BERT-PPII: The Polyproline Type II Helix Structure Prediction Model Based on BERT and Multichannel CNN.

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's CLS vector can fairly fuse sample's each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample's global contextual information. As the interactions among the protein chains' local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues' features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix.

Quercetin improves high-fat diet-induced obesity by modulating gut microbiota and metabolites in C57BL/6J mice.

High-fat diet-induced obesity is characterized by low-grade inflammation, which has been linked to gut microbiota dysbiosis. We hypothesized that quercetin supplementation would alter gut microbiota and reduce inflammation in obese mice. Male C57BL/6J mice, 4 weeks of age, were divided into 3 groups, including a low-fat diet group, a high-fat diet (HFD) group, and a high-fat diet plus quercetin (HFD+Q) group. The mice in HFD+Q group were given 50 mg per kg BW quercetin by gavage for 20 weeks. The body weight, fat accumulation, gut barrier function, glucose tolerance, and adipose tissue inflammation were determined in mice. 16 s rRNA amplicon sequence and non-targeted metabolomics analysis were used to explore the alteration of gut microbiota and metabolites. We found that quercetin significantly alleviated HFD-induced obesity, improved glucose tolerance, recovered gut barrier function, and reduced adipose tissue inflammation. Moreover, quercetin ameliorated HFD-induced gut microbiota disorder by regulating the abundance of gut microbiota, such as Adlercreutzia, Allobaculum, Coprococcus_1, Lactococcus, and Akkermansia. Quercetin influenced the production of metabolites that were linked to alterations in obesity-related inflammation and oxidative stress, such as Glycerophospho-N-palmitoyl ethanolamine, sanguisorbic acid dilactone, O-Phospho-L-serine, and P-benzoquinone. Our results demonstrate that the anti-obesity effects of quercetin may be mediated through regulation in gut microbiota and metabolites.