Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN
Hsi-Wen Kao, Wei-Lin Lu, Meng‐Ru Ho, Yu‐Fong Lin, Yun-Jung Hsieh, Tzu‐Ping Ko, Shang‐Te Danny Hsu, Kuen‐Phon Wu
Abstract
We used the deep learning tool ProteinMPNN to redesign ubiquitin (Ub) as a specific and functionally stimulating/enhancing binder of the Rsp5 E3 ligase. We generated 20 extensively mutated─up to 37 of 76 residues─recombinant Ub variants (UbVs), named R1 to R20, displaying well-folded structures and high thermal stabilities. These UbVs can also form stable complexes with Rsp5, as predicted using AlphaFold2. Three of the UbVs bound to Rsp5 with low micromolar affinity, with R4 and R12 effectively enhancing the Rsp5 activity six folds. AlphaFold2 predicts that R4 and R12 bind to Rsp5's exosite in an identical manner to the Rsp5-Ub template, thereby allosterically activating Rsp5-Ub thioester formation. Thus, we present a virtual solution for rapidly and cost-effectively designing UbVs as functional modulators of Ub-related enzymes.