Natural language processing (NLP) is a field of computer science concerned with automated
text and language analysis. In recent years, following a series of breakthroughs in deep and …

The prediction of protein three-dimensional structure from amino acid sequence has been a
grand challenge problem in computational biophysics for decades, owing to its intrinsic …

We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …

The design of proteins that bind to a specific site on the surface of a target protein using no
information other than the three-dimensional structure of the target remains a challenge …

Self-supervised deep language modeling has shown unprecedented success across natural
language tasks, and has recently been repurposed to biological sequences. However …

Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …

Physical interactions between proteins are essential for most biological processes
governing life. However, the molecular determinants of such interactions have been …

Recently it has become possible to de novo design high affinity protein binding proteins from
target structural information alone. There is, however, considerable room for improvement as …

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

Rational protein engineering requires a holistic understanding of protein function. Here, we
apply deep learning to unlabeled amino-acid sequences to distill the fundamental features …