CDS Professor Receives NIH Grant to Study Co-Phosphorylation Networks

Mehmet Koyuturk, Marzieh Ayati, and Mark Chance
Mehmet Koyuturk, Marzieh Ayati, and Mark Chance

In human cells, attachment of a phosphate to a protein at certain sites can alter the activity and the function of the protein. This mechanism, known as protein phosphorylation, is often used to communicate signals within and between cells. Recent research shows that likely over 70% of human proteins can be phosphorylated. Dysregulation of protein phosphorylation is known to play an important role in many diseases, including cancer, Alzheimer's disease, Parkinson's disease, obesity and diabetes, and fatty liver disease. Indeed, many modern drugs used to treat various cancers target kinases, the enzymes that are responsible for the phosphorylation of proteins. Despite the success of the "genomic revolution" and the importance of protein phosphorylation in human biology, the knowledge on protein phosphorylation in humans is quite limited. To date, thousands of phosphorylation sites on human proteins have been discovered, but the kinases that are responsible for phosphorylating these sites could be identified for less than 5% of these sites.

Computer and Data Sciences Professor Mehmet Koyuturk applies data science to accelerate discoveries on protein phosphorylation and cellular signaling. Koyuturk's collaborator Prof. Mark Chance, who is Vice Dean for Research at the School of Medicine, has pioneered the development of technology for using mass spectrometry to quantify phosphorylation levels of thousands of proteins in a given biological sample. In the last decade, this technology has been used to generate large-scale data on protein phosphorylation in many different contexts. To capitalize on the availability of these rich datasets, Koyuturk and Chance together developed a project that aims to drive discovery by integrating rich and diverse phosphorylation data. The project builds on the dissertation work of Prof. Marzieh Ayati, who laid out the foundations of "co-phosphorylation networks" as a Ph.D. student at CWRU before joining the faculty at the University of Texas - Rio Grande Valley in 2018. 

Use of "co-phosphorylation networks" in identifying kinases that target phosphorylation sites

Recognizing the challenges associated with analyzing phospho-proteomic data, the team utilizes network science to extract patterns of correlation in phosphorylation levels of proteins. By organizing these patterns in "co-phosphorylation networks" and using graph-theoretic algorithms and machine learning, they extract knowledge from these networks, which are then used to develop new biological hypotheses.  Besides generating basic biological knowledge such as functional annotation of phospho-proteins, kinases, and phosphatases, the project also aims to characterize the signaling processes that are affected in cancers and Alzheimer's disease.  The project, which is supported by a 1.35M R01 research grant from the National Institutes of Health, will last 4 years.