The biocatalytic oxidative deamination of β-amino alcohols holds significant practical potential in kinetic resolution and/or deracemization process to access (R)-β-amino alcohols. This study exemplifies a notable instance of acquisition and utilization of this valuable oxidative deamination activity. Initially, the mutation N261M (M0) was identified to endow a native valine dehydrogenase with oxidative deamination activity toward a few (S)-β-amino alcohols. Subsequently, a phylogenetic analysis-guided, double-code saturation mutagenesis strategy was proposed to engineer M0's side-chain binding site. This strategy facilitated the substrate-specific evolution of M0, resulting in the creation of a panel of mutants (M1-M4) with noteworthy oxidative deamination activity toward structurally diverse (S)-β-amino alcohols. Using these engineered amine dehydrogenases, termed as β-amino alcohol dehydrogenases (β-AADHs), the complete kinetic resolution and even deracemization of a range of β-amino alcohols have been achieved. This work reports distinct biocatalysts and a synthetic strategy for the synthesis of enantiopure (R)-β-amino alcohols and offers an innovative approach for substrate-specificity engineering of enzymes.

This paper investigates the reconstruction of Van Gogh’s drawings which have been degraded in the course of time due to aging problems, like ink fading and discoloration. Learning to predict the past and original appearances of degraded drawings can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we use reproductions as reference information for the past appearances of drawings and consider the reconstruction of drawings as a pixel-wise prediction problem. We present an approach to automatically predict the past appearances of drawings. This approach brings together methods from multi-resolution image analysis and deep convolutional neural networks (CNNs) for addressing the task of pixel-wise prediction. Our experiments first investigate how scale affects prediction performance of the proposed multi-scale CNN framework and then demonstrate the reconstruction capability of the multi-scale CNN framework. The results demonstrate that the predictive reconstruction of degraded images is a feasible endeavor.