title: Protein redesign by learning from data.
abstract: Protein redesign methods aim to improve a desired property by carefully selecting mutations in relevant regions guided by protein structure. However, often protein structural requirements underlying biological characteristics are not well understood. Here, we introduce a methodology that learns relevant mutations from a set of proteins that have the desired property and demonstrate it by successfully improving production levels of two enzymes by Aspergillus niger, a relevant host organism for industrial enzyme production. We validated our method on two enzymes, an esterase and an inulinase, creating four redesigns with 5-45 mutations. Up to 10-fold increase in production was obtained with preserved enzyme activity for small numbers of mutations, whereas production levels and activities dropped for too aggressive redesigns. Our results demonstrate the feasibility of protein redesign by learning. Such an approach has great potential for improving production levels of many industrial enzymes and could potentially be employed for other design goals.
authors.affiliation: Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands Netherlands Bioinformatics Centre, Nijmegen, The Netherlands Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands.
authors.forename: Bastiaan A
authors.initials: BA
authors.lastname: van den Berg
authors.affiliation: Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands Netherlands Bioinformatics Centre, Nijmegen, The Netherlands Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands.
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
authors.affiliation: DSM Biotechnology Center, Delft, The Netherlands.
authors.forename: Jan-Metske
authors.initials: JM
authors.lastname: van der Laan
authors.affiliation: Netherlands Bioinformatics Centre, Nijmegen, The Netherlands DSM Biotechnology Center, Delft, The Netherlands.
authors.forename: Johannes A
authors.initials: JA
authors.lastname: Roubos
authors.affiliation: Delft Bioinformatics Lab, Department of Intelligent Systems, Faculty Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands Netherlands Bioinformatics Centre, Nijmegen, The Netherlands Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands dick.deridder@wur.nl.
authors.forename: Dick
authors.initials: D
authors.lastname: de Ridder
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics; D001234:Aspergillus niger / Q000201:enzymology* / Q000235:genetics; D003001:Cloning, Molecular / Q000379:methods; D019020:Directed Molecular Evolution / Q000379:methods*; D004950:Esterases / Q000138:chemical synthesis* / Q000235:genetics; D005656:Fungal Proteins / Q000138:chemical synthesis* / Q000235:genetics; D006026:Glycoside Hydrolases / Q000138:chemical synthesis* / Q000235:genetics; D017433:Protein Structure, Secondary
chemical_list: D005656:Fungal Proteins; D004950:Esterases; D006026:Glycoside Hydrolases; C023086:inulinase
keywords: enzyme production levels; protein redesign; redesign by learning