Alexis Lamiable

TL;DR: PEP-FOLD3 is a novel computational framework that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance.

TL;DR: In this review, how HCA can be used to give insight into this last category of foldable segments is illustrated, with examples matching known 3D structures.

TL;DR: A new approach for the prediction of the coarse-grain 3D structure of RNA molecules is presented, which allows one to predict the global shape of large molecules of several hundreds of nucleotides that are out of reach of the state-of-the-art methods.

TL;DR: This updated HCDB is used to show that the hydrophobic amino acids of discordant clusters, i.e. those less abundant clusters for which the observed secondary structure is in disagreement with the binary pattern preference of the species to which they belong, are more exposed to solvent and are more involved in protein interfaces than the hydphobic amino acid of concordant clusters.

TL;DR: It is found that only the forward backtrack and a taboo sampling strategies can efficiently generate native or near‐native models, and such approaches are as efficient as former protocols, while being one order of magnitude faster, opening the door to the large scale de novo modeling of peptides and mini‐proteins.