(±)-hypermonanones A-G, seven pairs of monoterpenoid polyprenylated acylphloroglucinol enantiomers from Hypericum monanthemum.

Seven pairs of undescribed monoterpenoid polyprenylated acylphloroglucinol enantiomers [(±)-hypermonanones A-G (1-7)], together with three known analogues, were identified from the whole plant of Hypericum monanthemum Hook. The structures of these compounds were determined by analyses of their UV, HRESIMS, 1D/2D NMR spectroscopic data, and NMR calculations. The absolute configurations of these compounds were assigned by ECD calculations after chiral HPLC separation. Diverse monoterpene moieties were fused at C-3/C-4 of the dearomatized acylphloroglucinol core, which led to 3,4-dihydro-2H-pyran-integrated angular or linear type 6/6/6 tricyclic skeletons in 1-7. Compounds (-)-2 and (+)-2 exhibited significant NO inhibitory activity against LPS induced RAW264.7 cells with the IC50 values of 7.07 ± 1.02 µM and 11.39 ± 0.24 µM, respectively.

Hyperxylones A and B, two polycyclic polyprenylated acylphloroglucinols with a benzoyl substituted bicyclo[3.2.1]octane core from Hypericum beanii.

Two new polycyclic polyprenylated acylphloroglucinols (PPAPs) possessing a rare benzoyl substituted bicyclo[3.2.1]octane core, hyperxylones A (1) and B (2), along with three new dearomatized isoprenylated acylphloroglucinols (DIAPs), hyperxylones C - E (3-5), were isolated from the roots of Hypericum beanii. The structures of 1-5 were determined by high-resolution electrospray ionization mass spectroscopy (HRESIMS) and 1D/2D nuclear magnetic resonance (NMR) spectroscopic analyses, gauge-independent atomic orbital (GIAO) NMR calculations, and electronic circular dichroism (ECD) calculations. Compounds 1 and 2 were biomimetically semi-synthesized starting from 5 and 4, respectively, enabling the correct stereochemical assignment of 5 and 4. Moreover, compounds 1 and 2 showed anti-nonalcoholic steatohepatitis (NASH) activity by inhibiting lipid deposition in L02 cells; compounds 3 and 5 exhibited nitric oxide (NO) inhibitory activity in lipopolysaccharides (LPS)-induced RAW264.7 cells.

Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC.

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB.