Biology subjects

Azad, S.

Publications and source records attributed to Azad, S..

3 recordsLinked to original sources

bioRxiv2026

Omics-scale capture of electrophilic metabolites

The majority of metabolic pathways rely on the production of activated electrophilic intermediates, e.g., coenzyme-A esters, and their chemical structures and abundances are central to understanding enzyme function and biochemical mechanisms. However, most electrophilic metabolites are lost in traditional metabolomic analysis and thus remain poorly characterized. Here we introduce a biochemical probe, O-(trimethylammoniobutyl)-hydroxylamine (TAMOHA), that enables comprehensive profiling of electrophilic species such as coenzyme-A esters, ketones, and aldehydes. TAMOHA incorporates a highly nucleophilic hydroxyl amine that reacts quickly with electrophilic species upon tissue lysis, trapping them as stable derivatives that feature a tetraalkylammonium moiety whose constitutive charge and characteristic MS2 fragmentation fingerprint enable their highly sensitive detection. Using TAMOHA to survey the electrophilomes of E. coli, C. elegans, and mouse revealed several thousand electrophilic metabolites, most of which have not been characterized. We then demonstrate trapping of electrophilic metabolites with TAMOHA in the context of specific biochemical pathways, confirming previously proposed functions of two fatty acid metabolism enzymes, detecting formaldehyde production in mice, and providing new insights into the biosynthesis of ascaroside pheromones in C. elegans. We anticipate that use of TAMOHA for the profiling of electrophilic species will help clarify enzyme function and uncover previously elusive biochemical mechanisms in a wide range of biological systems. Table of Contents artwork O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/743530v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@79a416org.highwire.dtl.DTLVardef@3b937org.highwire.dtl.DTLVardef@74cae9org.highwire.dtl.DTLVardef@1b5a915_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses

Viral infections pose significant global health challenges, underscoring the urgent need for improved medications. Nevertheless, traditional medicinal approaches depend significantly on labor-intensive laboratory tests, which impede efficient identification and prolong vaccine development, particularly when screening a huge number of samples. To address these obstacles, we present a comprehensive Antiviral Peptide (AVP) Detection Dataset, comprising 14 unique features to improve the characterization of antiviral and non-antiviral peptides. Subsequently, we introduce the Antiviral Peptide detection enhanced by Ensemble Machine Learning (APDeeM) system. This advanced computational framework considerably reduces the time required for AVP detection by utilizing ensemble learning methodologies. The APDeeM system incorporates Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), and AdaBoost algorithms to facilitate the swift selection of AVP candidates without requiring urgent laboratory testing. Our proposed ensemble methodology showed superior performance, with an accuracy of 85.99%, F1 score of 87.60%, recall of 88.91%, and precision of 86.32%, exceeding the efficacy of all tested antiviral peptide prediction models in this research. The APDeeM approach signifies a substantial improvement over conventional detection techniques, expediting the identification of prospective vaccine candidates and facilitating the advancement of more effective antiviral peptides. The most promising AVP candidates may urge laboratory validation, optimize resources, and accelerate vaccine development.

bioinformatics↗

bioRxiv2025

Mathematical Modeling and Simulation of Tumor-Induced Angiogenesis in Retinal Hemangioblastoma

Retinal Hemangioblastoma (RH) is the most frequent manifestation of the von Hippel-Lindau syndrome (VHL), a rare disease associated with the germline mutation of the von Hippel-Lindau protein (pVHL). An emblematic feature of RH is the high vascularity, which is explained by the overexpression of angiogenic factors (AFs) arising from the pVHL impairment. The introduction of Optical Coherence Tomography Angiography (OCTA) allowed observing this feature with exceptional detail. Here, we combine OCTA images and a mechanistic model to investigate tumor growth and vascular development in a patient-specific way. We derived our model from the agreed pathology for RH and focused on the earliest stages of tumor-induced angiogenesis. Our simulations closely resemble the medical images, proving the capability of our model to recapitulate vascular patterning in actual patients. Our results also suggest that angiogenesis in RH occurs upon reaching a critical dimension (around 200 m), followed by the rapid formation of stable vascular networks. These findings open a new perspective on the crucial role of time in antiangiogenic therapy in RH, which has resulted in ineffective control. Indeed, it might be that when RH is diagnosed, angiogenesis is already too advanced to be effectively targeted with any effective means. Moreover, our simulations suggest that vascularization in RH is not a continuous process but an inconstant development with long, stable phases and rapid episodes of vascular sprouting. AUTHOR SUMMARYTumor-induced angiogenesis is a survival strategy commonly exploited by solid tumors to access further nutrients and sustain their growth. The recent introduction of Optical Coherence Tomography Angiography (OCTA) enables scientists and physicians to observe vascular patterning in the retina, non-invasively and with unprecedented detail. Here, we exploit direct observations on a vascular retinal tumor, Retinal Hemangioblastoma (RH), and a mathematical model to investigate the earliest stages of tumor-induced angiogenesis. Our simulations closely match reality and provide critical insights into the role of time in anti-angiogenic therapy for this neoplasm.

bioengineering↗