Evolutionary-scale prediction of atomic-level protein structure with a language model

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins.

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• 1 We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model.
• 2 As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations.
• 3 This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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