F retrotransposon was inserted in intron 32 of the TAF1 gene. Here, we genomically rewrote a portion of the mouse Taf1 allele with the corresponding 78-kb XDP patient derived TAF1 allele. In mESCs, the presence of the intronic SVAs--rather than the hybrid gene structure--reduces hyTAF1 levels. This leads to transcriptional downregulation of genes with TATA box enriched in their promoters and triggering apoptosis. Chromatin and transcriptome profiling revealed that intronic SVAs are actively transcribed, forming barriers that likely impede transcription elongation. In mice, neuronal lineage TAF1 humanization resulted lethality of male progeny within two months. XDP male mice had severe atrophy centered on the striatum--the same affected brain region in XDP patients. Lastly, CRISPRa-mediated activation of hyTAF1 restored mESC viability, suggesting boosting TAF1 transcription as a therapeutic approach.

genetics↗

bioRxiv2025

Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations

The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wls membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt-Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine extended atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein- protein interactions and guiding experimental validation or therapeutic targeting.

biochemistry↗

bioRxiv2025

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal cells such as cancer. A key player in the immunologic recognition of non-self is the immune synapse, where T cell receptors (TCRs) scan peptides presented on major histocompatibility complex (MHC) molecules to detect and eliminate cells displaying non-self antigens. While this interaction is vital for vaccine and immunotherapy success, the underlying rules of TCR recognition remain poorly understood. This is only further challenged as the application of predictive models is very limited due to small training datasets. While traditional machine learning models excel at predicting neoantigen binding to MHC, they often struggle to accurately predict immunogenicity. To address these challenges, we developed a quantum computing approach using Quantum Convolutional Neural Networks (QCNNs). Here we present Quantum Convolutional Human Leukocyte Antigen (HLA) Immunogenic Peptide Prediction (Q-CHIPP), the first application of quantum hardware based on training/predicting both MHC binding and immunogenicity in a combinatorial approach. Additionally, we present a large scale use of quantum hardware at scale with 46 qubits. This study underscores how quantum technology can be used for biological modeling and presents a scalable QCNN design with the potential to overcome current computational bottlenecks as quantum hardware advances.

immunology↗

bioRxiv2024

A high-throughput method for quantifying Drosophila fecundity

Measurements of Drosophila fecundity are used in a wide variety of studies, such as investigations of stem cell biology, nutrition, behavior, and toxicology. In addition, because fecundity assays are performed on live flies, they are suitable for longitudinal studies such as investigations of aging or prolonged chemical exposure. However, standard Drosophila fecundity assays have been difficult to perform in a high-throughput manner because experimental factors such as the physiological state of the flies and environmental cues must be carefully controlled to achieve consistent results. In addition, exposing flies to a large number of different experimental conditions (such as chemical additives in the diet) and manually counting the number of eggs laid to determine the impact on fecundity is time-consuming. We have overcome these challenges by combining a new multiwell fly culture strategy with a novel 3D-printed fly transfer device to rapidly and accurately transfer flies from one plate to another; the RoboCam, a low-cost, custom built robotic camera to capture images of the wells automatically; and an image segmentation pipeline to automatically identify and quantify eggs. We show that this method is compatible with robust and consistent egg laying throughout the assay period; and demonstrate that the automated pipeline for quantifying fecundity is very accurate (r2 = 0.98 for the correlation between the automated egg counts and the ground truth) In addition, we show that this method can be used to efficiently detect the effects on fecundity induced by dietary exposure to chemicals. Taken together, this strategy substantially increases the efficiency and reproducibility of high throughput egg laying assays that require exposing flies to multiple different media conditions.

genetics↗

bioRxiv2022

A tradeoff between enterovirus A71 particle stability and cell entry

A central role of viral capsids is to protect the viral genome from the harsh extracellular environment while facilitating initiation of infection when the virus encounters a target cell. Viruses are thought to have evolved an optimal equilibrium between particle stability and efficiency of cell entry. In this study, we genetically perturbed this equilibrium in a non-enveloped virus, enterovirus A71 to determine its structural basis. We isolated a single-point mutation variant with increased particle thermotolerance and decreased efficiency of cell entry. Using cryo-electron microscopy and molecular dynamics simulations, we determined that the thermostable native particles have acquired an expanded conformation that results in a significant increase in protein dynamics. Examining the uncoating intermediate states of the thermostable variant suggests a pathway, where the lipid pocket factor is released first, followed by internal VP4 and finally the viral RNA.

biophysics↗

bioRxiv2022

Phenotype to genotype mapping using supervised and unsupervised learning

The relationship between the genotype, the genetic instructions encoded into a genome, and phenotype, the macroscopic realization of such instructions, remains mostly uncharted. In addition, tools able to uncover the connection between the phenotype with a specific set of responsible genes are still under definition. In this work, we focus on yeast organelles called vacuoles, which are cell membrane compartments that vary size and shape in response to various stimuli, and we develop a framework relating changes of cellular morphology to genetic modification. The method is a combination of convolutional neural network (CNN) and an unsupervised learning pipeline, which employs a deep-learning based segmentation, classification, and anomaly detection algorithm. From the live 3D fluorescence vacuole images, we observe that different genetic mutations generate distinct vacuole phenotypes and that the same mutation might correspond to more than one vacuole morphology. We trained a Unet architecture to segment our cellular images and obtain precise, quantitative information in 2D depth-encoded images. We then used an unsupervised learning approach to cluster the vacuole types and to establish a correlation between genotype and vacuole morphology. Using this procedure, we obtained 4 phenotypic groups. We extracted a set of 131 morphological features from the segmented vacuoles images, reduced to 50 after a tree-based feature selection. We obtained a purity of 85% adopting a Fuzzy K-Means based algorithm on a random subset of 880 images, containing all the detected phenotypic groups. Finally, we trained a CNN on the labels assigned during clustering. The CNN has been used for prediction of a large dataset (6942 images) with high accuracy (80%). Our approach can be applied extensively for live fluorescence image analysis and most importantly can unveil the basic principles relating genotype to vacuole phenotype in yeast cell, which can be thought as a first step for inferring cell designing principles to generate organelles with a specific, desired morphology.

bioinformatics↗

bioRxiv2022

Predicting Epitope Candidates for SARS-CoV-2

Epitopes are short amino acid sequences that define the antigen signature to which an antibody binds. In light of the current pandemic, epitope analysis and prediction is paramount to improving serological testing and developing vaccines. In this paper, we leverage known epitope sequences from SARS-CoV, SARS-CoV-2 and other Coronaviridae and use those known epitopes to identify additional antigen regions in 62k SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, discuss where epitopes are located on proteins, and how epitopes can be grouped into classes. We also discuss the mutation density of different regions on proteins using a big data approach. We find that there are many conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike Glycoprotein.

bioinformatics↗

bioRxiv2022

Exploring the rules of chimeric antigen receptor phenotypic output using combinatorial signaling motif libraries and machine learning

Chimeric antigen receptor (CAR) costimulatory domains steer the phenotypic output of therapeutic T cells. In most cases these domains are derived from native immune receptors, composed of signaling motif combinations selected by evolution. To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing [~]2,300 synthetic costimulatory domains, built from combinations of 13 peptide signaling motifs. The library produced CARs driving diverse fate outputs, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, the non-native combination of TRAF- and PLC{gamma}1-binding motifs was found to simultaneously enhance cytotoxicity and stemness, a clinically desirable phenotype associated with effective and durable tumor killing. The neural network accurately predicts that addition of PLC{gamma}1-binding motifs improves this phenotype when combined with TRAF-binding motifs, but not when combined with other immune signaling motifs (e.g. PI3K-or Grb2-binding motifs). This work shows how libraries built from the minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/474985v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@8dbf97org.highwire.dtl.DTLVardef@1d8300org.highwire.dtl.DTLVardef@1838ba0org.highwire.dtl.DTLVardef@143d907_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.

biophysics↗

bioRxiv2020

K2P channel C-type gating involves asymmetric selectivity filter order-disorder transitions

K2P channels regulate nervous, cardiovascular, and immune system functions1,2 through the action of their selectivity filter (C-type) gate3-6. Although structural studies show K2P conformations that impact activity7-13, no selectivity filter conformational changes have been observed. Here, combining K2P2.1 (TREK-1) X-ray crystallography in different potassium concentrations, potassium anomalous scattering, molecular dynamics, and functional studies, we uncover the unprecedented, asymmetric, potassium-dependent conformational changes underlying K2P C-type gating. Low potassium concentrations evoke conformational changes in selectivity filter strand 1 (SF1), selectivity filter strand 2 (SF2), and the SF2-transmembrane helix 4 loop (SF2-M4 loop) that destroy the S1 and S2 ion binding sites and are suppressed by C-type gate activator ML335. Shortening the uniquely long SF2-M4 loop to match the canonical length found in other potassium channels or disrupting the conserved Glu234 hydrogen bond network supporting this loop blunts C-type gate response to various physical and chemical stimuli. Glu234 network destabilization also compromises ion selectivity, but can be reversed by channel activation, indicating that the ion binding site loss reduces selectivity similar to other channels14. Together, our data establish that C-type gating occurs through potassium-dependent order-disorder transitions in the selectivity filter and adjacent loops that respond to gating cues relayed through the SF2-M4 loop. These findings underscore the potential for targeting the SF2-M4 loop for the development of new, selective K2P channel modulators.

biophysics↗

bioRxiv2018

Co-translation drives the assembly of mammalian nuclear multisubunit complexes

AO_SCPLOWBSTRACTC_SCPLOWCells dedicate significant energy to build proteins often organized in multiprotein assemblies with tightly regulated stoichiometries. As genes encoding proteins assembling in the same multisubunit complexes are dispersed in the genome of eukaryotes, it is unclear how multisubunit complexes assemble. We show that mammalian nuclear transcription complexes (TFIID, TREX-2 and SAGA) composed of a large number of subunits but lacking precise architectural details are built co-translationally. We demonstrate that the dimerization domains and their positions in the interacting subunits determine the co-translational assembly pathway (simultaneous or sequential). Our results indicate that protein translation and complex assembly are linked in building mammalian multisubunit complexes and suggest that co-translational assembly is a general principle in mammalian cells to avoid non-specific interactions and protein aggregation. These findings will significantly advance structural biology by defining endogenous co-translational building blocks in the architecture of multisubunit complexes.

molecular biology↗