Biology subjects

Koyuturk, M.

Publications and source records attributed to Koyuturk, M..

13 recordsLinked to original sources

bioRxiv2026

scProfiterole: Clustering of Single-Cell Proteomic DataUsing Graph Contrastive Learning via Spectral Filters

Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type identification for single cell proteomic (scProteomics) data are relatively scarce. Adoption of algorithms that have been developed for scRNAseq into scProteomics is challenged by the larger number of drop-outs, missing data, and noise in single cell proteomic data. Graph contrastive learning (GCL) on cell-to-cell similarity graphs derived from single cell protein expression profiles show promise in cell type identification. However, missing edges and noise in the cell-to-cell similarity graph requires careful design of convolution matrices to overcome the imperfections in these graphs. Here, we introduce scPO_SCPLOWROFITEROLEC_SCPLOW (Single Cell Proteomics Clustering via Spectral Filters), a computational framework to facilitate effective use of spectral graph filters in GCL-based clustering of single cell proteomic data. Since clustering assumes a homophilic network topology, we consider three types of homophilic filters: (i) random walks, (ii) heat kernels, (iii) beta kernels. Direct implementation of these filters is computationally prohibitive, thus the filters are either truncated or approximated in practice. To overcome this limitation, scPO_SCPLOWROFITEROLEC_SCPLOW uses Arnoldi orthonormalization to implement polynomial interpolations of any given spectral graph filter. Our results on comprehensive single cell proteomic data show that (i) graph contrastive learning with learnable polynomial coefficients that are carefully initialized improves the effectiveness and robustness of cell type identification, (ii) heat kernels and beta kernels improve clustering performance over adjacency matrices or random walks, and (iii) polynomial interpolation of spectral filters outperforms approximation or truncation. The source code for scPO_SCPLOWROFITEROLEC_SCPLOW and Supplementary Materials are available at https://github.com/mustafaCoskunAgu/scProfiterole.

bioinformatics↗

bioRxiv2025

Rethinking the Estrogen Receptor Beta Dominance Hypothesis in Endometriosis: Insights from Single Cell RNA Sequencing Meta-analysis

Structured AbstractO_ST_ABSBackgroundC_ST_ABSO_LIEndometriosis is a chronic, estrogen-dependent disease characterized by the presence of endometrial-like tissue growing outside the uterus. The molecular and clinical heterogeneity of endometriosis complicate diagnostic and treatment options -- diagnostic delays of seven to ten years are common and therapies often lack long-term efficacy. Estrogen signaling and estrogen receptor beta (ER{beta}) expression is thought to be increased in endometriosis, contributing to increased cell proliferation in lesions. The "ER{beta} dominance hypothesis" is a prevailing hypothesis in the field, setting ER{beta} as a high-priority therapeutic target. If effectively modulated, ER{beta} could be the first therapy to directly target lesion biology, rather than only managing symptoms. C_LI Objective(s)O_LIWe aimed to characterize ER{beta}s expression in endometriosis by cell type and evaluate its therapeutic relevance, primarily assessing the validity of the ER{beta} dominance hypothesis. C_LI Study DesignO_LIWe reanalyzed scRNAseq data from eight previously published studies. Our final filtered dataset included 557,061 cells, the largest endometriosis single cell atlas ever constructed. We quantified gene expression levels of ESR1 and ESR2, which encode ER[a] and ER{beta} respectively, across each tissue and cell type, to identify cell-type specific drivers of ESR2/ER{beta} expression across diseased and healthy tissues. To characterize the differences between cells that uniquely express ESR1 versus those that uniquely express ESR2, we performed differential gene expression and pathway enrichment analyses. C_LI ResultsO_LICount and distribution analyses revealed no significant ESR2/ER{beta} dominance in any cell or tissue type by Fishers Exact Tests and Wilcoxon Rank Sum Tests. Differential gene expression and pathway enrichment analyses suggest distinct roles of each estrogen receptor isoform. C_LI Conclusion(s)O_LIOverall, our results argue against a simplified model of ER{beta} dominance and instead propose a dual-isoform and cell and tissue-specific framework for understanding estrogen receptor signaling in endometriosis. These findings hold important implications for future therapeutic strategies. Specifically, treatments that target ER{beta} alone may fail to account for the functional role and relative abundance of ER. In the future, therapeutic approaches that consider isoform-specific, tissue-specific, and cell-specific expression patterns may prove most effective in reducing recurrence and improving outcomes for patients. C_LI Condensation pageO_ST_ABSTweetable statementC_ST_ABSSingle cell RNA Sequencing meta-analysis shows estrogen receptor beta is not dominantly expressed in most endometriosis tissues. Estrogen receptor alpha to estrogen receptor beta ratios vary by cell type and tissue type. Each isoform directs cell-type specific behavior in endometriosis and disease-free tissues. AJOG at a GlanceO_LIWhy was this study conducted? O_LIWe wanted to characterize estrogen receptor betas expression in endometriosis and evaluate its therapeutic relevance. C_LI C_LIO_LIWhat are the key findings? O_LIEstrogen receptor beta is not dominantly expressed in any tissue. Estrogen receptor alpha and estrogen receptor beta have disease- and cell-type specific behaviors. C_LI C_LIO_LIWhat does this study add to what is already known? O_LIIt characterizes estrogen receptor isoform expression and signaling by cell type. It also challenges the current estrogen receptor beta dominance hypothesis, meaning estrogen receptor beta may not be a key driver of endometriosis. C_LI C_LI

systems biology↗

bioRxiv2023

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimers disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimers disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer HighlightsO_LIPhosphorylation-level dysregulation surpasses protein expression C_LIO_LIHigher phospho-dysregulation in females, starting as early as 3-month time point C_LIO_LINovel candidates BIG3, CLCN6, and STX7 exhibit consistent phospho-dysregulation C_LIO_LIDeveloped AD-Xplorer: Online tool to explore Alzheimers disease phospho-proteome C_LI In BriefThis study investigates dysregulation of phospho-proteome in an Alzheimers disease (AD) mouse model, identifying consistent phosphorylation of established AD biomarkers APOE and GFAP, along with novel candidate biomarkers BIG3, CLCN6, and STX7. In addition, the study observes significant PDK1 dysregulation at 9 months, particularly in females. AD-Xplorer, our interactive tool for exploring temporal and sex-linked phosphorylation changes, protein expression, kinase activities, and pathway enrichment, empowers researchers to gain deeper insights into AD mechanisms and uncover novel biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2022

Are under-studied proteins under-represented? How to fairly evaluate link prediction algorithms in network biology

For biomedical applications, new link prediction algorithms are continuously being developed and these algorithms are typically evaluated computationally, using test sets generated by sampling the edges uniformly at random. However, as we demonstrate, this evaluation approach introduces a bias towards "rich nodes", i.e., those with higher degrees in the network. More concerningly, this bias persists even when different network snapshots are used for evaluation, as recommended in the machine learning community. This creates a cycle in research where newly developed algorithms generate more knowledge on well-studied biological entities while under-studied entities are commonly overlooked. To overcome this issue, we propose a weighted validation setting specifically focusing on under-studied entities and present AWARE strategies to facilitate bias-aware training and evaluation of link prediction algorithms. These strategies can help researchers gain better insights from computational evaluations and promote the development of new algorithms focusing on novel findings and under-studied proteins. TeaserSystematically characterizes and mitigates bias toward well-studied proteins in the evaluation pipeline for machine learning. Code and data availabilityAll materials (code and data) to reproduce the analyses and figures in the paper is available in figshare (doi:10.6084/m9.figshare.21330429). The code for the evaluation framework implementing the proposed strategies is available at github{dagger}. We provide a web tool{ddagger} to assess the bias in benchmarking data and to generate bias-adjusted test sets.

systems biology↗

bioRxiv2022

Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites

MotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations. ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks. ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations. AvailabilityThe code and data are available at compbio.case.edu/NetKSA.

bioinformatics↗

bioRxiv2021

Multiplex Embedding of Biological Networks Using Topological Similarity of Different Layers

Network embedding techniques, which provide low dimensional representations of the nodes in a network, have been commonly applied to many machine learning problems in computational biology. In most of these applications, multiple networks (e.g., different types of interactions/associations or semantically identical networks that come from different sources) are available. Multiplex network embedding aims to derive strength from these data sources by integrating multiple networks with a common set of nodes. Existing approaches to this problem treat all layers of the multiplex network equally while performing integration, ignoring the differences in the topology and sparsity patterns of different networks. Here, we formulate an optimization problem that accounts for inner-network smoothness, intra-network smoothness, and topological similarity of networks to compute diffusion states for each network. To quantify the topological similarity of pairs of networks, we use Gromov-Wasserteins discrepancy. Finally, we integrate the resulting diffusion states and apply dimensionality reduction (singular value decomposition after log-transformation) to compute node embeddings. Our experimental results in the context of drug repositioning and drug-target prediction show that the embeddings computed by the resulting algorithm, HO_SCPLOWATTUSHAC_SCPLOW, consistently improve predictive accuracy over algorithms that do not take into account the topological similarity of different networks.

bioinformatics↗

bioRxiv2021

Constructing Functional Networks of Phosphorylation Sites Using Co-Phosphorylation

Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases, and normal development. Here, we comprehensively investigate the concept of "co-phosphorylation", defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phospho-proteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzhemiers disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations, and pathway annotations to investigate the functional relevance of co-phosphorylation. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative co-phosphorylation. Specifically, we show that co-phosphorylation can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish co-phosphorylation as a useful resource for analyzing phospho-proteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation.

systems biology↗

bioRxiv2020

Uncovering complementary sets of variants for the prediction of quantitative phenotypes

MotivationGenome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. ResultsWe propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-of-the-art selection methods while having a simpler premise and being at least 2 orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with ~107 variants in a matter of minutes while taking the dependencies between the variants into account. ConclusionMacarons can offer a reasonable trade-off between phenotype predictivity, runtime and the complementarity of the selected subsets. The framework we present can be generalized to other high-dimensional feature selection problems within and beyond biomedical applications. AvailabilityMacarons is implemented in Matlab and the source code is available at: https://github.com/serhan-yilmaz/macarons

bioinformatics↗

bioRxiv2020

Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

MotivationLink prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes-if available- of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art. ResultsWe select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link prediction AvailabilityOur method is implemented as a python library and is available at githublink Contactmustafa.coskun@agu.edu.tr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Robust Inference of Kinase Activity using Functional Networks

Mass spectrometry enables high-throughput screening of phospho-proteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimers disease and Parkinsons disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

systems biology↗

bioRxiv2020

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules in Breast Cancer

MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. ResultsWe develop a network-based algorithm, CoPPNO_SCPLOWETC_SCPLOW, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural, and functional association of phosphosites, kinase-substrate associations, and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry based phosphorylation datasets for breast cancer, and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive breast cancer subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers. Availability and implementationCoPPNO_SCPLOWETC_SCPLOW is available at http://compbio.case.edu/coppnet/

bioinformatics↗

bioRxiv2019

DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations Involving Understudied Kinases

Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase specific predictions, yet for a large body of kinases, only a few or no target sites are reported. We present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas.

bioinformatics↗

bioRxiv2018

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naive Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing a to comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. Author SummaryKinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. Most of the current computational methods for prediction of kinase-substrate associations use static information such as sequence motifs and physical interactions to generate predictions. However, phosphorylation is a dynamic process and these static predictions may overlook unique features of cellular context, where kinases may be rewired. In this manuscript, we propose a computational method, CoPhosK, which uses the mass spectrometry based phosphoproteomics data to predict the kinase for all identified phosphosites in the experiment. We show that our approach complements and extends existing approaches.

bioinformatics↗