Many pathogenic bacteria that infect humans, animals and plants rely on a quorum-sensing (QS) system to produce virulence factors. N-Acyl homoserine lactones (AHLs) are the best-characterized cell-cell communication signals in QS. The concentration of AHL plays a key role in regulating the virulence-gene expression and essential biological functions of pathogenic bacteria. N-Acyl homoserine lactonases (AHL-lactonases) have important functions in decreasing pathogenicity by degrading AHLs. Here, structures of the AHL-lactonase from Ochrobactrum sp. (AidH) in complex with N-hexanoyl homoserine lactone, N-hexanoyl homoserine and N-butanoyl homoserine are reported. The high-resolution structures together with biochemical analyses reveal convincing details of AHL degradation. No metal ion is bound in the active site, which is different from other AHL-lactonases, which have a dual Lewis acid catalysis mechanism. AidH contains a substrate-binding tunnel between the core domain and the cap domain. The conformation of the tunnel entrance varies with the AHL acyl-chain length, which contributes to the binding promiscuity of AHL molecules in the active site. It also supports the biochemical result that AidH is a broad catalytic spectrum AHL-lactonase. Taken together, the present results reveal the catalytic mechanism of the metal-independent AHL-lactonase, which is a typical acid-base covalent catalysis.

Measurement while drilling (MWD) data reflect the drilling rig–rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD data and rock mechanics parameters to support rock classification, most current models do not consider the impact of the continuous drilling-sequence process, thereby leading to rock-classification errors, while small and unbalanced field datasets result in poor model performance. We propose a novel deep neural network model based on Bi-directional Long Short-Term Memory (BILSTM) to extract information-related sequences in MWD data and improve the accuracy of the rock-mass classification. Two optimization modules were designed to improve the model’s generalization performance. Stratified K-fold cross-validation was used for model optimization in small and unbalanced datasets. Model validation is based on the MWD dataset of a highway tunnel in Yunnan, China. Multiple metrics show that the prediction ability of the network is significantly better than those of a multilayer perceptron (MLP) and a support-vector machine (SVM), while the model exhibits an improved generalization performance. The accuracy of the network can reach 90%, which is 13% and 15% higher than the MLP and SVM, respectively.

The crystal structure of desheptapeptide (B24–B30) insulin (DHPI), a virtually inactive analog of insulin, was determined at 1.6 Å resolution. In the refined structure model, DHPI retains three α-helices (A1–A8, A12–A18, and B9–B19) as its structural framework, while great conformational changes occur in the N and C termini of B-chain. The β-turn, which lies in B20–B30 in insulin and insulin analogs with high potency, no longer exists in DHPI. Relative motion is observed among the three α-helices, each as a rigid functional group. In contrast, a region covering B5–B6 and A6–A11 exhibits a relatively stable conformation. We interpret our results as identifying: ( i ) the importance of β-turn in determining the receptor-binding potency of insulin and ( ii ) a leading role of Phe ^B24 in maintaining the β-turn structure.

The three-dimensional structure of a thermostable β-glycosidase (Gly _Tn ) from the thermophilic eubacterium Thermus nonproteolyticus HG102 was determined at a resolution of 2.4 Å. The core of the structure adopts the (βα) ₈ barrel fold. The sequence alignments and the positions of the two Glu residues in the active center indicate that Gly _Tn belongs to the glycosyl hydrolases of retaining family 1. We have analyzed the structural features of Gly _Tn related to the thermostability and compared its structure with those of other mesophilic glycosidases from plants, eubacteria, and hyperthermophilic enzymes from archaea. Several possible features contributing to the thermostability of Gly _Tn were elucidated.