Prediction of protein structure from sequence has been intensely studied for many decades,
owing to the problem's importance and its uniquely well-defined physical and computational …

Highlights⿢The recent use of deep learning has dramatically improved the accuracy of non-
homologous protein structure modeling.⿢Protein structure prediction problem was largely …

Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …

Personalized genome sequencing has revealed millions of genetic differences between
individuals, but our understanding of their clinical relevance remains largely incomplete. To …

Genomic rearrangements, encompassing mutational changes in the genome such as
insertions, deletions or inversions, are essential for genetic diversity. These rearrangements …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Insertion sequence (IS) elements are the simplest autonomous transposable elements found
in prokaryotic genomes. We recently discovered that IS110 family elements encode a …

In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …

Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …

Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …