Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases.

E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

Clinical and virological features of chronic hepatitis B in the French national surveillance program, 2008-2012: A cross-sectional study.

Background & Aims: Among people living with HBV, only a subset of individuals with chronic hepatitis is in need of treatment, and this proportion varies according to the population, region, and setting. No estimates of the proportion of people who are infected with HBV and meet the treatment eligibility criteria in France are available. Methods: 552 treatment-naïve individuals with chronic HBV infection referred for the first time to a hepatology reference centre between 2008 and 2012 were prospectively included. Demographic, clinical, and laboratory data were analysed. Results: In total, 61.1% of patients were males, with a median age of 37.5 years. Moreover, 64% were born in an intermediate- or high-HBV endemicity country, and 90% were HBeAg-negative. At referral, median HBV DNA and HBsAg levels were 3.3 and 3.6 log IU/ml, respectively; 37.8% of patients had alanine aminotransferase >40 U/L, and 29.0% had moderate or severe fibrosis (≥F2), including 9.4% with cirrhosis. The most prevalent genotypes were D (34.7%), E (27.4%), and A (25.7%). Coinfections were rare: 2.4% were HIV-positive, 4.0% were HCV-positive, and 6.0% were HDV-positive. According to the 2017 EASL Clinical Practice Guidelines, using a single time point analysis, 2.7% of patients were classified as HBeAg-positive chronic infection, 6.1% as HBeAg-positive chronic hepatitis B, 26.5% as HBeAg-negative chronic hepatitis B, and 61.1% as HBeAg-negative chronic infection, whereas 3.6% patients could not be classified. The performance of HBsAg level quantification to identify individuals with HBeAg-negative chronic hepatitis B was poor. A total of 29.1% met the criteria for initiation of antiviral treatment, whereas 66.5% remained under routine clinical surveillance. Most eligible patients initiated recommended first-line therapies, including tenofovir (45.3%), entecavir (36.8%), or pegylated interferon alpha (11.6%). Conclusions: Of all cases, 9.4% had cirrhosis at presentation and 29.1% met the 2017 EASL Clinical Practice Guidelines treatment criteria. HBsAg levels failed to accurately identify individuals with HBeAg-negative chronic infection. Lay summary: Among French adults chronically infected with HBV referred for the first time to hepatology reference centres, about one-third had a significant liver disease. Approximately one-third of individuals met criteria for initiation of antiviral treatment based on entecavir or tenofovir or, occasionally, pegylated interferon alpha.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, ß-secretase, monoamine oxidase B, glycogen synthase kinase 3ß, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.