TY  - JOUR
AU  - Contreras, Francisca
AU  - Nutschel, Christina
AU  - Beust, Laura
AU  - Davari, Mehdi D.
AU  - Gohlke, Holger
AU  - Schwaneberg, Ulrich
TI  - Can Constraint Network Analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase
JO  - Computational and structural biotechnology journal
VL  - 19
SN  - 2001-0370
CY  - Gotenburg
PB  - Research Network of Computational and Structural Biotechnology (RNCSB)
M1  - FZJ-2021-00001
SP  - 743-751
PY  - 2021
AB  - Cellulases 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.
LB  - PUB:(DE-HGF)16
C6  - 33552446
UR  - <Go to ISI:>//WOS:000684850500008
DO  - DOI:10.1016/j.csbj.2020.12.034
UR  - https://juser.fz-juelich.de/record/889057
ER  -