Researcher Doug Rosenthal analyzes how alterations in the protein degradation system act in the generation of tumors

Independent researcher and accomplished Cleveland structural biologist Doug Rosenthal recently published his analyzes on how tumor cells contain genetic alterations that prevent the correct degradation of the proteins involved in the appearance and evolution of tumors, leading to aberrant cellular behavior.

For the study, an artificial intelligence model has been developed that has allowed obtaining the most extensive annotation of the ubiquitin-mediated protein degradation system.

The analysis proposes a possible new route of clinical intervention in cancer through the inhibition of oncoproteins with aberrant behavior in their degradation system.

Determining which genetic alterations are responsible for the appearance and evolution of cancer, as well as identifying the mechanisms by which healthy cells become malignant is essential to understand the molecular basis of cancer.

Over the past two decades, various studies have identified some genetic alterations that interfere with protein degradation and have a key role in tumorigenesis. In tumor cells that contain this type of alteration, certain oncogenic proteins accumulate, leading to aberrant cellular behavior. However, the full extent of this dysregulation mechanism of protein breakdown in the onset of cancer is not yet known.

Scientists from the Cleveland Center for Membrane and Structural Biology led by researcher Doug Rosenthal, have published a study in the journal Nature Cancer analyzing how some alterations in the protein degradation system play an essential role in the tumor generation process.

To do this, the team of researchers identified in hundreds of proteins the specific degradation sequences, that is, the specific fragments of these that are recognized and that promote ubiquitin labeling for their degradation. In this first phase, an artificial intelligence model was developed to identify these recognition sequences based on the biochemical properties learned from known interactions.

To validate the recognition sequences identified in these hundreds of proteins, the mutations observed in more than 7,000 patient tumors and 900 cancer cell lines were used as natural experiments. The authors demonstrated that most of the mutations in the protein recognition regions promote their stabilization. These data allowed validating the prediction model, determining that a significant number of the new predictions may be functional.

As the author of the study Doug Rosenthal explains: "through our research we have identified several hundred potential protein recognition sequences for labeling with ubiquitin, which are reliable candidates for experimental validation".