AI Uses Physics to Illuminate the Dark Proteome

""The design of folded proteins has advanced substantially in recent years," wrote co-authors Krishna Shrinivas, Michael P. Brenner, and Ryan K. Krueger. "However, many proteins and protein regions are intrinsically disordered and lack a stable fold, that is, the sequence of an intrinsically disordered protein (IDP) encodes a vast ensemble of spatial conformations that specify its biological function.""

"Akin to physics and modern cosmology wherein there is dark matter, in biology there exists the dark proteome. The exact composition of dark matter is not known, despite estimates that it comprises 27% of the total mass-energy density in the universe and 85% of matter in the universe according to 2022 research by Yuan, et al. published in Physics Reports. In the dark proteome, there may exist a potential treasure trove of undiscovered therapeutic targets for pharmaceutical drugs to combat diseases. The dark proteome includes proteins with unknown structures, understudied proteins, undetected proteins, proteins from non-canonical genes that deviate from standard genes, and intrinsically disordered proteins."

"A milestone in biology has been achieved thanks to artificial intelligence (AI) machine learning and applied physics. Intrinsically disordered proteins lack a well-defined structure. The lack of a stable structure makes IDPs next to impossible to study with traditional structural biology tools such as X-ray crystallography and cryo-electron microscopy."

AI and applied physics enabled design of unstable proteins that belong to the dark proteome. Intrinsically disordered proteins (IDPs) lack stable folds and encode vast ensembles of spatial conformations that determine biological function. The dark proteome comprises proteins with unknown structures, understudied or undetected proteins, proteins from non-canonical genes, and IDPs, and may contain undiscovered therapeutic targets. The absence of stable structure makes IDPs difficult to analyze using X-ray crystallography and cryo-electron microscopy. AI-driven access to these protein classes can accelerate advances in biology, synthetic biology, and personalized medicine. The human proteome comprises nearly 20,000 proteins.

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