Biology subjects

Sayin, A. Z.

Publications and source records attributed to Sayin, A. Z..

3 recordsLinked to original sources

bioRxiv2025

Integration of In Vitro and In Silico Approaches Enables Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug attrition and poses a significant threat to patient safety. However, current preclinical prediction methods, including heuristic screening rules, in vitro assays, machine learning models and animal testing, have serious limitations. Here, we demonstrate that combining in vitro toxicity data (cytotoxicity, mitochondrial toxicity, bile salt export pump (BSEP) inhibition) with pharmacokinetic information enables high DILI predictivity. In a retrospective analysis of 241 drugs, we show that the ratio of their in vivo maximum plasma concentration (Cmax) to their lowest in vitro toxicity strongly correlates with clinical DILI risks, with ROC AUC up to 96%. Then, we show that comparable predictivity (ROC AUC up to 91%) is achievable prospectively when Cmax values are predicted in silico by high-throughput physiologically based kinetic modelling. Dynamic simulations of bile acid perturbations further identify drugs potentially causing DILI specifically through BSEP inhibition, providing additional mechanistic insights. This integrative, mechanistic approach shows enhanced DILI predictivity and interpretability, offering an animal-free alternative for early drug development.

pharmacology and toxicology↗

bioRxiv2023

DiPPI: A curated dataset for drug-like molecules in protein-protein interfaces

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large dataset for drug-like molecules in protein interfaces. We further present DiPPI (Drugs in Protein-Protein Interfaces), a two-module website to facilitate the search for such molecules and their properties by exploiting our dataset in drug repurposing studies. In the interface module of the website, we extracted several properties of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we curated a list of drug-like small molecules and FDA-approved drugs from various databases and extracted those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinskis rules and various molecular descriptors, are also calculated and made available on the website to guide the selection of drug molecules. Our dataset contains 534,203 interfaces for 98,632 proteins, of which 55,135 are detected to bind to a drug-like molecule. 2,214 drug-like molecules are deposited on our website, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data; and is freely available at http://interactome.ku.edu.tr:8501.

bioinformatics↗

bioRxiv2023

Conformational diversity and protein-protein interfaces in drug repurposing in Ras signaling pathway

Ras/Raf/MEK/ERK signaling pathway regulates cell growth, division, and differentiation. In this work, we focus on drug repurposing in the Ras/Raf/MEK/ERK signaling pathway, considering structural similarities of protein-protein interfaces. The complexes in this pathway are extracted from literature and interfaces formed by physically interacting proteins are found via PRISM (PRotein Interaction by Structural Matching) if not available in Protein Data Bank. As a result, the structural coverage of these interactions has been increased from 21% to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to additional structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir and saquinavir may bind to Epidermal Growth Factor Receptor (EGFR) and Receptor Tyrosine-Protein Kinase ErbB-3 (ERBB3/HER3) interface. Tipranavir and indinavir may also bind to EGFR and Receptor Tyrosine-Protein Kinase ErbB-2 (ERBB2/HER2) interface. Additionally, a drug used in Alzheimers disease (galantamine) and an antinauseant for cancer chemotherapy patients (granisetron) can bind to RAF proto-oncogene serine/threonine-protein kinase (RAF1) and Serine/threonine-protein kinase B-Raf (BRAF) interface. Hence, we propose an algorithm to find drugs to be potentially used for cancer. As a summary, we propose a new strategy of using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way. Significance statementThis work focuses on drug repurposing in the Ras/Raf/MEK/ERK signaling pathway based on structural similarities of protein-protein interfaces. The Food and Drug Administration approved drugs bound to the protein-protein interfaces are proposed for the other interfaces using protein-protein interface clusters based on structural similarities. Moreover, the structural coverage of protein complexes of physical interactions in the pathway has been increased from 21% to 92% using multiple conformations of each protein to include protein dynamics.

bioinformatics↗