Biology subjects

Shevchuk, N.

Publications and source records attributed to Shevchuk, N..

4 recordsLinked to original sources

bioRxiv2026

Differential Expression of TKS4 Isoforms and Their Role in Cellular Processes in Breast Cancer

The scaffold protein TKS4 plays a role in the development of several cancers. Alternative splicing of the TKS4 gene generates two isoforms, TKS4L and TKS4b; however, their distinct expression patterns and functional roles have not yet been characterized. We have shown that TKS4 isoforms were differentially expressed across human cell lines and breast cancer (BC) tumor samples. Both TKS4L and TKS4L/TKS4b mRNA ratios were significantly altered in tumors compared with adjacent tissues. We identified six novel binding SH3-domain-containing partners for TKS4L, none of which interact with TKS4b, suggesting their functional differences. Tyrosine phosphorylation of both isoforms was induced by Src(Y527F) kinase overexpression, enabling binding to the SH2 domains of signaling proteins. Interestingly, TKS4b significantly accumulated in the nucleus, while TKS4L was primarily present in the cytosol in MCF-7 cells. TKS4b overexpression enhanced MCF-7 cell migration. Both TKS4 isoforms exhibit oncogenic properties by promoting epithelial-mesenchymal transition in BC cells, highlighting their potential as targets for therapeutic intervention.

molecular biology↗

bioRxiv2026

Critical Assessment of ML models for ADMET Prediction in TDC leaderboards

In this work we performed a critical assessment of the benchmarking procedures used in Therapeutics Data Commons (TDC) ADMET leaderboards, focusing on reproducibility, robustness against data leakage, and signs of test-set overfitting across all 22 TDC ADMET endpoints. For each endpoint, the top 3 leaderboard models were screened with a unified protocol: execution environment reproducibility check, data leakage assessment, verification of hyperparameter optimisation practices, and final re-evaluation of results and TDC ranking. Only 3 methods (CaliciBoost, MapLight, MapLight+GNN) passed all checks and showed overall reproducible performance, whereas most of top-ranked models exhibited unavailable code, non-reproducible execution environments, runtime incompatibilities, or various methodological flaws. In particular, we identified direct or indirect data leakages in MiniMol, GradientBoost and XGBoost models. We also used our in-house models based on the Mol2Vec architecture to investigate the consequences of deliberately overfitting on the TDC test set. It is shown that deliberate or accidental tuning on the public test set may lead to significant inflation of the model metrics and leaderboard position. Our results emphasize the urgent need for better public ADMET benchmarks with the hidden test sets, strict dataset versioning and model submission with standardized inference environments.

biophysics↗

bioRxiv2025

Improving ADMET prediction with descriptor augmentation of Mol2Vec embeddings

The accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties is crucial for early-stage drug development, enabling the reduction of late-stage attrition and guiding compound prioritization. In recent years, machine learning models have emerged as powerful tools for ADMET prediction, leveraging diverse molecular representations ranging from handcrafted descriptors to graph neural networks and language model embeddings. Despite these advances, balancing predictive performance with computational efficiency remains a key challenge, particularly for high-throughput screening scenarios. Among unsupervised embedding methods, Mol2Vec has shown promise by capturing chemical substructure context analogously to word embeddings in natural language processing. However, its performance on comprehensive ADMET benchmarks has not been systematically assessed. In this work, we reimplement Mol2Vec with an expanded training corpus and higher embedding dimensionality, and evaluate its utility across 16 ADMET prediction tasks from the Therapeutics Data Commons (TDC). We show that while Mol2Vec embeddings alone are competitive, combining them with classical molecular descriptors and applying feature selection significantly improves performance. Our final MLP models with enhanced Mol2Vec embeddings achieved top-1 results in 10 of 16 benchmarks, outperforming all previously reported models on the TDC leaderboard in this regard, demonstrating that descriptor-enriched representations, paired even with relatively simple MLPs, can rival or exceed the performance of more complex models.

bioinformatics↗

bioRxiv2025

Leveraging Large Language Models for Literature-Driven Prioritization of Protein Binding Pockets

We present a novel approach for the identification and prioritization of protein binding pockets for small molecules by combining geometric pocket detection with Large Language Models (LLMs). Our method leverages Fpocket to generate candidate pockets, which are then validated against published experimental data extracted from research articles using LLM with a series of prompts fine-tuned to identify and extract residue-level information associated with experimentally confirmed binding sites. We developed a curated benchmark dataset of diverse proteins and associated literature to train and evaluate the LLMs performance in paper relevance assessment and pocket extraction. The extracted information is then mapped onto protein structures and used to filter and merge the geometry-based predictions, generating a refined volumetric representation of biologically relevant pockets. This hybrid pipeline offers an efficient, accurate and automated method for identifying functional binding pockets, addressing a significant bottleneck in the high-throughput drug discovery workflows. The developed benchmark dataset and methodology are freely available at https://github.com/MelnychenkoM/LLM-benchmark-dataset.

biophysics↗