New Alpiniamide-Type Polyketide with Antibiofilm Activities from the Marine-Derived Streptomyces sp. ZS-A65.

Two new alpiniamide-type polyketides, alpiniamides H-I (1-2), in addition to four recognized compounds, were discovered in Streptomyces sp. ZSA65 derived from the marine sediments. The planar structure and absolute configuration of alpiniamides H-I were elucidated using a combination of 1D, 2D NMR, HRESIMS data analysis, Mosher's method and ECD calculations. The antibiofilm and antibacterial activities against P. aeruginosa were evaluated using the microdilution method. Notably, Compound 2 exhibited strong antibiofilm property.

Cytotoxic and Antifungal Staurosporine Derivatives from Marine-Derived Actinomycete Streptomyces sp. ZS-A121.

A novel staurosporine derivate, streptomholyrine A (1), along with 6 known compounds were identified from the rice-based solid fermentation of marine-derived Streptomyces sp. ZS-A121. The planar structure and absolute configuration of streptomholyrine A were elucidated using a combination of 1D, 2D NMR, HRESIMS data analysis, chemical transformation, ECD and NMR calculations. Screening of all these compounds revealed their cytotoxic activity against HCT-116 cell lines, with IC50 values ranging from 0.012 to 11.67 µM, except for the known 1H-indole-3-hydroxyacetyl, which showed no inhibition activity. Furthermore, streptomholyrine A, along with two known staurosporine derivatives, k252d and staurosporine, exhibited activities against Candida albicans, with MICs of 12.5, 25.0 and 50.0 µg/ml, respectively.

Structurally Diverse Metabolites from the Marine-Derived Streptomyces sp. DS-27 Based on Two Different Culture Conditions.

Nine new compounds, including streptothiomycin A-E (1-5), two cyclopentenones (6, 7), one α-pyrone (8), wailupemycin Q (20), along with sixteen known compounds were identified from a rhizosphere strain Streptomyces sp. DS-27 derived from the marine cordgrass Spartina alterniflora under two different culture conditions. All of the structures were elucidated by extensive analysis of 1D/2D NMR and HR-ESI-MS data. The absolute configurations were determined by NOESY analysis, ECD, specific rotation and GIAO NMR calculations, and DP4+ probability analysis. Bioactivity investigation showed that compounds 5 and 7 exhibited significant inhibitory effects on LPS-induced NO production in a dose-dependent manner, which indicates their anti-inflammatory potential.

Alpiniamides E-G from the Saline Lake-Derived Streptomyces sp. QHA48 and Their Lipid Accumulation Inhibitory Activity.

Alpiniamides E-G, three previously unreported linear polyketide derivatives, along with two known compounds, were isolated from Streptomyces sp. QHA48, which was isolated from the saline lakes of Qinghai-Tibet Plateau. The structures of these compounds were determined through analysis of their spectroscopic data, as well as density functional theory prediction of NMR chemical shifts, application of the DP4+ algorithm and electronic circular dichroism (ECD) calculations. In a cell-based lipid-lowering assay, all five alpiniamides exhibited significant inhibition of lipid accumulation in HepG2 cells without inducing cytotoxic effects at a concentration of 27 µM.

Two new di-tert-butyl-type compounds from a saline-lake derived Streptomyces sp. XZB42.

Two novel di-tert-butyl-type structures (1-2), and five known compounds (3-7) were isolated from the chemical investigations of a saline lake actinomycete, Streptomyces sp. XZB42. The structures of the new compounds were elucidated by extensive NMR spectroscopic analysis, HRESIMS data, GIAO (gauge-including atomic orbitals) NMR and specific optical rotation (SOR).

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods.

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.