A team led by scientsts in the Baker lab has combined recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. This breakthrough has significant implications for understanding the biochemical processes that are common to all animals, plants, and fungi. This open-access work appears in Science.
Proteins are the workhorses of all cells, but they rarely act alone. Different proteins often must fit together to form precise “complexes” that carry out specific tasks, including reading genes, digesting nutrients, and responding to signals from neighboring cells and the outside world. When protein complexes malfunction, disease can result.
“This work shows that deep learning can now generate real insights into decades-old questions in biology —not just what a particular protein looks like, but also which proteins come together to interact,” said senior author Qian Cong, an assistant professor in the department of biophysics at the University of Texas Southwestern Medical Center.
To exhaustively map the interactions that give rise to protein complexes, a team of structural biologists from UW Medicine, University of Texas Southwestern Medical Center, Harvard University, and other several institutions examined all known gene sequences in yeast. Using advanced statistical analyses, they identified pairs of genes that naturally acquire mutations in a linked fashion. They reasoned that such shared mutations are a sign that the proteins encoded by the genes must physically interact.
The researchers also used new deep-learning software to model the three-dimensional shapes of these interacting proteins. RoseTTAFold, invented
