Elodie Laine
Laboratoire de Biologie Computationnelle et Quantitative, Sorbonne Université-CNRS, Paris, France
Proteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. I will present DANCE, a fully-automated computational pipeline for Dimensionality Analysis for protein Conformational Exploration. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. DANCE’s source code is available at: https://github.com/PhyloSofS-Team/DANCE.
