Cysteine side chains can exist in distinct oxidation states depending on the pH and redox
potential of the environment, and cysteine oxidation plays important yet complex regulatory …

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

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 ability to computationally generate novel yet physically foldable protein structures could
lead to new biological discoveries and new treatments targeting yet incurable diseases …

Protein structure and function is determined by the arrangement of the linear sequence of
amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can …

The generation of de novo protein structures with predefined functions and properties
remains a challenging problem in protein design. Diffusion models, also known as score …

Major computational challenges exist in relation to the collection, curation, processing and
analysis of large genomic and imaging datasets, as well as the simulation of larger and …

Learning the design patterns of proteins from sequences across evolution may have
promise toward generative protein design. However it is unknown whether language …

Advanced Artificial Intelligence (AI) enabled large language models (LLMs) to revolutionize
Natural Language Processing (NLP). Their adaptation to protein sequences spawned the …

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 on tokens …