Protein structure predictions articles from across Nature Portfolio

Cell-based and molecular exploration provide comparative characterization of currently developed PI3K inhibitors and structural insights for future inhibitor design.

ImmuneBuilder is a set of deep learning models trained to predict the structure of antibodies, nanobodies, and T-Cell receptors with state-of-the-art accuracy while being much faster than AlphaFold2 and AlphaFoldMultimer.

Mutations in a protein active site can alter function in useful ways, but the active site is sensitive to changes. Here the authors present a general strategy to design combinatorial mutation libraries. Applied to GFP, the authors isolate thousands of fluorescent designs that exhibit large and useful changes in spectral properties.

Recently, a pipeline for the design of protein-binding proteins using only the structure of the target protein was reported. Here, the authors report that the incorporation of deep learning methods into the original pipeline increases experimental success rate by ten-fold.

Advances in protein structure prediction have led to a significant influx of protein structure data. Here the authors exploit this data to offer an unbiased overview of complex sequence-structure-function relationships in the protein universe. This work opens up new uses for 3D structure data repositories in meta-omics and other fields of biology.

Prediction of antibody structures is critical for understanding and designing novel therapeutic and diagnostic molecules. Here, the authors present IgFold: a fast, accurate method for antibody structure prediction using an end-to-end deep learning model.