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| Hauptseite > Publikationsdatenbank > Critical Assessment of Structure-based Approaches to Improve Protein Resistance in Aqueous Ionic Liquids by Enzyme-wide Saturation Mutagenesis |
| Hauptseite > Publikationsdatenbank > Critical Assessment of Structure-based Approaches to Improve Protein Resistance in Aqueous Ionic Liquids by Enzyme-wide Saturation Mutagenesis |
| Journal Article | FZJ-2021-05353 |
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2022
Research Network of Computational and Structural Biotechnology (RNCSB)
Gotenburg
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Please use a persistent id in citations: http://hdl.handle.net/2128/29989 doi:10.1016/j.csbj.2021.12.018
Abstract: Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of BsLipA screened against four aIL. We show that, surprisingly, most of the approaches yield low gain in precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant positions up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.
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