Journal Article

FZJ-2021-00001

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Can Constraint Network Analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Contreras, F. ; Nutschel, C.FZJ* ; Beust, L. ; Davari, M. D. ; Gohlke, H. (Corresponding author)FZJ* ; Schwaneberg, U. (Corresponding author)

2021
Research Network of Computational and Structural Biotechnology (RNCSB) Gotenburg

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Please use a persistent id in citations: http://hdl.handle.net/2128/26996  doi:10.1016/j.csbj.2020.12.034

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Strukturbiochemie (IBI-7)
3. John von Neumann - Institut für Computing (NIC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. Forschergruppe Gohlke (hkf7_20200501) (hkf7_20200501)

Appears in the scientific report 2021

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Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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