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 of …

Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution
methods show promise, but an explicit sequence-to-structure map remains elusive. …

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 …

Computational prediction of protein structure from sequence is broadly viewed as a foundational
problem of biochemistry and one of the most difficult challenges in bioinformatics. Once …

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

Deep learning using neural networks relies on a class of machine-learnable models constructed
using ‘differentiable programs’. These programs can combine mathematical equations …

In mammalian cells, much of signal transduction is mediated by weak protein–protein interactions
between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in …

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with
exceptionally high accuracy. Its implementation, however, lacks the code and data required …

Background Rapid progress in deep learning has spurred its application to bioinformatics
problems including protein structure prediction and design. In classic machine learning …

Proteins power a vast array of functional processes in living cells. The capability to create
new proteins with designed structures and functions would thus enable the engineering of …