Characterizing and predicting protein modifications with a novel AI tool

Original story from Baylor College of Medicine (TX, USA). An AI model that reveals how protein modifications link genetic mutations to disease has been developed. Researchers at Baylor College of Medicine (TX, USA) have developed an AI model that reveals how protein modifications link genetic mutations to disease. The method, called DeepMVP, significantly outperforms previously published models and has implications for the development of novel therapeutics. “Proteins are responsible for all the functions of the body, from growing tissues to regulating metabolism or fighting disease. Their functions are often regulated by modifications that take place after proteins are made through a process called post-translational modification (PTM),” explained corresponding author Bing Zhang, professor at the Lester and Sue Smith Breast Center and of molecular and human genetics at Baylor. The modifications include the addition of chemical groups, such as phosphates or sugars, that influence how a protein behaves, where it goes in the cell or how long it lasts. When PTMs go wrong, the proteins may not perform as expected and contribute to diseases like cancer, heart conditions or neurological disorders. Key cellular complex plays unexpected role in gene expression Research has uncovered a previously unknown, widespread role of ORC in regulating human cell gene expression.