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- ItemDisulfide Bond Engineering of an Endoglucanase from Penicillium verruculosum to Improve Its Thermostability(Basel : Molecular Diversity Preservation International (MDPI), 2019) Bashirova, Anna; Pramanik, Subrata; Volkov, Pavel; Rozhkova, Aleksandra; Nemashkalov, Vitaly; Zorov, Ivan; Gusakov, Alexander; Sinitsyn, Arkady; Schwaneberg, Ulrich; Davari, Mehdi D.Endoglucanases (EGLs) are important components of multienzyme cocktails used in the production of a wide variety of fine and bulk chemicals from lignocellulosic feedstocks. However, a low thermostability and the loss of catalytic performance of EGLs at industrially required temperatures limit their commercial applications. A structure-based disulfide bond (DSB) engineering was carried out in order to improve the thermostability of EGLII from Penicillium verruculosum. Based on in silico prediction, two improved enzyme variants, S127C-A165C (DSB2) and Y171C-L201C (DSB3), were obtained. Both engineered enzymes displayed a 15–21% increase in specific activity against carboxymethylcellulose and β-glucan compared to the wild-type EGLII (EGLII-wt). After incubation at 70 °C for 2 h, they retained 52–58% of their activity, while EGLII-wt retained only 38% of its activity. At 80 °C, the enzyme-engineered forms retained 15–22% of their activity after 2 h, whereas EGLII-wt was completely inactivated after the same incubation time. Molecular dynamics simulations revealed that the introduced DSB rigidified a global structure of DSB2 and DSB3 variants, thus enhancing their thermostability. In conclusion, this work provides an insight into DSB protein engineering as a potential rational design strategy that might be applicable for improving the stability of other enzymes for industrial applications.
- ItemCan constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase(Gotenburg : Research Network of Computational and Structural Biotechnology (RNCSB), 2021) Contreras, Francisca; Nutschel, Christina; Beust, Laura; Davari, Mehdi D.; Gohlke, Holger; Schwaneberg, UlrichCellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.