Frontiers in Protein Structure Research

© 2022 by the authors; CC BY-NC-ND license

configurational entropy; force fields; intrinsically disordered proteins; protein folding; NMR; high hydrostatic pressure; thermodynamic stability; α-helical bundle; Li-Fraumeni syndrome; hereditary breast cancer; germline TP53 missense variants; quantitative prediction model; protein conformation; protein–protein interactions; protein–protein binding; protein–protein complex; coarse-grained modeling; multiscale modeling; UFM1; UBA5; UFC1; protein-protein interactions; NMR; complex structure; oxidative stress; Nrf2; Keap1; nuclear magnetic resonance spectroscopy; hydrogen/deuterium exchange; mass spectrometry; circular dichroism; intrinsically disordered; bifidobacteria; fucosidases; glycosyl hydrolases; conserved domains; human milk; analytical ultracentrifugation; CO₂ concentrating mechanism; diffusion-ordered NMR spectroscopy; electrospray ionization mass spectrometry; homotetramer; manganese; metalloprotein; photosynthesis; small-angle X-ray scattering; analytical ultracentrifugation; C1q; calcium binding proteins; circular dichroism; genetic variation; otoconia; otolin-1; OTOL1; site-directed mutagenesis; thermal shift assay; B.1.1.7; B.1.617.2; COVID-19; E484Q; T478K and L452R mutation; N501Y mutation; spike protein; tetrabromobisphenol A; tetrabromobisphenol S; erythrocyte membrane; retardants; erythrocytes; mass spectrometry; protein folding; protein–ligand interactions; protein dynamics; FK506-binding protein; FKBP12; FKBP51; oxidative folding; glutathionylation; nitrosylation; cysteine reactivity; ribosomal exit tunnel; transient complex; glutathione; intrinsically disordered proteins; phosphorylation; force fields; transmembrane proteins; saturation mutagenesis; deep sequencing; residue packing; intrinsically disordered proteins; phosphorylation; force fields; transmembrane proteins; intrinsically disordered proteins; deep learning; convolutional neural network; bidirectional long-short term memory; protein; prediction; contact; distance; deep learning; alphafold; ProSPr; CASP; dataset; retrainable; intrinsically disordered proteins; mutual synergetic folding; solvent accessibility of peptide bonds; inter-subunit interaction; solvent-accessible surface area; Shannon information entropy; amino acid composition; glucose; GlcNAc; galactose; GalNAc; mannose; xylose; fucose; Neu5Ac; glucuronate; iduronate; tetrahydropyran; protein folding; entropy; free energy; free energy landscape; energy-dependent protein folding; co-translational protein folding; molecular chaperones; physical model of protein folding; n/a