Hyderabadi cracks decades-old global biological puzzle at 25 years of age
Using the AI, a young scientist Mohan Vamsi Nallapareddy and his team developed an AI tool, which can predict protein-production failures at the molecular level and such predictions could significantly transform cancer treatment and genetic therapies.
Hyderabad: A Hyderabadi has achieved a breakthrough in the decades-old global biological puzzle involving amino acid depletion and protein production in humans, at just under the age of 25 years.
Using the AI, the young scientist, Mohan Vamsi Nallapareddy, and his team developed a tool that can predict protein-production failures at the molecular level, and such predictions could significantly transform cancer treatment and genetic therapies.
Every cell in the human body uses amino acids derived from food to create proteins essential for life. However, in malnutrition, chronic illness, or rapidly growing cancers, certain amino acids become deficient.
When this happens, the cell’s protein-production machinery slows down or stops altogether. Incorrect or incomplete proteins are formed, and in some cases, vital proteins fail to form at all. This molecular malfunction weakens immunity, disrupts cellular functions, and worsens disease.
For years, scientists struggled to pinpoint the exact genetic sequences responsible for these failures. Using a deep-learning artificial intelligence model, Vamsi and his collaborators identified the mechanisms behind this breakdown.
According to researchers, the model can read the genetic code associated with any protein-making gene and identify which amino acids are missing. It also precisely predicts where protein synthesis will stall and recognises the codon patterns responsible for these disruptions. This will now enable researchers to analyse a gene sequence and detect potential protein-production failures before they occur within the body.
The model offers deeper insights into how nutritional deprivation intensifies disease and sheds new light on cancer biology and metabolic disorders. It supports the development of more accurate gene therapies and aids in designing artificial proteins. In the biopharmaceutical sector, the technology can improve the manufacturing of insulin, vaccines, antibodies, and other biologics.
Further, the model helps explain why certain genetic mutations cause disease only in specific conditions. This achievement was published in Communications Biology, a journal from the Nature group. “From here, I will focus on precision medicine and gene therapy,” said Vamsi.
After completing schooling in Hyderabad and a BE in Computer Science from BITS Pilani, Vamsi joined University College London as a research assistant. He is currently a doctoral assistant at the École Polytechnique Fédérale de Lausanne in Switzerland under the guidance of Pierre Vandergheynst, an expert in convolutional neural networks on graphs.
