Biology subjects

Tikhonova, A.

Publications and source records attributed to Tikhonova, A..

2 recordsLinked to original sources

bioRxiv2025

Surface proteomics reveals arginine metabolism as a vulnerability in high grade serous ovarian cancer

The significant lethality of high-grade serous ovarian cancer (HGSC) is driven by the lack of long-term efficacy of current treatments, underscoring the need for developing additional therapeutics. Cell surface proteins represent attractive therapeutic targets yet remain underexplored in high-grade serous ovarian cancer. Here, we employed cell surface N-glycoproteomics to elucidate the cell surface proteomes of HGSC cells alongside normal epithelial and cancer-associated stromal cells, uncovering new opportunities for therapeutic intervention. Integration of cell surface N-glycoproteomics and functional screening revealed multiple minimally characterized, HGSC-enriched surface proteins that are critical for HGSC proliferation, most notably, SLC7A1. Multi-omic and functional characterization indicated that SLC7A1 is necessary for HGSC migration, protein synthesis and mitochondrial functions likely linked to its role as an arginine transporter. Finally, we demonstrate that elevated surface expression of SLC7A1 in HGSC reflects dysregulated arginine metabolism, pointing to a putative therapeutic vulnerability. Our work identifies SLC7A1 and arginine metabolism as a previously unrecognized molecular vulnerability in HGSC and provides a framework to guide future therapeutic development.

cancer biology↗

bioRxiv2025

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery, as CYP450-mediated metabolism profoundly influences drug efficacy, safety, and adverse reaction risks. However, experimental determination of CYP450-ligand interactions remains resource- and time-intensive, underscoring the need for robust computational alternatives. While ligand-based methods are commonly employed, they often fail to fully account for structural intricacies governing protein-ligand interactions. To address this gap, we developed a hybrid machine learning framework integrating ligand descriptors, protein descriptors, and protein-ligand interaction descriptors, that include molecular docking-derived parameters, rescoring function components from multiple algorithms and structural interaction fingerprints (SIFt). Evaluated on CYP1A2 and CYP17A1 isoforms, our model demonstrated superior predictive accuracy in cross-validation compared to stand-alone molecular docking and ligand-based approaches. Furthermore, benchmarking against state-of-the-art tools --SwissADME and ADMETlab 3.0 -- revealed enhanced performance in binding prediction. This work establishes a versatile framework for advancing computational tools to prioritize CYP450 binding assessments during drug discovery.

bioinformatics↗