Biology subjects

Mahdizadeh, S. J.

Publications and source records attributed to Mahdizadeh, S. J..

6 recordsLinked to original sources

bioRxiv2025

FMO4 drives lung adenocarcinoma by stabilizing the MAT2A/MAT2B complex and hindering ferroptosis

Lung cancer is the leading cause of death by cancer in the world and finding new targets is a major medical need to tackle this disease. Here, upon proteomic analysis to identify common players in oncogenic EGFR- and KRAS-driven lung adenocarcinoma mouse models, we uncovered a largely unknown protein in cancer, flavin-containing monooxygenase 4 (FMO4), whose expression was increased in lung tumors compared with adjacent lung tissue. FMO4 expression was strongly increased also in lung cancer samples from patients compared with healthy lung, and its expression level was inversely correlated with overall survival. Remarkably, in vivo deletion of FMO4 greatly decreased tumor burden and increased survival in oncogenic KRAS-driven lung adenocarcinoma mice unveiling its crucial role in tumor biology. Mechanistically, we found that FMO4 loss of function promotes ferroptosis and cooperates with ferroptosis inducers in vitro and in vivo. Moreover, FMO4 facilitates the interaction between MAT2A and MAT2B, promoting the generation of cysteine from methionine, which in turn boosts the generation of glutathione, thus protecting lung adenocarcinoma against ferroptosis. In summary we identified a new target in lung adenocarcinoma with important implications in cancer biology.

cancer biology↗

bioRxiv2025

MolAI: A Deep Learning Framework for Data-driven Molecular Descriptor Generation and Advanced Drug Discovery Applications

This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training dataset of 221 million unique compounds, MolAI employs an autoencoder neural machine translation (NMT) model to generate latent space representations of molecules. The model demonstrated exceptional performance through extensive validation, achieving a 99.99% accuracy in regenerating input molecules from their corresponding latent space. This study showcases the effectiveness of MolAI-driven molecular descriptors by developing an ML-based model (iLP) that accurately predicts the predominant protonation state of molecules at neutral pH. These descriptors also significantly enhance ligand-based virtual screening and are successfully applied in a framework (iADMET) for predicting ADMET features with high accuracy. This capability of encoding and decoding molecules to and from latent space opens unique opportunities in drug discovery, structure-activity relationship analysis, hit optimization, de novo molecular generation, and the training infinite machine learning models. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/644888v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@aa4cdforg.highwire.dtl.DTLVardef@9cedd8org.highwire.dtl.DTLVardef@c32f68org.highwire.dtl.DTLVardef@5d7716_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Proximity Interactome analyses unveil novel regulators of IRE1a canonical signaling

The unfolded protein response (UPR) is a key adaptive pathway that controls endoplasmic reticulum (ER) homeostasis. The UPR is transduced by three ER-resident sensors of ER homeostasis disruption in the lumen of this compartment. They trigger select downstream signaling pathways in the cytosol and nucleus. Among them, IRE1 (referred to as IRE1 hereafter), a type I transmembrane protein, senses accumulation of improperly folded proteins in the ER lumen and transduces signals through both kinase and endoribonuclease (RNase) activities in the cytosol. IRE1 catalyzes XBP1 mRNA unconventional splicing and RNA degradation (Regulated IRE1 Dependent Decay, termed RIDD). Recent studies have reported that IRE1-dependent protein-protein interactions (PPi) drive additional non-canonical IRE1 functions. Herein, we define the IRE1 signalosome as a list of IRE1 binding partners (direct or not) which alter IRE1 signaling towards XBP1 mRNA splicing and RIDD. Here we determined the IRE1 in situ interactome using BioID, putatively connecting IRE1 to previously unrecognized cellular functions. In addition, we link the binding of several IRE1 partners to the regulation of its RNase. Furthermore, we identify HNRNPL as an IRE1-interacting partner, previously unrecognized, which stabilizes IRE1 under basal conditions by counteracting ERAD-mediated degradation. Overall, the characterization of the IRE1 signalosome not only reveals the multi-faceted control of IRE1 RNase activity and stability by its interacting partners and allow us to discuss putative additional IRE1 regulators and cellular functions based on the nature of its interactome and its localization.

biochemistry↗

bioRxiv2024

iScore: A ML-Based Scoring Function for de novo Drug Discovery

In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable speed and precision. Uniquely, iScore circumvents the conventional reliance on explicit knowledge of protein-ligand interactions and full picture of atomic contacts, instead leveraging a set of ligand and binding pocket descriptors to evaluate binding affinity. This approach avoids the inefficient and slow conformational sampling stage, thereby enabling the rapid screening of ultra-huge molecular libraries, a crucial advancement given the practically infinite dimensions of chemical space. iScore was rigorously trained and validated using the PDBbind 2020 refined set, CASF 2016, and CSAR NRC-HiQ Set1/2, employing three distinct ML methodologies: Deep Neural Network (iScore-DNN), Random Forest (iScore-RF), and eXtreme Gradient Boosting (iScore-XGB). A hybrid model, iScore-Hybrid, was subsequently developed to incorporate the strengths of these individual base learners. The hybrid model demonstrated a Pearson correlation coefficient (R) of 0.78 and a root mean square error (RMSE) of 1.23 in cross-validation, outperforming the individual base learners and establishing new benchmarks for scoring power (R = 0.814, RMSE=1.34), ranking power ({rho} = 0.705), and screening power (success rate at top 10% = 73.7%).

bioinformatics↗

bioRxiv2023

An experimental target-based platform in yeast for screening Plasmodium vivax deoxyhypusine synthase inhibitors

The enzyme deoxyhypusine synthase (DHS) catalyzes the first step in the post-translational modification of the eukaryotic translation factor 5A (eIF5A). This is the only protein known to contain the amino acid hypusine, which results from this modification. Both eIF5A and DHS are essential for cell viability in eukaryotes, and inhibiting DHS can be a promising strategy for the development of new therapeutic alternatives. The human and parasitic orthologous proteins are different enough to render selective targeting against infectious diseases; however, no DHS inhibitor selective for the parasite ortholog has previously been reported. Here, we established a yeast surrogate genetics platform to identify inhibitors of DHS from Plasmodium vivax, one of the major causative agents of malaria. We constructed genetically modified Saccharomyces cerevisiae strains expressing DHS genes from Homo sapiens (HsDHS) or P. vivax (PvDHS) in place of the endogenous DHS gene from S. cerevisiae. This new strain background was [~]60-fold more sensitive to an inhibitor of human DHS than the one previously used. Initially, a virtual screen using datasets from the ChEMBL-NTD database was performed. Candidate ligands were tested in growth assays using the newly generated yeast strains expressing heterologous DHS genes. Among these, two showed promise by preferentially reducing the growth of the PvDHS-expressing strain. Further, in a robotized assay, we screened 400 compounds from the Pathogen Box library using the same S. cerevisiae strains, and one compound preferentially reduced the growth of the PvDHS-expressing yeast strain. Western blot revealed that these compounds significantly reduced eIF5A hypusination in yeast. Our study demonstrates that this yeast-based platform is suitable for identifying and verifying candidate small molecule DHS inhibitors, selective for the parasite over the human ortholog.

synthetic biology↗

bioRxiv2022

Mechanical strain stimulates COPII-dependent trafficking via Rac1

Secretory trafficking from the endoplasmic reticulum (ER) is subject to regulation by extrinsic and intrinsic factors. While much of the focus has been on biochemical triggers, little is known whether and how the ER is subject to regulation by mechanical signals. Here, we show that COPII-dependent ER-export is regulated by mechanical strain. Mechanotransduction to the ER was mediated via a previously unappreciated ER-localized pool of the small GTPase Rac1. Mechanistically, we show that Rac1 interacts with the small GTPase Sar1 to drive budding of COPII carriers and stimulate ER-to-Golgi transport. Altogether, we establish an unprecedented link between mechanical strain and export from the ER.

cell biology↗