Many proteins exert their function by switching among different structures. Knowing the
conformational ensembles affiliated with these states is critical to elucidate key mechanistic …

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 …

More than three billion years of evolution have produced an image of biology encoded into
the space of natural proteins. Here we show that language models trained at scale on …

Genomic (DNA) sequences encode an enormous amount of information for gene regulation
and protein synthesis. Similar to natural language models, researchers have proposed …

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

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 …

Recent breakthroughs have used deep learning to exploit evolutionary information in
multiple sequence alignments (MSAs) to accurately predict protein structures. However …

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

AlphaFold2 and related computational systems predict protein structure using deep learning
and co-evolutionary relationships encoded in multiple sequence alignments (MSAs) …