Show simple item record

dc.contributor.authorRisso, Valeria Alejandra 
dc.contributor.authorGutiérrez Rus, Luis Ignacio 
dc.contributor.authorOrtega Muñoz, Mariano 
dc.contributor.authorSantoyo González, Francisco 
dc.contributor.authorGavira Gallardo, José Antonio 
dc.contributor.authorSánchez Ruiz, José Manuel 
dc.date.accessioned2020-11-06T10:44:07Z
dc.date.available2020-11-06T10:44:07Z
dc.date.issued2020-06-28
dc.identifier.citationRisso, V., Romero-Rivera, A., Gutierrez-Rus, L. I., Ortega-Muñoz, M., Santoyo-Gonzalez, F., Gavira, J. A., ... & Kamerlin, S. C. L. (2020). Enhancing a De Novo Enzyme Activity by Computationally-Focused Ultra-Low-Throughput Screening. Chemical Science. [DOI: 10.1039/d0sc01935f]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64102
dc.description.abstractDirected evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers ( 2 104 M 1 s 1 and 102 s 1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within 2 kcal mol 1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stabilityguidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme designes_ES
dc.description.sponsorshipKnut and Alice Wallenberg Foundation (Wallenberg Academy Fellowship) 2018.0140es_ES
dc.description.sponsorshipHuman Frontier Science Program RGP0041/2017es_ES
dc.description.sponsorshipFEDER Funds/Spanish Ministry of Science, Innovation and Universities BIO2015-66426-R RTI2018-097142-B-100es_ES
dc.description.sponsorshipFEDER/Junta de Andalucia - Consejeria de Economia y Conocimiento E.FQM.113.UGR18es_ES
dc.description.sponsorshipSwedish National Infrastructure for computing (SNAC) 2018/2-3 2019/2-1es_ES
dc.language.isoenges_ES
dc.publisherRoyal Society Chemistryes_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMolecular dynamics es_ES
dc.subjectDirected evolutiones_ES
dc.subjectKemp eliminationes_ES
dc.subjectEfficient catalysises_ES
dc.subjectPotential Functionses_ES
dc.subjectForce fieldes_ES
dc.subjectFree energyes_ES
dc.subjectDesign es_ES
dc.subjectProteins es_ES
dc.subjectOptimizationes_ES
dc.titleEnhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screeninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1039/d0sc01935f
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


Files in this item

[PDF]

This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License