The prediction of protein three-dimensional structure from amino acid sequence has been a
grand challenge problem in computational biophysics for decades, owing to its intrinsic …

In-cell structural biology aims at extracting structural information about proteins or nucleic
acids in their native, cellular environment. This emerging field holds great promise and is …

Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …

We describe a de novo computational approach for designing proteins that recapitulate the
binding sites of natural cytokines, but are otherwise unrelated in topology or amino acid …

Amino acid mutations that lower a protein's thermodynamic stability are implicated in
numerous diseases, and engineered proteins with enhanced stability can be important in …

Substantial improvements in enzyme activity demand multiple mutations at spatially
proximal positions in the active site. Such mutations, however, often exhibit unpredictable …

The field of protein design has made remarkable progress over the past decade. Historically,
the low reliability of purely structure-based design methods limited their application, but …

The design of structurally diverse enzymes is constrained by long-range interactions that are
necessary for accurate folding. We introduce an atomistic and machine learning strategy for …

The preservation of labile biomolecules presents a major challenge in chemistry, and deep
eutectic solvents (DESs) have emerged as suitable environments for this purpose. However …

The accurate prediction of protein stability upon sequence mutation is an important but
unsolved challenge in protein engineering. Large mutational datasets are required to train …