Biology subjects

Dogan, B.

Publications and source records attributed to Dogan, B..

11 recordsLinked to original sources

bioRxiv2026

Clustering Strategies Improve Structure-Preserving Visualization of Single-Cell RNA-seq Data with CBMAP

Dimensionality reduction for visualization is a fundamental step in single-cell RNA sequencing (scRNA-seq) analysis due to the extremely high dimensionality of gene expression profiles. However, widely used nonlinear embedding techniques such as UMAP and t-SNE can introduce substantial distortions when projecting data into two-dimensional space, potentially altering global organization, local neighborhoods, and distance relationships in ways that may mislead downstream biological interpretation. In this study, we investigate the applicability of Clustering-Based Manifold Approximation and Projection (CBMAP) for the visualization of scRNA-seq data and systematically examine how clustering strategies influence the quality of the resulting embeddings. CBMAP was integrated with several clustering algorithms commonly used in single-cell analysis, including k-means, Leiden, HDBSCAN, Secuer, HGC, and FlowSOM. The resulting embeddings were evaluated using quantitative metrics that measure global, local, and distance-level structure preservation and were compared with widely used dimensionality reduction methods such as UMAP, t-SNE, and PaCMAP across multiple benchmark datasets. Our results demonstrate that the clustering stage plays a critical role in determining the structural fidelity of CBMAP embeddings. Clustering algorithms specifically designed for single-cell transcriptomic data, particularly Secuer, produced more consistent preservation of global relationships between cell populations. Across multiple datasets, CBMAP more faithfully preserved global structural organization and inter-population distance relationships than the compared methods, although local neighborhood preservation was generally weaker than in techniques optimized for local structure. Importantly, CBMAP embeddings retained biologically meaningful relationships in trajectory benchmark datasets. When combined with RNA velocity analysis, CBMAP successfully preserved cyclic progenitor states and branching differentiation trajectories, demonstrating compatibility with trajectory-aware visualization. These findings indicate that CBMAP provides a structure-faithful visualization framework for scRNA-seq data and that clustering selection plays a central role in determining embedding quality.

bioinformatics↗

bioRxiv2025

Targeting Ubiquitin-Specific Protease 7 (USP7): A Pharmacophore-Guided Drug Repurposing and Physics-Based Molecular Simulations Study

Ubiquitin-Specific Protease 7 (USP7) has emerged as a critical therapeutic target in cancer due to its regulatory roles of tumor suppressors, oncoproteins, and epigenetic modifiers. In this study, we present a comprehensive drug repurposing strategy to identify potential USP7 inhibitors from a curated library of 6,654 FDA-approved and investigational small molecules. Using structure-based pharmacophore models derived from USP7-inhibitor crystal structures, we conducted virtual screening to select candidates with favorable pharmacophoric alignment. The top 100 hits were subjected to short 10 ns molecular dynamics (MD) simulations and MM/GBSA binding energy calculations, narrowing down to 36 promising ligands. These were further evaluated through longer (100 ns) MD simulations, binding energy refinement, ligand clustering based on molecular fingerprints, and cancer-specific activity predictions using a binary QSAR model. Twelve compounds demonstrated favorable binding profiles, structural diversity, and high predicted anticancer activity, such as xantifibrate, etofylline clofibrate, and carafiban. This multi-tiered hybrid virtual screening pipeline highlights the potential of drug repurposing for the rapid identification of USP7 inhibitors, offering a cost-effective path toward novel anticancer therapeutics.

bioinformatics↗

bioRxiv2025

Dietary protein source mediates colitis pathogenesis through bacterial modulation of bile acids

Introductory paragraphEvidence-based dietary recommendations for individuals with inflammatory bowel diseases (IBD) are limited. Red meat consumption is associated with increased IBD incidence and relapse in patients, suggesting that switching to a plant-based diet may limit gut inflammation. However, the mechanisms underlying the differential effects of these diets remain poorly understood. Feeding diets containing plant- or animal-derived proteins to murine colitis models revealed that mice given a beef protein (BP) diet exhibited the most severe colitis, while mice fed pea protein (PP) developed mild inflammation. The colitis-promoting effects of BP were microbially-mediated as determined by bacterial elimination or depletion and microbiota transplant studies. In the absence of colitis, BP-feeding reduced abundance of Lactobacillus johnsonii and Turicibacter sanguinis and expanded Akkermansia muciniphila, which localized to the mucus in association with decreased mucus thickness and quality. BP-fed mice had elevated primary and conjugated fecal bile acids (BAs), and taurocholic acid administration to PP-fed mice worsened colitis. Dietary psyllium protected against BP-mediated inflammation, restored BA-modulating commensals and normalized BA ratios. Collectively, these data suggest that the protein component of red meat may be responsible, in part, for the colitis-promoting effects of this food source and provide insight into dietary factors that may influence IBD severity.

microbiology↗

bioRxiv2023

DIFFERENTIAL SCANNING CALORIMETRY: A SUITABLE METHODOLOGY TO PROBLEM THE BINDING PROPERTIES OF BCL-2 INHIBITORS

The Differential Scanning Calorimeter (DSC) technique is commonly used for quantitative analysis of protein stability, and it is one of the methods used to quantify protein-ligand interactions. DSC provides denaturation curves that can be a source of data on binding constants. B-cell leukemia/lymphoma-2 (BCL-2) is an antiapoptotic protein, one of the overexpressed proteins in cancer cells. Here, binding constants for BCL-2 of nine BCL-2 inhibitors were evaluated by utilization of DSC measurements. The binding free energies of suggested inhibitors of BCL-2 were measured by melting temperature, Tm, and heat capacity, Cp. Evaluated BCL-2 binding parameters of candidate molecules agreed with our previously reported biological assays and molecular simulations indicating that the DSC method could be applied for the determination of free energies of enzyme inhibitors with high accuracy. Therefore, our results showed that this fast and relatively cheap technique can be conducted in high throughput drug screening analyses to compare the binding affinity of a set of compounds against a specific target protein. BCL-2 and most of its inhibitors have hydrophobic cavities, and a definite percent of dimethyl sulfoxide (DMSO) was advised to increase the solubility. Our work also suggests that the presence of the recommended high concentration of DMSO as the solvent significantly changes the affinity of the inhibitors to BCL-2, revealing similar DSC curves for all inhibitors.

biophysics↗

bioRxiv2023

Investigating the Effect of GLU283 Protonation State on the Conformational Heterogeneity of CCR5 by Molecular Dynamics Simulations

CCR5 is one of the co-receptors for HIV-1 entry into host cells and is class A GPCR. This receptor has vital roles in the immune system and is involved in the pathogenesis of different diseases. Various studies were conducted to understand its activation mechanism including structural studies in which inactive and active states of the receptor were determined in complex with various binding partners. These determined structures provided opportunities to perform molecular dynamics simulations (MD) and analyze conformational changes observed in protein structures. The atomic level dynamical studies allow us to explore the effects of ionizable residues in the receptor. Here, our aim was to investigate the changes observed in the conformation of CCR5 when it is in complex with inhibitor maraviroc (MRV), an approved anti-HIV drug or HIV-1 envelope protein GP120 in comparison to when the receptor was in apo form. In our simulations, we considered both ionized and protonated states of ionizable binding site residue GLU2837.39 in CCR5 as the protonation state of this residue was considered ambiguously in previous studies. Our simulation results suggested that in fact, the change in the protonation state of GLU2837.39 caused interaction profiles to be different between CCR5 and its binding partners, GP120 or MRV. We observed that when the protonated state of GLU2837.39 was considered in complex with envelope protein GP120, there were substantial structural changes in CCR5 indicating it adopts more of an active-like conformation. On the other hand, CCR5 when it was in complex with MRV always adopted inactive conformation regardless of the protonation state. Hence, CCR5 coreceptor displays conformational heterogeneity not only based on its binding partner but also on the state of the protonation state of a binding site residue GLU2837.39. This outcome is also in accordance with some studies showing that GP120 binding could activate signaling pathways. Additionally, this outcome could also have critical implications for the discovery of novel CCR5 inhibitors to be used as anti-HIV drugs by in silico methods such as molecular docking since consideration of the protonated state of GLU2837.39 could be required.

biophysics↗

bioRxiv2022

Machine learning approaches to quantitatively predict selectivity of compounds against hDAC1 and hDAC6 isoforms

The design of compounds selectively binding to specific isoforms of histone deacetylases (hDAC) is an ongoing research to prevent adverse side effects. Two of the most studied isoforms are hDAC1 and hDAC6 that are important targets to inhibit in various disease conditions. Here, various machine learning approaches were tested with the aim of developing models to predict the bioactivity and selectivity towards specific isoforms. Selectivity models were developed by directly training on the bioactivity differences of tested compounds against hDAC1 and hDAC6. Both classification and regression models were developed and compared to each other by using traditional evaluation metrics.

bioinformatics↗

bioRxiv2022

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single cell level. However, scRNA-seq relies on isolating cells from tissues. Thus, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows to measure gene expression while preserving spatial information. A first step in the spatial transcriptomic analysis is to identify the cell type which requires a careful selection of cell-specific marker genes. For this purpose, currently scRNA-seq data is used to select limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a new approach for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are extracted prior to the marker gene selection step. For the selection of marker genes, cluster validity indices, Silhouette index or Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast and accurate. Even for the large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with less memory requirements.

bioinformatics↗

bioRxiv2020

Near-Physiological-Temperature Serial Femtosecond X-ray Crystallography Reveals Novel Conformations of SARS-CoV-2 Main Protease Active Site for Improved Drug Repurposing

The COVID19 pandemic has resulted in 25+ million reported infections and nearly 850.000 deaths. Research to identify effective therapies for COVID19 includes: i) designing a vaccine as future protection; ii) structure-based drug design; and iii) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determined two apo structures of Severe Acute Respiratory Syndrome CoronaVirus-2 main protease at ambienttemperature by Serial Femtosecond X-ray crystallography. We employed detailed molecular simulations of selected known main protease inhibitors with the structures and compared binding modes and energies. The combined structural biology and molecular modeling studies not only reveal the dynamics of small molecules targeting main protease but will also provide invaluable opportunities for drug repurposing and structure-based drug design studies against SARS-CoV-2. One Sentence SummaryRadiation-damage-free high-resolution SARS-CoV-2 main protease SFX structures obtained at near-physiological-temperature offer invaluable information for immediate drug-repurposing studies for the treatment of COVID19.

biophysics↗

bioRxiv2020

Structural and functional characterization of allatostatin receptor type-C of Thaumetopoea pityocampa revealed the importance of Q2716.55 residue in G protein-dependent activation pathway

Insect neuropeptide receptors are among the potential targets for designing next-generation pesticides. Activation of allatostatin receptor type C (AstR-C), a G Protein-coupled receptor (GPCR), upon stimulation with its endogenous ligand, allatostatin C (AST-C), leads to the inhibition of juvenile hormone (JH) secretion that consequently regulates physiology of insects. Here we conducted in silico and in vitro approaches to characterize the structure and function of AstR-C of Thaumetopoea pityocampa (T.pit), a well-known pest in Mediterranean countries. The sequence of AstR-C and AST-C were derived from whole genome sequencing (WGS) data. Resonance energy transfer (RET) methods were used to investigate the downstream effectors of the receptor and the temporal kinetics of G protein activation. Three-dimensional (3D) structure of AstR-C constructed via homology modeling methods was subjected to molecular dynamics (MD) simulations and docking studies to identify the orthosteric pocket. Our results showed that T.pit AstR-C couples to Gi/o subtype of G proteins at sub-nanomolar ranges of the the ligand with the G protein recruitment and activation kinetics of [~]4 and 6 seconds, respectively, when 1 nM AST-C is administered. At the increasing concentration of native ligand, {beta}arrestin was shown to be recruited at nanomolar ranges the ligand. Docking and MD simulation studies revealed the importance of extracellular loop 2 (ECL2) in T.pit AstRC/AST-C interaction, and combination of in silico and in vitro methods supported the accuracy of the built model and the predicted orthosteric pocket. Q2716.55 (Ballesteros-Weinstein generic numbering) was found to have a substantial role in G protein dependent activation of AstR-C possibly via contributing to the flexibility of the structure.

bioinformatics↗

bioRxiv2019

Yersiniabactin producing AIEC promote inflammation-associated fibrosis in gnotobiotic Il10-/- mice

Fibrosis is a significant complication of intestinal disorders associated with microbial dysbiosis and pathobiont expansion, notably Crohns disease (CD). Mechanisms that favor fibrosis are not well understood and therapeutic strategies are limited. Here we demonstrate that colitis susceptible Il10-deficient mice develop inflammation-associated fibrosis when mono-associated with adherent/invasive Escherichia coli (AIEC) that harbor the yersiniabactin (Ybt) pathogenicity island. Inactivation of Ybt siderophore production in AIEC nearly abrogated fibrosis development in inflamed mice. In contrast, inactivation of Ybt import through its cognate receptor FyuA enhanced fibrosis severity. This corresponded with increased colonic expression of profibrogenic genes prior to the development of histological disease, therefore suggesting causality. FyuA-deficient AIEC also exhibited greater localization within sub-epithelial tissues and fibrotic lesions that was dependent on Ybt biosynthesis and corresponded with increased fibroblast activation in vitro. Together, these findings suggest that Ybt establishes a pro-fibrotic environment in the host in the absence of binding to its cognate receptor and indicates a direct link between intestinal AIEC and the induction of inflammation-associated fibrosis.

microbiology↗

bioRxiv2019

A DNA recognition code for probing the in vivo functions of zinc finger transcription factors at domain resolution

Multi-zinc finger proteins constitute the largest class of human transcription factors. Their DNA-binding specificity is usually encoded by a subset of their tandem Cys2His2 zinc finger (ZF) domains - the subset that binds to DNA, however, is often unknown. Here, by combining a context-aware machine-learning-based model of DNA recognition with in vivo binding data, we characterize the sequence preferences and the ZF subset that is responsible for DNA binding in 209 human multi-ZF proteins. We show that in vivo DNA binding is primarily driven by [~]50% of the ZFs - these DNA-binding ZFs are under strong selective pressure within and across species, and their mutations affect the expression of hundreds of genes as revealed by pan-cancer trans-eQTL analysis across 18 tissues. Among the genes affected by mutations in multi-ZF proteins, we identify several oncogenic factors regulated by SP1, and show that SP1 up-regulation in cancer promotes the expression of these genes while mutations in SP1 ZFs lead to their repression. Together, these analyses suggest that mutations in DNA-binding ZFs have distinct and widespread regulatory consequences that contribute to transcriptome remodelling in cancer.

bioinformatics↗