Phosphorylation Mechanism of N-Acetyl-l-glutamate Kinase, a QM/MM Study

The priming phosphorylation of KaiC is activated by the release of its autokinase autoinhibition

KaiC, a cyanobacterial circadian clock protein with autokinase activity, catalyzes the dual phosphorylation of its own S431 and T432 residues in a circadian manner in the presence of KaiA and KaiB. Priming phosphorylation at T432 is a key step that promotes secondary phosphorylation at S431. Although KaiA binding is considered essential for KaiC phosphorylation, the mechanisms underlying the activation and inactivation of priming phosphorylation remain elusive. We found that although the priming phosphorylation is autoinhibited within KaiC, it actually proceeds at a rate constant of 0.019 h-1 even in the absence of KaiA. The autoinhibition of KaiC and the mechanism underlying the release from autoinhibition by KaiA were examined by KaiC structural analysis and by classical molecular dynamics and quantum mechanics/molecular mechanics simulations. We found that the side chain of T432 adopts two rotamers in dephosphorylated KaiC, one of which places T432 in a position suitable for a nucleophilic attack on the terminal phosphate of adenosine triphosphate. However, the nucleophilicity of T432 was insufficient to overcome an energy barrier of ∼21 kcal mol-1 because the catalytic function of a nearby base, E318, was self-suppressed by hydrogen bonding to positively charged R385. Biochemical assays of KaiC mutants showed that the autoinhibition of KaiC autokinase activity is attenuated by conferring T432 high nucleophilicity through the KaiA-assisted release of R385 from E318 to E352. During the circadian cycle, R385 switches interacting partners to inactivate/activate the autokinase function and to ensure the unidirectionality of the KaiC phosphorylation cycle.

The possible inhibition mechanism of low salt dry-curing on volatile flavor deterioration in refrigerated grass carp (Ctenopharyngodon idella) blocks: Metabolomics and microorganisms

Complex microbial communities have an important impact on the flavor of low salt dry-curing (LSD)-pretreated grass carp blocks. Here, the flavors, metabolites, and bacterial diversity of LSD-pretreated fish during cold storage were analyzed using flavor analysis, metabolomics, and high-throughput sequencing to investigate their correlations in detail. LSD promoted the volatile flavor deterioration of grass carp blocks under 6 days of refrigeration but inhibited it under 15 days of refrigeration. Furthermore, 924 metabolites were identified in the refrigerated grass carp blocks, and LSD inhibited the growth of Psychrophilic dominant spoilage microorganisms (Proteobacteria) and promoted microbial abundance (Actinobacteriota, Firmicutes, Bacteroidota, and Cyanobacteria). Correlation analysis revealed that the degradation of phosphatidylcholine connected with the monomonas genus in LSD-pretreated fish blocks played a vital role in inhibiting the key volatile flavor (esters, aldehydes, and alcohols) deterioration. This information is useful for elucidating the inhibition mechanism of LSD on flavor deterioration in refrigerated fish blocks.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Mechanism of the feedback-inhibition resistance in aspartate kinase of Corynebacterium pekinense: from experiment to MD simulations

In microorganisms and plants, aspartate kinase (AK) is the initial committed enzyme of the biosynthesis of the aspartate acid family amino acids and is inhibited by end products. In the paper, we mutated the key allosteric regulatory site A380 around the binding site of the Lys inhibitor in Corynebacterium pekinense AK (CpAK). A single-mutant A380C was obtained with 12.35-fold higher enzyme activity through high-throughput screening. On this basis, T379 as another key allosteric regulatory site was further modified, and the double-mutant T379N/A380C with 22.79-fold higher enzyme activity was obtained. Molecular dynamics (MD) simulations were used to investigate the mechanism of allosteric inhibition by Lys. The results indicated that the binding of Lys with CpAK resulted in conformational changes and a larger distance between the phosphorus atom of ATP and the oxygen atom of Asp, which was detrimental for the catalytic reaction. However, the mutation of allosteric sites opens the "switch" of allosteric regulation and can prevent the conformational transformation. Some key residues such as G168, R203, and D193 play an important role in maintaining the substrate binding with CpAK and further enhance the enzyme activity.