Aspartase from Escherichia coli (AspA) catalyzes the direct conversion of acrylic acid to β-alanine; however, its substrate specificity and low catalytic efficiency limit its broader application. We engineered an AspA mutant capable of efficiently catalyzing the amination of acrylic acid for β-alanine synthesis, using Rosetta Enzyme Design to computationally redesign the Cβ-binding region of the acrylic acid binding site in AspA. Based on energy scores, structural configurations, and hydrogen bonding networks, 51 candidate variants with penalty scores below 30 were selected for mutant construction and performance testing; >70% of these variants exhibited enhanced catalytic activity in acrylic acid’s hydrogen amination. Four mutants achieved over 3-fold improved activity. The optimal mutant, M1 (T190I-M324I-K327L-N329C), demonstrated a 7.3-fold increased specific enzyme activity and a 13.0-fold improved kcat/Km compared with the wild type. Conformational changes in the S-loop and enhanced hydrophobic interactions near the active site contributed significantly to M1’s enhanced activity. Upon reaction optimization, the conversion of β-alanine synthesis using M1 in whole-cell catalysis increased from 5% with the wild type to 90% with M1. This study provides a reference for the biocatalytic synthesis of β-alanine, significantly enhancing the conversion of acrylic acid and demonstrating the enzyme’s potential for industrial applications.
Long-Xian Li, Baodi Ma, Yi Xu· Catalysts· 0 citations
Classical likelihood-ratio tests and $\Delta$AIC exacerbate the statistical significance crisis by scaling with sample size, often flagging negligible improvements as highly significant. While causal estimands like the average treatment effect (ATE) quantify practical magnitude, their reliance on the expectation operator ties them to the data's original coordinate scale. Furthermore, existing pseudo-$R^2$ metrics are inadequate: variance-based measures ignore higher-order distributional changes, and current formulations lack invariance to monotone transformations. We resolve these limitations by introducing Entropic Variance (EV) as a rigorous, scale-independent generalization of error variance in ordinary least squares. We define the population EV-based parameter, $\rho^2_V$, which projects unbounded cross-entropy onto a standardized $[0,1]$ scale, and establish that the EV-based $F_\text{V}$ statistic asymptotically follows an $F$-distribution. Building on these distributional properties, we propose two estimators: the empirical population $R^2_{\text{SV}}$ and the out-of-sample predictive $R^2_{\text{SVP}}$. Both are derived by exponentiating per-observation cross-entropy and incorporate a degrees-of-freedom correction for training optimism. Leveraging the $F_\text{V}$-distribution, we derive refined $p$-values and confidence intervals for $\rho^2_V$ without requiring intractable Fisher information matrices. Simulation studies and a Parkinson's disease microbiome application demonstrate the superiority of variable selection via these EV-$R^2$ metrics. Notably, evaluating the $R^2_{\text{SVP}}$ of a LASSO path via data-splitting reduced false discovery rates from 80% to 6% in simulations while fully preserving signal recall.
