Home > Publications database > Critical Assessment of Structure-based Approaches to Improve Protein Resistance in Aqueous Ionic Liquids by Enzyme-wide Saturation Mutagenesis > print

001     903713
005     20230128125810.0
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100 1 _ |a El Harrar, Till
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245 _ _ |a Critical Assessment of Structure-based Approaches to Improve Protein Resistance in Aqueous Ionic Liquids by Enzyme-wide Saturation Mutagenesis
260 _ _ |a Gotenburg
|c 2022
|b Research Network of Computational and Structural Biotechnology (RNCSB)
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520 _ _ |a 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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700 1 _ |a Davari, Mehdi D.
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700 1 _ |a Jaeger, Karl-Erich
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700 1 _ |a Schwaneberg, Ulrich
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700 1 _ |a Gohlke, Holger
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773 _ _ |a 10.1016/j.csbj.2021.12.018
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