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Prediction Of Kinase-Substrate Associations Using The Functional Landscape Of Kinases And Phosphorylation Sites, Serhan Yilmaz, Filipa Blasco Tavares Pereira Lopes, Mark R. Chance, Mehmet Koyutürk
Prediction Of Kinase-Substrate Associations Using The Functional Landscape Of Kinases And Phosphorylation Sites, Serhan Yilmaz, Filipa Blasco Tavares Pereira Lopes, Mark R. Chance, Mehmet Koyutürk
Faculty Scholarship
Protein 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, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships …
Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica D. Wiredja, Daniela M. Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance
Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica D. Wiredja, Daniela M. Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance
Faculty Scholarship
We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve 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, …
Gene, Pathway And Network Frameworks To Identify Epistatic Interactions Of Single Nucleotide Polymorphisms Derived From Gwas Data, Yu Liu, Sean Maxwell, Tao Feng, Xiaofeng Zhu, Robert C. Elston, Mehmet Koyutürk, Mark R. Chance
Gene, Pathway And Network Frameworks To Identify Epistatic Interactions Of Single Nucleotide Polymorphisms Derived From Gwas Data, Yu Liu, Sean Maxwell, Tao Feng, Xiaofeng Zhu, Robert C. Elston, Mehmet Koyutürk, Mark R. Chance
Faculty Scholarship
Background: Interactions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.Results: We developed four biologically based frameworks to …
An Integrative -Omics Approach To Identify Functional Sub-Networks In Human Colorectal Cancer, Rod K. Nibbe, Mehmet Koyutürk, Mark R. Chance
An Integrative -Omics Approach To Identify Functional Sub-Networks In Human Colorectal Cancer, Rod K. Nibbe, Mehmet Koyutürk, Mark R. Chance
Faculty Scholarship
Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that …
Target Selection And Annotation For The Structural Genomics Of The Amidohydrolase And Enolase Superfamilies, Xiaojing Zheng, Mark R. Chance
Target Selection And Annotation For The Structural Genomics Of The Amidohydrolase And Enolase Superfamilies, Xiaojing Zheng, Mark R. Chance
Faculty Scholarship
To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not …