# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 3 Biosynthesis of NMN
### Protein Engineering of NMN Synthase  
Highly active and robust NMN synthases are critical for the efficient and stable production of NMN. In addition to direct screening, another effective approach involves using computer-aided protein engineering to modify key enzymes. This method leverages computer simulations to quickly identify promising mutants through accurate predictions. Bioinformatic models are capable of predicting the active site of enzymes as well as as sessing the impact of specific point mutations on enzyme stability, substrate affinity, and other functional aspects, facilitating precise enzyme modifications [63; 64]. [PERSON] et al. [65] developed a multienzyme cascade reaction and applied protein engineering to evolve crucial enzymes for the cost-effective in vitro synthesis of NMN from D-ribose, a readily available substrate. The research utilized tools, such as SWISS-MODEL and YASARA, for homology modeling and molecular docking of Cp-NAMPT, an essential enzyme in this pathway. Following established protocols, the team efficiently screened mutants using a high-throughput method based on substrate fluorescence [66]. The Cp-NAMPT mutant displayed a 57% increase in activity, significantly enhanced thermostability, and improved substrate binding compared to the wild-type enzyme. Substituting the wild type with the Cp-NAMPT-Y15S mutant increased the NMN titer from 3.96 g/L to 4.75 g/L. The targeted modifications of the key enzymes through protein engineering have markedly increased the efficiency of NMN production.  
[PERSON] et al. [67] discovered two novel highly active NRKs from _Sparassis crispa_ and _Beaureeria bassiana_, with respective specific activities of 6.14 and 6.66 U/mg. _Beaureeria bassiana_ NRK (_Bba_NRK) was chosen for structural modification. Alphafold2 was utilized to model the structure of the identified wild-type _Bba_NRK, while AutoDock Vina was employed for substrate docking analysis. A total of eleven mutants were constructed and assessed, among which V172K (M1) exhibited 20% higher enzyme activity than the wild type. Then, the PLIP website was employed to calculate and analyze the binding affinity. Compared to the WT, M1 had a deeper substrate channel, enabling a shorter distance for phosphate-group transfer between substrates and bringing the \(\gamma\)-phosphate group of ATP closer to NR. In M1, residues K172, N92, and S93 established additional hydrogen bonds with the substrate. Notably, S93 directly stabilized the phosphate group of ATP, while residues R164 and Y171 engaged in \(\pi\)-bonding interactions with the substrate. This mutation resulted in a more stable and efficient binding conformation, significantly enhancing the enzyme's catalytic efficiency. The lack of a solved NRK crystal structure poses challenges for 3D structural modeling and mutation site prediction. While directed evolution methods can modify proteins to some extent, they often lack precision and traceability. Deep learning and artificial intelligence (AI) techniques, trained on extensive sequence and structure datasets, have the potential to design protein molecules with entirely novel structures and molecular functions [68]. For instance, RFdiffusion, a protein design model grounded in diffusion models, can produce novel protein designs by iteratively refining protein structures while adhering to specific constraints [69]. Unlike traditional approaches, it does not rely on predefined templates or existing protein frameworks. Instead, it can directly generate desired protein structures starting from a random initial state, showcasing remarkable adaptability and innovation. Overall, AI models have significantly enhanced the success rate of de novo protein design, addressing crucial challenges, such as refining NRK structural nuances and ensuring precise complementarity with substrates.