Proteolysis-targeting chimeras (PROTACs), which selectively degrade targeted proteins by the ubiquitin-proteasome system, have emerged as a novel therapeutic technology with potential advantages over traditional inhibition strategies. In the past few years, this technology has achieved substantial progress and two PROTACs have been advanced into phase I clinical trials. However, this technology is still maturing and the design of PROTACs remains a great challenge. In order to promote the rational design of PROTACs, we present PROTAC-DB, a web-based open-access database that integrates structural information and experimental data of PROTACs. Currently, PROTAC-DB consists of 1662 PROTACs, 202 warheads (small molecules that target the proteins of interest), 65 E3 ligands (small molecules capable of recruiting E3 ligases) and 806 linkers, as well as their chemical structures, biological activities, and physicochemical properties. Except the biological activities of warheads and E3 ligands, PROTAC-DB also provides the degradation capacities, binding affinities and cellular activities for PROTACs. PROTAC-DB can be queried with two general searching approaches: text-based (target name, compound name or ID) and structure-based. In addition, for the convenience of users, a filtering tool for the searching results based on the physicochemical properties of compounds is also offered. PROTAC-DB is freely accessible at http://cadd.zju.edu.cn/protacdb/.

An association between cholecystectomy and metabolic syndrome has not been fully established. Here we analyzed the association between cholecystectomy and metabolic syndrome in a Chinese population of 5672 subjects who undergone annual health checkups at the First Affiliated Hospital, College of Medicine, Zhejiang University between January 2011 and December 2012. The prevalences of gallstones, cholecystectomy and metabolic syndrome were 6.0%, 3.6%, and 32.5%, respectively. The prevalence of metabolic syndrome was significantly higher in subjects with a history of cholecystectomy (63.5%) than in those with gallstones (47.0%) or in those without gallstone disease (30.3%; P<0.01 for both). Multivariate logistic regression analysis showed that cholecystectomy was significantly associated with increased risk of metabolic syndrome (OR = 1.872; 95% CI: 1.193–2.937). However, the association of gallstones with metabolic syndrome was not statistically significant (OR = 1.267; 95% CI: 0.901–1.782). Altogether, our results suggest that cholecystectomy significantly increases the risk of metabolic syndrome.

Our results showed that BMI was significantly and positively associated with H. pylori infection, and a high BMI was associated with an increased risk of the infection.

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We conducted a meta-analysis to assess the association between tumor necrosis factor-alpha (TNF-alpha) gene TNFA -308 (G>A), TNFA -238 (G>A), TNFA -857 (C>T), TNFA -863 (C>A), TNFA -1031 (T>C), TNFA -1210 (A>T) polymorphisms and breast cancer(BC) susceptibility. We also performed subgroup analyses based on ethnicity (Caucasian, Asian, and African). An extensive search was performed to identify all case-control studies investigating such association. Thirteen eligible studies, including 10,236 BC patients and 13,143 controls, were identified. No significant association was observed in all genotypes in worldwide populations, but stratification by ethnicity indicated that the TNFA -308 A allele was associated with a decreased risk of BC compared with the G allele in Caucasian individuals (OR = 0.927, 95%CI = 0.879-0.978). Similar results were obtained when the A/A +A/G genotype was compared with the G/G genotype. In addition, meta-analysis results indicated that the A/A genotype of TNFA -308 was a risk factor for BC in African (A/A vs. G/G OR = 4.085 95%CI = 1.460-11.425; A/A vs. G/A OR = 4.861 95%CI = 1.746-13.527; A/A vs. G/A + G/G OR = 4.246 95%CI = 1.551-11.625), but not in Caucasian or Asian individuals. In conclusion, the results of this meta-analysis indicate that the TNFA -308 A allele may be an important protective factor for BC in European individuals, but it is not likely to confer susceptibility to BC in worldwide populations. In addition, the AA genotype of TNFA -308 may be a risk factor for BC in African individuals. Besides, other polymorphisms were not associated with BC susceptibility.

Background/Aim. The risk factors for nonalcoholic fatty liver disease (NAFLD) in lean population have not been fully clarified. This study aimed to explore the association between uric acid to HDL-cholesterol ratio (UHR) and NAFLD in lean Chinese adults. Methods. A cross-sectional study was performed among 6285 lean Chinese adults (body mass index < 24 kg/m2) who took their annual health checkups. NAFLD was diagnosed based on hepatic ultrasound examination, with exclusion of other etiologies. Results. Of 6285 lean participants enrolled, 654 NAFLD cases were diagnosed. The overall NAFLD prevalence was 10.41%, and the prevalence was 15.45% and 7.16% in men and women, respectively. UHR was significantly higher in NAFLD patients than in controls (14.25 ± 5.33% versus 10.09 ± 4.23%, P<0.001). UHR quintiles were positively associated with NAFLD prevalence, which was 1.91% in the first UHR quintile and increased to 3.58%, 7.81%, 14.17%, and 24.54% in the second, third, fourth, and fifth quintile groups, respectively (P<0.001 for trend). Multivariate logistic regression analysis showed that UHR was independently associated with an increased risk of NAFLD (odds ratio: 1.105; 95% CI: 1.076–1.134; P<0.001). Sensitivity analysis showed that UHR remained significantly associated with NAFLD in lean participants with normal range of serum uric acid and HDL-cholesterol levels. Conclusions. UHR was significantly associated with NAFLD and may serve as a novel and reliable marker for NAFLD in lean adults.

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug-drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug-drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than noninhibitors. We then developed a series of quantitative structure-activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure–activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM &gt; XGBoost &gt; rbf-GPR &gt; Cubist &gt; GBM &gt; DNN &gt; RF &gt; pca-ANN &gt; MARS &gt; linear-GPR ≈ KNN &gt; linear-SVM ≈ PLSR &gt; CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.