Crystal structures of DhaA223 and DhaA231. (a) Cartoon representations of DhaA223 (8OE2, red) and DhaA231 (PDB ID: 8OE6, dark blue) crystal structures aligned to the DhaA115 (PDB ID: 6SP5, gray). Residues of the catalytic pentad are shown as sticks. Stabilizing mutations are shown as spheres (pink spheres indicate mutations suggested by both FireProt and PROSS). (b) The structural context of selected stabilizing mutations. Newly formed stabilizing interactions involving other residues or water molecules (red spheres) are depicted by yellow dashed lines.
Zbyněk Prokop and David Bednář Research Groups
Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein–water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.
Kunka, A., Marques, S. M., Havlásek, M., Vašina, M,, Velátová, N., Cengelová, L., Kovář, J., Damborský, J., Marek, M., Bednář*. D., and Prokop*, Z. Advancing Enzyme’s Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning
ACS Catal. 2023, 13, 19, 12506–12518, https://doi.org/10.1021/acscatal.3c02575