EPFL has developed a new AI approach to protein design

EPFL researchers have developed a novel AI-driven model designed to predict protein sequences from backbone scaffolds, incorporating complex molecular environments. It promises significant advancements in protein engineering and applications across various fields, including medicine and biotechnology.

Designing proteins that can perform specific functions involves understanding and manipulating their sequences and structures. This task is crucial for developing targeted treatments for diseases and creating enzymes for industrial applications.

One of the grand challenges in protein engineering is designing proteins de novo, meaning from scratch, to tailor their properties for specific tasks. This has profound implications for biology, medicine, and materials science. For instance, engineered proteins can target diseases with high precision, offering a competitive alternative to traditional small molecule-based drugs.

Additionally, custom-designed enzymes, which act as natural catalysts, can facilitate rare or nonexistent reactions in nature. This capability is particularly valuable in the pharmaceutical industry for synthesizing complex drug molecules and in environmental technology for breaking down pollutants or plastics more efficiently.

A team of scientists led by Matteo Dal Peraro at EPFL has now developed CARBonAra (Context-aware Amino acid Recovery from Backbone Atoms and heteroatoms), an AI-driven model that can predict protein sequences, but by taking into account the restraints imposed by different molecular environments – a unique accomplishment.

CARBonAra is trained on a dataset of approximately 370,000 subunits, with an additional 100,000 for validation and 70,000 for testing from the Protein Data Bank (PDB). Learn more about.

Source: press release
photo credits: Alexandra Banbanaste (EPFL)