Forscher können das KI-Sprachmodell wieder entwickeln, um zuvor „undstörbare“ Krankheitsproteine zu zielen

From the article

>In 2024, the Nobel Prize in Chemistry was awarded to researchers at Google DeepMind for developing AlphaFold, an AI system that predicts the 3D structure of proteins—a major advance in drug discovery. But many disease-related proteins, including those involved in cancer and neurodegeneration, don’t have stable structures.

>That’s where PepMLM takes a different approach—instead of relying on structure, the tool uses only the protein’s sequence to design peptide drugs. This makes it possible to target a much broader range of disease proteins, including those that were previously considered „undruggable.“

>“Most drug design tools rely on knowing the 3D structure of a protein, but many of the most important disease targets don’t have stable structures,“ said Pranam Chatterjee, senior author of the study who led the work at Duke and is now a faculty member at the University of Pennsylvania. „PepMLM changes the game by designing peptide binders using only the protein’s amino acid sequence,“ said Chatterjee.

>In lab tests, the team showed that PepMLM could design peptides—short chains of amino acids—that stick to disease-related proteins and, in some cases, help destroy them. These included proteins involved in cancer, reproductive disorders, Huntington’s disease, and even live viral infections.

>“This is one of the first tools that can design these kinds of molecules directly from the protein’s sequence,“ said Chatterjee. „It opens the door to faster, more effective ways to develop new treatments.“

>The study included major contributions from McMaster University, where Christina Peng, a Ph.D. student in the Truant Lab, led the Huntington’s disease experiments.