Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

Exploiting cyclodextrins as artificial chaperones to enhance enzyme protection through supramolecular engineering

We report a method of enzyme stabilisation exploiting the artificial protein chaperone properties of β-cyclodextrin (β-CD) covalently embedded in an ultrathin organosilica layer. Putative interaction points of this artificial chaperone system with the surface of the selected enzyme were studied in silico using a protein energy landscape exploration simulation algorithm. We show that this enzyme shielding method allows for drastic enhancement of enzyme stability under thermal and chemical stress conditions, along with broadening the optimal temperature range of the biocatalyst. The presence of the β-CD macrocycle within the protective layer supports protein refolding after treatment with a surfactant.

Design and Characterization of In-One Protease-Esterase PluriZyme

Proteases are abundant in prokaryotic genomes (~10 per genome), but their recovery encounters expression problems, as only 1% can be produced at high levels; this value differs from that of similarly abundant esterases (1-15 per genome), 50% of which can be expressed at good levels. Here, we design a catalytically efficient artificial protease that can be easily produced. The PluriZyme EH1AB1 with two active sites supporting the esterase activity was employed. A Leu24Cys mutation in EH1AB1, remodelled one of the esterase sites into a proteolytic one through the incorporation of a catalytic dyad (Cys24 and His214). The resulting artificial enzyme, EH1AB1C, efficiently hydrolysed (azo)casein at pH 6.5-8.0 and 60-70 °C. The presence of both esterase and protease activities in the same scaffold allowed the one-pot cascade synthesis (55.0 ± 0.6% conversion, 24 h) of L-histidine methyl ester from the dipeptide L-carnosine in the presence of methanol. This study demonstrates that active sites supporting proteolytic activity can be artificially introduced into an esterase scaffold to design easy-to-produce in-one protease-esterase PluriZymes for cascade reactions, namely, the synthesis of amino acid esters from dipeptides. It is also possible to design artificial proteases with good production yields, in contrast to natural proteases that are difficult to express.

A Plurizyme with Transaminase and Hydrolase Activity Catalyzes Cascade Reactions

Engineering dual‐function single polypeptide catalysts with two abiotic or biotic catalytic entities (or combinations of both) supporting cascade reactions is becoming an important area of enzyme engineering and catalysis. Herein we present the development of a PluriZyme, TR₂E₂, with efficient native transaminase (k_cat: 69.49±1.77 min⁻¹) and artificial esterase (k_cat: 3908–0.41 min⁻¹) activities integrated into a single scaffold, and evaluate its utility in a cascade reaction. TR₂E₂ (pH_opt: 8.0–9.5; T_opt: 60–65 °C) efficiently converts methyl 3‐oxo‐4‐(2,4,5‐trifluorophenyl)butanoate into 3‐(R)‐amino‐4‐(2,4,5‐trifluorophenyl)butanoic acid, a crucial intermediate for the synthesis of antidiabetic drugs. The reaction proceeds through the conversion of the β‐keto ester into the β‐keto acid at the hydrolytic site and subsequently into the β‐amino acid (e.e. >99 %) at the transaminase site. The catalytic power of the TR₂E₂PluriZyme was proven with a set of β‐keto esters, demonstrating the potential of such designs to address bioinspired cascade reactions.

Carbohydrate-Binding Modules of Potential Resources: Occurrence in Nature, Function, and Application in Fiber Recognition and Treatment

Great interests have recently been aroused in the independent associative domain of glycoside hydrolases that utilize insoluble polysaccharides-carbohydrate-binding module (CBM), which responds to binding while the catalytic domain reacts with the substrate. In this mini-review, we first provide a brief introduction on CBM and its subtypes including the classifications, potential sources, structures, and functions. Afterward, the applications of CBMs in substrate recognition based on different types of CBMs have been reviewed. Additionally, the progress of CBMs in paper industry as a new type of environmentally friendly auxiliary agent for fiber treatment is summarized. At last, other applications of CBMs and the future outlook have prospected. Due to the specificity in substrate recognition and diversity in structures, CBM can be a prosperous and promising ‘tool’ for wood and fiber processing in the future.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.