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• W2773202714 endingPage "1096" @default.
• W2773202714 startingPage "1085" @default.
• W2773202714 abstract "Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger (AnEH). We investigate whether the influence a mutation has on enzyme selectivity can be accurately predicted through linear models, and whether prediction accuracy can be improved using higher-order counterparts. Comparing linear and polynomial degree = 2 models, mean Pearson coefficients (r) from $$50,{times },5$$ -fold cross-validation increase from 0.84 to 0.91 respectively. Equivalent models tested on interaction-minimised sequences achieve values of $$r=0.90$$ and $$r=0.93$$ . As expected, testing on a simulated control data set with no interactions results in no significant improvements from higher-order models. Additional experimentally derived AnEH mutants are tested with linear and polynomial degree = 2 models, with values increasing from $$r=0.51$$ to $$r=0.87$$ respectively. The study demonstrates that linear models perform well, however the representation of epistatic interactions in predictive models improves identification of selectivity-enhancing mutations. The improvement is attributed to higher-order kernel functions that represent epistatic interactions between residues." @default.
• W2773202714 created "2017-12-22" @default.
• W2773202714 creator A5022958013 @default.
• W2773202714 creator A5029588486 @default.
• W2773202714 creator A5045279138 @default.
• W2773202714 creator A5066959335 @default.
• W2773202714 date "2017-12-01" @default.
• W2773202714 modified "2023-10-11" @default.
• W2773202714 title "Learning epistatic interactions from sequence-activity data to predict enantioselectivity" @default.
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• W2773202714 doi "https://doi.org/10.1007/s10822-017-0090-x" @default.
• W2773202714 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29234997" @default.
• W2773202714 hasPublicationYear "2017" @default.
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