The development of medical applications of machine learning has required manual
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated …

The twenty-first century is presenting humankind with unprecedented environmental and
medical challenges. The ability to design novel proteins tailored for specific purposes would …

The vast majority of missense variants observed in the human genome are of unknown
clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on …

The Internet contains a wealth of knowledge—from the birthdays of historical figures to
tutorials on how to code—all of which may be learned by language models. However, while …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …

Abstract AlphaFold2 (ref.) has revolutionized structural biology by accurately predicting
single structures of proteins. However, a protein's biological function often depends on …

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 …

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

The prediction of protein subcellular localization is of great relevance for proteomics
research. Here, we propose an update to the popular tool DeepLoc with multi-localization …