Photocatalytic regeneration of nicotinamide cofactor biomimetics drives biocatalytic reduction by Old Yellow enzymes.

A key approach in developing green chemistry involves converting solar energy into chemical energy of biomolecules through photocatalysis. Photocatalysis can facilitate the regeneration of nicotinamide cofactors during redox processes. Nicotinamide cofactor biomimetics (NCBs) are economical substitutes for natural cofactors. Here, photocatalytic regeneration of NADH and reduced NCBs (NCBsred) using graphitic carbon nitride (g-C3N4) was developed. The process involves g-C3N4 as the photocatalyst, Cp*Rh(bpy)H2O2+ as the electron mediator, and Triethanolamine as the electron donor, facilitating the reduction of NAD+ and various oxidative NCBs (NCBsox) under light irradiation. Notably, the highest reduction yield of 48.32 % was achieved with BANA+, outperforming the natural cofactor NAD+. Electrochemical analysis reveals that the reduction efficiency and capacity of cofactors relies on their redox potentials. Additionally, a coupled photo-enzymatic catalysis system was explored for the reduction of 4-Ketoisophorone by Old Yellow Enzyme XenA. Among all the NCBsox and NAD+, the highest conversion ratio of over 99 % was obtained with BANA+. After recycled for 8 times, g-C3N4 maintained over 93.6 % catalytic efficiency. The photocatalytic cofactor regeneration showcases its outstanding performance with NAD+ as well as NCBsox. This work significantly advances the development of photocatalytic cofactor regeneration for artificial cofactors and its potential application.

Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey.

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.