MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.
BackgroundExcessive gestational weight gain (GWG) poses negative impact on mothers and their children. It is important to understand the modifiable lifestyle factors associated with excessive GWG during pregnancy to guide future public health practice.AimTo investigate the association between physical activity during pregnancy and GWG of Chinese urban pregnant women.MethodsA pregnant women cohort was established between 2005 and 2007 in Changzhou, China. Physical activity levels of pregnant women were assessed using pedometer in the 2nd and 3rd trimester, respectively. According to step counts, pregnant women were categorized into 4 different physical activity groups: Sedentary, Low Active, Somewhat Active and Active. The pregnant women were followed for eligibility and data collection from the 2nd trimester to delivery. Multiple linear regression and multiple binary logistic model were applied to determine the association between physical activity and GWG.ResultsPhysical activity levels and GWG of 862 pregnant women were assessed, among them 473 (54.9%) experienced excessive GWG. The adjusted odds ratio (OR) was 0.59 (95%CI: 0.36 ~ 0.95) for excessive GWG in the Active group during the 2nd trimester and 0.66 (95%CI: 0.43 ~ 1.00) in the Somewhat Active group during the 3rd trimester, compared with the Sedentary group respectively. In the last two trimesters, the Active group had 1.45 kg less GWG, than the Sedentary group. The ORs of excessive GWG decreased with the increased level of physical activity (P < 0.05).ConclusionThis study suggests that pregnant women being physically active have less weight gain during pregnancy.
Peroxisome proliferator-activated receptors (PPARs) act as metabolic sensors and central regulators of fat and glucose homeostasis. Furthermore, PPARγ has been implicated as major catabolic regulator of bone mass in mice and humans. However, a potential involvement of other PPAR subtypes in the regulation of bone homeostasis has remained elusive. Here we report a previously unrecognized role of PPARβ/δ as a key regulator of bone turnover and the crosstalk between osteoblasts and osteoclasts. In contrast to activation of PPARγ, activation of PPARβ/δ amplified Wnt-dependent and β-catenin-dependent signaling and gene expression in osteoblasts, resulting in increased expression of osteoprotegerin (OPG) and attenuation of osteoblast-mediated osteoclastogenesis. Accordingly, PPARβ/δ-deficient mice had lower Wnt signaling activity, lower serum concentrations of OPG, higher numbers of osteoclasts and osteopenia. Pharmacological activation of PPARβ/δ in a mouse model of postmenopausal osteoporosis led to normalization of the altered ratio of tumor necrosis factor superfamily, member 11 (RANKL, also called TNFSF11) to OPG, a rebalancing of bone turnover and the restoration of normal bone density. Our findings identify PPARβ/δ as a promising target for an alternative approach in the treatment of osteoporosis and related diseases.
Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.
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