Predicting the Structure of Enzymes with Metal Cofactors: The Example of [FeFe] Hydrogenases.

ABSTRACT: The advent of deep learning algorithms for protein folding opened a new era in the ability of predicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning predictions is mandatory. We use the example of [FeFe] hydrogenases, enzymes of unicellular algae promising for biotechnology applications to illustrate this situation. [FeFe] hydrogenase is an iron-sulfur protein that catalyzes the chemical reduction of protons dissolved in liquid water into molecular hydrogen as a gas. Hydrogen production efficiency and cell sensitivity to dioxygen are important para

SUBMITTER: Botticelli S 

PROVIDER: S-EPMC11011570 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature