Biology subjects

Ayati, M.

Publications and source records attributed to Ayati, M..

6 recordsLinked to original sources

bioRxiv2023

Making Proteomics Accessible: RokaiXplorer for interactive analysis of phospho-proteomic data

In this work, we present RokaiXplorer, an intuitive web tool designed to address the scarcity of user-friendly solutions for proteomics and phospho-proteomics data analysis and visualization. RokaiXplorer streamlines data processing, analysis, and visualization through an interactive online interface, making it accessible to researchers without specialized training in proteomics or data science. With its comprehensive suite of modules, RokaiXplorer facilitates the analysis of phosphosites, proteins, kinases, and gene ontology terms. The tool offers functionalities such as data normalization, statistical testing, enrichment analysis, subgroup analysis, report generator, and multiple visualizations, including volcano plots, bar plots, heatmaps, tables, and interactive networks. Additionally, RokaiXplorer allows researchers to effortlessly deploy their own data browsers, fostering the sharing of research data and findings interactively. By providing simplicity, efficiency, and multi-level data analysis within a single application, it is poised to become a valuable resource for the scientific community working with phospho-proteomic data. Access RokaiXplorer at: http://explorer.rokai.io

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

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

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↗

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↗