ArticleComputational and structural biotechnology journal2021
Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- Exploring sequence- and structure-based fitness landscapes to enhance thermal resistance and activity of endoglucanase II with minimal experimental effort.RSC chemical biology · 2025Article
- Fungal cellulases: protein engineering and post-translational modifications.Applied microbiology and biotechnology · 2022Review
- Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis.Computational and structural biotechnology journal · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
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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
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