The structure, function, and evolution of proteins depend on physical and genetic
interactions among amino acids. Recent studies have used new strategies to explore the …

In the course of evolution, proteins undergo important changes in their amino acid
sequences, while their three-dimensional folded structure and their biological function …

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …

Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …

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 …

The rational design of enzymes is an important goal for both fundamental and practical
reasons. Here, we describe a process to learn the constraints for specifying proteins purely …

The ability to design functional sequences and predict effects of variation is central to protein
engineering and biotherapeutics. State-of-art computational methods rely on models that …

Many high-throughput experimental technologies have been developed to assess the
effects of large numbers of mutations (variation) on phenotypes. However, designing …

The vast expansion of protein sequence databases provides an opportunity for new protein
design approaches which seek to learn the sequence-function relationship directly from …

The functions of proteins and RNAs are defined by the collective interactions of many
residues, and yet most statistical models of biological sequences consider sites nearly …