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.

Endoglucanase effects on energy consumption in mechanical fibrillation of cellulose fibers into nanocelluloses

Enzymatic processing is seen as a promising means of moving toward environment-friendly industrial processes such as the use of endoglucanase (EG) enzyme in the production of nanocellulose. However, the properties that make EG pretreatment effective in the isolation of fibrillated cellulose remain a subject of debate. To address this issue, we considered EGs from four glycosyl hydrolase (GH) families (5, 6, 7 and 12) and investigated the roles of the tri-dimensional structures and catalytic features depending on the presence of a carbohydrate binding module (CBM). By using eucalyptus Kraft wood fibers, we produced cellulose nanofibrils (CNF) using mild enzymatic pretreatment followed by disc ultra-refining. Compared with the control (in the absence of pretreatment), the GH5 and GH12 enzymes (CBM free) reduced the fibrillation energy by approximately 15 %. The most efficient energy reduction, 25 and 32 %, was achieved with GH5 and GH6 linked to CBM, respectively. They improved the rheological properties of the CNF suspensions (noting that neither of these EGs released soluble products). Interestingly, while the hydrolytic activity was significant (released soluble products), GH7-CBM did not lead to a reduction in fibrillation energy. Hence, the large molecular weight and wide cleft of GH7-CBM led to soluble sugar release but contributed little to fibrillation. Our findings suggest that the improved fibrillation observed upon EG pretreatment is not a consequence of hydrolytic activity or release of products but mostly related to efficient adsorption on the substrate and modification of the surface viscoelastic (amorphogenesis).

Rational enzyme design for controlled functionalization of acetylated xylan for cell-free polymer biosynthesis

Xylan O-acetyltransferase 1 (XOAT1) is involved in O-acetylating the backbone of hemicellulose xylan. Recent structural analysis of XOAT1 showed two unequal lobes forming a cleft that is predicted to accommodate and position xylan acceptors into proximity with the catalytic triad. Here, we used docking and molecular dynamics simulations to investigate the optimal orientation of xylan in the binding cleft of XOAT1 and identify putative key residues (Gln445 and Arg444 on Minor lobe & Asn312, Met311 and Asp403 on Major lobe) involved in substrate interactions. Site-directed mutagenesis coupled with biochemical analyses revealed the major lobe of XOAT1 is important for xylan binding. Mutation of single key residues yielded XOAT1 variants with various enzymatic efficiencies that are applicable to one-pot synthesis of xylan polymers with different degrees of O-acetylation. Taken together, our results demonstrate the effectiveness of computational modeling in guiding enzyme engineering aimed at modulating xylan and redesigning plant cell walls.

A ratiometric fluorescent assay for evaluation of alkaline phosphatase activity based on ionic liquid-functionalized carbon dots

A ratiometric fluorescent assay is fabricated for the evaluation of alkaline phosphatase (ALP) activity. This assay is composed of ionic liquid-functionalized carbon dots (IL-CDs) with blue fluorescence signal at 470 nm and 2,3-diaminophenazine (DAP) with yellow fluorescence signal at 570 nm. IL-CDs were synthesized via electrochemical method by using ionic liquid (1-butyl-3-methylimidazolium tetrafluoroborate) and ultrapure water as precursors. DAP is produced by the oxidation reaction between o-phenylenediamine and H₂O₂ under the catalysis of horseradish peroxidase. H₂O₂ is reduced by ascorbic acid which is the hydrolysis product of ascorbic acid 2-phosphate under the catalysis of ALP, finally reducing the amount of DAP. The activity of ALP is evaluated through the ratiometric fluorescent signal between IL-CDs and DAP via Förster resonance energy transfer. Under optimal experimental conditions, this ratiometric fluorescent assay has a response that covers the 0.04 to 3.2 U L^-1 (12 to 960 pM) ALP activity. This assay possesses ultralow detection limit of 0.012 U L^-1 (3.6 pM) for ALP and high selectivity for ALP among several enzymes. The method was used to measure ALP activity in human serum samples with satisfying results. Graphical abstract Schematic presentation of IL-CDs-based ratiometric fluorescent assay for ALP activity evaluation via FRET strategy between IL-CDs and DAP. This ratiometric fluorescent assay possessed low detection limit of ALP activity (0.012 U L^-1) and high selectivity among several enzymes.

Evolution of inhibitor-resistant natural mutant forms of HIV-1 protease probed by pre-steady state kinetic analysis

Pre-steady state kinetic analysis of mechanistic features of substrate binding and processing is crucial for insight into the evolution of inhibitor-resistant forms of HIV-1 protease. These data may provide a correct vector for rational drug design assuming possible intrinsic dynamic effects. These data should also give some clues to the molecular mechanism of protease action and resistance to inhibitors. Here we report pre-steady state kinetics of the interaction of wild type or mutant forms of HIV-1 protease with a FRET-labeled peptide. The three-stage "minimal" kinetic scheme with first and second reversible steps of substrate binding and with following irreversible peptide cleavage step adequately described experimental data. For the first time, a set of "elementary" kinetic parameters of wild type HIV-1 protease and its natural mutant inhibitor-resistant forms MDR-HM, ANAM-11 and prDRV4 were compared. Inhibitors of the first and second generation were used to estimate the inhibitory effects on HIV-1 protease activity. The resulting set of kinetic data supported that the mutant forms are kinetically unaffected by inhibitors of the first generation, proving their functional resistance to these compounds. The second generation inhibitor darunavir inhibited mutant forms MDR-HM and ANAM-11, but was ineffective against prDRV4. Our kinetic data revealed that these inhibitors induced different conformational changes in the enzyme and, thereby they have different mode of binding in the enzyme active site. These data confirmed hypothesis that the driving force of the inhibitor-resistance evolution is disruption of enzyme-inhibitor complex by changing of the contact network in the inhibitor binding site.

A highly Conserved Aspartic Acid Residue of the Chitosanase from Bacillus Sp. TS Is Involved in the Substrate Binding

The chitosanase from Bacillus sp. TS (CsnTS) is an enzyme belonging to the glycoside hydrolase family 8. The sequence of CsnTS shares 98 % identity with the chitosanase from Bacillus sp. K17. Crystallography analysis and site-direct mutagenesis of the chitosanase from Bacillus sp. K17 identified the important residues involved in the catalytic interaction and substrate binding. However, despite progress in understanding the catalytic mechanism of the chitosanase from the family GH8, the functional roles of some residues that are highly conserved throughout this family have not been fully elucidated. This study focused on one of these residues, i.e., the aspartic acid residue at position 318. We found that apart from asparagine, mutation of Asp318 resulted in significant loss of enzyme activity. In-depth investigations showed that mutation of this residue not only impaired enzymatic activity but also affected substrate binding. Taken together, our results showed that Asp318 plays an important role in CsnTS activity.