Polyketide synthases (PKSs) share a subset of biosynthetic steps in construction of a polyketide, and the offload from the PKS main module of specific product release is most often catalyzed by a thioesterase (TE). In spite of the fact that various PKS systems have been discovered in polyketide biosynthesis, the molecular basis of TE-catalyzed macrocyclization remains challenging. In this study, MD simulations and QM/MM methods were combined to investigate the catalytic mechanism and substrate diversity of pikromycin (PIK) TE with two systems (PIK-TE-1 and PIK-TE-2), where substrates 1 and 2 correspond to TE-catalyzed precursors of 10-deoxymethynolide and narbonolide, respectively. The results showed that, in comparison with PIK-TE-2, system PIK-TE-1 exhibited a greater tendency to form a stable prereaction state, which is critical to macrocyclization. In addition, the structural characteristics of prereaction states were uncovered through analyses of hydrogen-bonding and hydrophobic interactions, which were found to play a key role in substrate recognition and product release. Furthermore, potential energy surfaces were calculated to study the molecular mechanism of macrocyclization, including the formation of tetrahedral intermediates from re- and si-face nucleophilic attacks and the release of products. The energy barrier of macrocyclization from re-face attack was calculated to be 16.3 kcal/mol in PIK-TE-1, 3.6 kcal/mol lower than that from si-face attack and 4.1 kcal/mol lower than that from re-face attack in PIK-TE-2. These results are in agreement with experimental observations that the yield of 10-deoxymethynolide is superior to that of narbonolide in PIK TE catalyzed macrocyclization. Our findings elucidate the catalytic mechanism of PIK TE and provide a better understanding of type I PKS TEs in protein engineering.

The narrow substrate scope of naturally occurring alcohol dehydrogenases (ADHs) greatly limits the enzymatic synthesis of important chiral alcohols. On the basis of X-ray crystal structures and kinetic profiling of a substrate library, we engineered variants of the stereospecific alcohol dehydrogenase from Candida parapsilopsis. This resulted in a set of four mutant enzymes which enable the asymmetric reduction of a broad range of prochiral ketones, including valuable pharmaceuticals and fine chemicals. The engineering strategy of this study paves the way for creating additional ADHs tailored for production of complex chiral alcohols.

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The 2,5-furandicarboxylic acid (FDCA)-based aliphatic−aromatic copolyester is an intensively researched area of bio-based polymers with high gas barrier and mechanical properties. However, the contradiction between the barrier and degradation performance still remains a huge challenge and severely limits their applications. Here, we combine branched neopentyl glycol, hydrophilic diglycolic acid, and FDCA to prepare poly-(neopentyl glycol diglycolate/furandicarboxylate) (PNDF) copolyesters. With poor crystallization capability, PNDF40 and 50 (content of NF units) display low modulus (58 vs 108 MPa) but elastomeric behavior. Their tensile broken samples can even rapidly restore the original length, which might be derived from the physical crosslinking networks. It was interesting to find that even when more than 50% of the NF units were replaced by ND units, the PNDF copolyesters still retained high gas barrier. The introduction of diglycolic acid improved the hydrolysis rate, showing potential degradability under environmental conditions. However, enzymatic degradation using Candida antarctica lipase B (CALB) revealed that the branched neopentyl glycol decreased the biodegradation rate when compared with other linear diols. Furthermore, the hydrolytic pathway of PNDF was explored by density functional theory (DFT) calculation. Through Fukui function analysis, we identified the most active sites of PNDF for hydrolysis. Additionally, the calculated energy barrier indicated that hydrolysis of the polymer chain became easier with the increase in the number of ND units. Molecular dynamics (MD) simulations of PNDF−CALB illustrated that Val154 and Gln157 of CALB located at catalytic entrance formed noncovalent interaction with PNDF, which would sterically hinder the carbonyl carbon from reaching an ideal distance for nucleophilic attack and decrease the tendency to enter a pre-reaction state.

Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.