Helder Veras Ribeiro Filho’s scientific research bridges the fields of pharmacology, immunology and computer science. Driven by a desire to understand immunological interactions at the atomic level, the pharmacist turned to computational tools to investigate the complexities of immune cell recognition.
His ultimate goal is to develop innovative treatments for disease by deciphering how immune cells identify and target diseased cells. With a background in pharmacology from the Federal University of Ceará, he also hold a master’s degree in experimental pharmacology from the same institution and a Ph.D. in computational biology from the State University of Campinas (UNICAMP). His expertise is further enhanced by a postdoctoral degree in immunoinformatics from the University of Maryland. Despite the distance from his native Ceará, he remains passionate about kitesurfing.
Our immune system is a powerful defense network that protects us from harmful invaders such as viruses and bacteria, as well as internal threats such as cancer. T cells, a type of white blood cell, play a key role. They use T cell receptors (TCRs) to recognize specific protein fragments, or peptides, on the surface of other cells. This recognition triggers an immune response that eliminates the infected or abnormal cells.
This project will investigate whether artificial intelligence can predict this complex recognition process. We will analyze data on TCRs and peptides, including their amino acid sequences and 3D structures, using machine learning and deep learning. The goal is to develop models that can accurately predict which TCRs will bind to specific peptides. These models will improve our understanding of the immune system and aid in the diagnosis of disease. In addition, we will use generative models to design new TCRs that can precisely target specific peptides, such as those found on cancer cells, potentially leading to the development of innovative immunotherapies.
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