Physics of Protein Folding, Misfolding, and Intrinsic Disorder: A Themed Issue in Honour of Professor Vladimir Uversky on the Occasion of His 60th Birthday

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: affinity chromatography; alpha-synuclein; AlphaFold; amino acid bias; amino acids; androgen receptor (AF1); average shape of conformational ensembles; bioinformatics; Biology, life sciences; biomineralization; biomolecular condensates; calcium carbonate; Calvin-Benson-Bassham cycle; canonical protein; charge clustering; charged-residue patterning; circular dichroism; coarse-grained simulation; coexistence line; cold hardiness; conditional order; conditionally disordered protein; conformational compactness; counterion condensation theory; COVID-19; Crkl; D/E repeats; dark proteomes; dimers; discrete molecular dynamics; disorder prediction; disorder propensity; disorder scale; DNA binding; drug targets; Dss1; duplexes; ellipsoid model; entanglement; flexible; fluorescence; fluorescence anisotropy; FUS; glutamate receptor; heterogeneity; hidden order; history of modern science; hub proteins; hydrodynamic radius; IDP; IDPs; integrative structural biology; intramolecular interaction; intrinsic disorder; intrinsic disordered regions; intrinsically disordered domains; intrinsically disordered protein; intrinsically disordered protein regions (IDRs); intrinsically disordered proteins; intrinsically disordered proteins (IDPs); intrinsically disordered regions; isoform; isothermal titration calorimetry; IUPred analysis; K/R repeats; kinetics; large-scale analysis; LC–MS/MS; light scattering; linker length; liquid-liquid phase separation; LLPS stability; long foldable segments; lung cancer; machine learning; Mathematics and Science; membrane; membrane-less organelles; metabolism regulation; molecular dynamics; molecular dynamics simulations; molecular mass; moonlighting protein; multivalency; mutations; Myc; n/a; nickel; NMR; nucleation pathways; Ostrinia nubilalis; otoliths; Parkinson’s; Paxillin; percolation; phase separation; physical interactions; polyelectrolytes; polymer physics; polymer rheology; predictive performance; predictor; proline content; protein disorder; protein function; protein purification; protein sequence; protein solubility; protein structure; protein-protein interaction; protein–protein interactions; pyHCA; Reference, Information and Interdisciplinary subjects; Research and information: general; RNA; salt-dependent binding affinity; SARS-CoV-2; SAXS; sequence composition; SH2 domains; single molecule fluorescence; site-directed mutagenesis; SLiM; small-molecule inhibitors; soluble domains; solvent-accessible surface area; stress; synapsin; synaptic vesicle; Tau; topology; transcription factor; tumor suppressor p53; unfolded; unstructured; van’t Hoff
• ISBN: 3036591133; 3036591125; 9783036591124; 9783036591131
• DOI: 10.3390/books978-3-0365-9113-1

This Special Issue of Biomolecules, “Physics of Protein Folding, Misfolding, and Intrinsic Disorder: A Themed Issue in Honour of Professor Vladimir Uversky on the Occasion of His 60th Birthday”, is a dedication to one of the discoverers of IDPs. This collection is a small token of the respect, admiration, and affection the contributors have for Prof. Vladimir (Volodya) Uversky. It is also a celebration of his illustrious career, and his accomplishments, and contributions to the IDP field that have inspired so many minds worldwide. This Special Issue presents the state of the art as it emerges from the contribution of the community of IDP researchers (IDPers), who have responded to the invitation to give credit to the pioneering work of Prof. Vladimir Uversky aimed at defining the class of disordered proteins and at promoting the attention of scientists toward the existence of “non-globular proteins”. The papers in this collection show the advancement of our knowledge through the application of an integrative structural approach and witnesses at the same time the interest of the IDPer community toward new concepts (i.e., liquid-liquid phase separation) and new methodological frontiers (i.e., the application of machine learning and artificial intelligence to disorder prediction and modelling).