Speaker
Description
Amine transaminases (ATAs) are important enzymes for the production of chiral amines in the pharmaceutical and fine chemical industries[1]. However, the application of ATAs on novel substrates in an industrial setting usually still requires significant protein engineering efforts to optimize their structural and catalytic properties.
Intending to provide novel computational tools for in silico ATA engineering, we use our in-house software Constraint Network Analysis (CNA)[2] to study protein rigidity at the atomic level and map changes in protein rigidity during simulated heating to experimental measurements of thermostability. Additionally, we can identify structural weak spot residues, which have been shown to provide an increased likelihood for stability improvements upon mutation in other enzymes[3].
When comparing the predicted thermostability of 44 ATA variants to their experimentally measured temperature of half-inactivation (T50), we find two sub-populations of variants with significant, moderate correlations. These sub-populations arise from a single, highly stabilizing mutation, which is not identified by our out of the box application of CNA.We identified structural weak spot residues and applied the recently published machine learning model ProteinMPNN[4] with the goal to predict the most stabilizing mutations for every weak spot. These predicted novel variants are currently being tested experimentaly.
[1] F. Guo, P. Berglund, Green Chem., 2017, 19, 333-360.
[2] C. Pfleger, et al., J Chem Inf Model., 2013, 53, 1007-1015.
[3] C. Nutschel, et al., J Chem Inf Model. 2020, 60, 1568-1584.
[4] J. Dauparas, et al., Science. 2022, 378, 49-56.
| Authors | Docter, Steffen[a,b]; Gohlke, Holger[a,b] |
|---|---|
| Affiliation | [a] Institute of Bio- and Geosciences (IBG-4), Forschungszentrum Jülich GmbH, 52452 Jülich, Germany; [b] Institute of Pharmaceutical and Medicinal Chemistry, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany |
| Consent | Yes |
