GWASdb: a database for human genetic variants identified by genome-wide association studies

Recent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10−3) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Statistical optimisation of process variables and large-scale production of Metarhizium rileyi (Ascomycetes: Hypocreales) microsclerotia in submerged fermentation

Microsclerotia (MS) formation was successfully induced in Metarhizium rileyi (Ascomycetes: Hypocreales) in liquid culture. To optimise the process variables of liquid fermentation, we first used a two-level fraction design to confirm the variables, including inoculum density, initial pH, shaker speed, and temperature, affecting M. rileyi MS production. Three variables were found to be important. Subsequently, a 23 full factorial central composite design (CCD) and response surface methodology were applied to ascertain the optimal level of each variable. A second-order polynomial was determined and shaker speed and inoculum density were found to be the primary variables affecting MS yields. Finally, we realised and optimised M. rileyi MS submerged fermentation based on previous findings. A maximum MS yields (3.84 × 104 MS/mL) were recorded in submerged fermentation at an initial pH of 5.5, growth temperature of 26°C, inoculum density of 10%, higher aeration rate (150 rpm in the initial 3 days and 200 rpm in the subsequent 3 days), and higher agitation rate of 800 L/h sterile air

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

ABSTRACTCo-infection between HIV-1 and HCV is common today in certain populations. However, treatment of co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-validation and test set validation was performed to confirm the performance of 60 classification models. Our results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment.</jats:p

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug&ndash;drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Na&iuml;ve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure&ndash;activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.</jats:p