Machine learning (ML) is transforming protein structure prediction. Algorithms can predict 3D structures from amino acid ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. In a study published in the journal Nature ...

As bispecifics, ADCs, protein degraders, and AI-designed mini-proteins move into the clinic, discovery teams face a new ...

The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new functions and can solve problems in medicine or materials science. The past ...

This review provides an overview of traditional and modern methods for protein structure prediction and their characteristics and introduces the groundbreaking network features of the AlphaFold family ...

For decades, scientists have relied on structure to understand protein function. Tools like AlphaFold have revolutionized how researchers predict and design folded proteins, allowing for new ...

In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction. The ...

In the wee hours of an October morning, David Baker, a protein biologist at the University of Washington (UW), received the most-awaited phone call in a scientist’s career. Halfway around the world, ...

Fusion oncoproteins arise when a gene fuses with another gene and acquires new abilities. Such abilities can include the ...

Intrinsically disordered proteins (IDPs) make up about 30 percent of our proteome. They are important to many fundamental aspects of biology and disrupted in disease. Since they lack a stable shape, ...