Predicting compatibility between ferredoxins and the Fe protein of nitrogenase using in silico protein modeling

Biological nitrogen fixation is the process by which certain bacteria and archaea use the enzyme nitrogenase to reduce atmospheric nitrogen into bioavailable ammonium. Engineering non-nitrogen-fixing organisms, like plants, to use nitrogenase could reduce dependency on synthetic fertilizer and mitigate the environmental impacts of industrial fertilizer production. However, nitrogenase activity requires delivery of reducing power by small electron carrying proteins known as ferredoxins and flavodoxins, and successfully engineering nitrogenase into new systems will require a mechanistic understanding of electron delivery by these proteins. Most organisms often have multiple ferredoxins, raising the question of which ferredoxin can support nitrogenase activity. The purpose of this study is to gain insight into how we can predict which ferredoxin is compatible with the Fe protein, the component of nitrogenase that interacts with ferredoxin or flavodoxin. Our in silico protein-protein docking simulations reveal that ferredoxins and flavodoxins involved in nitrogen fixation have a distance [≤] 10 [A] between their redox cofactor and the [4Fe-4S] cluster of the Fe protein. We found shorter cofactor distance contributes to faster intermolecular electron tunneling rates (> 106 sec-1). These nitrogen-fixing bacterial ferredoxins also form more complementary interactions with the Fe protein compared to non-nitrogen-fixing bacterial and plant ferredoxins. Heterologous expression of a set of ferredoxins from both nitrogen-fixing and non-nitrogen-fixing bacteria in the diazotroph Rhodopseudomonas palustris support our model-derived prediction that cofactor distances of [≤] 10 [A] favor nitrogenase compatibility. These findings offer a framework to predict and potentially enhance ferredoxin-nitrogenase compatibility, which will help to improve our ability to engineer nitrogen fixation into non-nitrogen-fixing organisms like plants.