Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Computational and Structural Biotechnology Journal, vol. 19, pp. 743–751

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.

Authors 6

1. Francisca Contreras Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany

2. Christina Nutschel

   Forschungszentrum Jülich · John von Neumann Institute for Computing · Jülich Supercomputing Centre

   Affiliation as printed

   John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC) and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

3. Laura Beust Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany

4. Mehdi D. Davari Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany

5. Holger Gohlke corresponding

   Forschungszentrum Jülich · John von Neumann Institute for Computing · Jülich Supercomputing Centre · Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany

   John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC) and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

6. Ulrich Schwaneberg corresponding Aachen DWI - Leibniz Institute for Interactive Materials Institute of Biotechnology

   DWI – Leibniz Institute for Interactive Materials · RWTH Aachen University

   Affiliation as printed

   DWI-Leibniz Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany