Django's Sites Framework is a contributed module bundled with the core library that allows for the use of a single Django application/codebase with different sites (that can use different databases, logic in views, etc). The SITE_ID setting, as stated in the docs, "is used so that application data can hook into specific sites and a single database can manage content for multiple sites."
In this particular case AllAuth requires the Sites Framework in order to function properly. Many other third-party libraries are built to safely handle cases where multiple sites may be present and as such may be best .
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Several codon-based models for the evolution of protein-coding DNA sequences are developed that account for varying selection intensity among amino acid sites. The "neutral model" assumes two categories of sites at which amino acid replacements are either neutral or deleterious. The "positive-selection model" assumes an additional category of positively selected sites at which nonsynonymous substitutions occur at a higher rate than synonymous ones. This model is also used to identify target sites for positive selection. The models are applied to a data set of the V3 region of the HIV-1 envelope gene, sequenced at different years after the infection of one patient. The results provide strong support for variable selection intensity among amino acid sites The neutral model is rejected in favor of the positive-selection model, indicating the operation of positive selection in the region. Positively selected sites are found in both the V3 region and the flanking regions.
django.contrib.sites registers apost_migrate signal handler which creates adefault site named example.com with the domain example.com. This sitewill also be created after Django creates the test database. To set thecorrect name and domain for your project, you can use a data migration.
How did CurrentSiteManagerknow which field of Photo was theSite? By default,CurrentSiteManager looks for aeither a ForeignKey calledsite or aManyToManyField calledsites to filter on. If you use a field named something other thansite or sites to identify whichSite objects your object isrelated to, then you need to explicitly pass the custom field name asa parameter toCurrentSiteManager on yourmodel. The following model, which has a field called publish_on,demonstrates this:
To avoid repetitions, adddjango.contrib.sites.middleware.CurrentSiteMiddleware toMIDDLEWARE. The middleware sets the site attribute on everyrequest object, so you can use request.site to get the current site.
A function that checks if django.contrib.sites is installed andreturns either the current Siteobject or a RequestSite objectbased on the request. It looks up the current site based onrequest.get_host() if theSITE_ID setting is not defined.
Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level,the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information,and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo ( ), which can help facilitate the functional investigation of protein malonylation on mammals and plants.
A number of computational methods have been introduced to predict malonylation sites based on machine learning approaches. Xu et al. developed the first web-server, Mal-Lys, to predict Kmal sites for M. musculus proteins12. Specifically, the support vector machine (SVM) and minimum redundancy maximum relevance (mRMR) technique were adopted to develop the prediction model by considering features from the peptide fragments, including the position specific amino acid propensity, sequence order information, and physicochemical properties. Xiang et al. used pseudo amino acids as features to construct an SVM-based classifier13. Wang et al. took multiple organisms into consideration to build a novel online prediction tool, MaloPred, for the identification of malonylation sites in E.coli, M. musculus, and H. sapiens, separately, by integrating not only protein sequence information and physicochemical properties, but also evolutionarily similar features14. Taherzadeh et al. further investigated whether different species require different prediction models to maximize the accuracy15. By training the models using data from mice and testing them on other species, they found similar underlying physicochemical mechanisms between mice and humans, but not between mice and bacteria. It should be noted that their SVM-based web server, SPRINT-Mal, was the first online malonylation sites prediction tool to take into account the predicted structural properties of the proteins. Zhang et al. provided systematic comparisons of sequence-based features, physicochemical-property-based features, and evolutionary-derived features in the identification of Kmal sites for E. coli, M. musculus, and H. sapiens, respectively16. Random forest (RF), SVM, LightGBM, K-nearest neighbor (KNN), and logistic regression (LR) were adopted to generate optimal feature sets. The integration of the single-method-based models through ensemble learning was found to improve the prediction performance in independent tests. Ahmed et al. proposed a new hybrid resampling method for highly imbalanced data17. Furthermore, deep learning approach has recently been widely applied to biological sequence analysis18,19,20,21. Chen et al. first constructed an integration of the deep learning model based on long short-term memory (LSTM) with an RF classifier for the prediction of mammalian malonylation sites22. Due to the strong capability of the deep learning methodology to learn sparse representation, this methodology showed a superior performance compared to traditional machine learning model. In addition to the malonylation site, many computational methods have been developed for the prediction of various PTM sites based on protein sequences23,24,25,26,27,28.
To improve our understanding of the mechanism of malonylation, it is necessary to identify the malonylation sites accurately in advance. However, the experimental identification was mainly performed using mass spectrometry, which is time-consuming, labor-intensive, and expensive. Computational approaches could be used to effectively and accurately identify malonylation sites. Currently, existing computational approaches mostly rely on feature engineering, while deep learning is capable of excavating the underlying characteristics from a large-scale training dataset. Additionally, although the biological functions of malonylation in plants require attention, the currently existing tools have only taken into account malonylation sites in humans, mice, and bacteria. An efficient methodology for the identification of malonylation sites in more organisms would greatly improve the understanding of the mechanisms of malonylation. Therefore, the primary purpose of this study was to develop hybrid models combining CNN and machine learning algorithms for the prediction of malonylation sites in mammals and plants, respectively. Meanwhile, a user-friendly web tool, which includes an optimal classifier, was established for individual use in the identification of malonylation sites.
WebLogo was mainly used to analyze the frequencies of occurrence of every position around the malonylation sites29. In Supplementary Fig. S1, mouse and human, both mammals, tended to have similar patterns. More specifically, the malonylation peptides had higher frequencies in the cases of Lysine (K), Leucine (L), and Glutamic acid (E) in mammalian proteins. As shown in Fig. 2, using the TwoSampleLogo30 of malonylation and non-malonylation peptides, the enrichment of amino acids neighboring the malonylation sites across species was observed. Lysine (K) was found to be significantly enriched at multiple positions in both H. sapiens and M. musculus proteins, particularly at positions from 1 to 19 and positions 27, 28, and 29. Meanwhile, leucine (L) was found to be depleted at positions 9, 10, and 13. On the other hand, T. aestivum proteins showed a very different pattern when compared to H. sapiens and M. musculus proteins for arginine (R) enrichment at positions 10, 12, 13, 14, 21, 28, and 29 and no evidence of serine (S) depletion. The two sample logo for H. sapiens to M. musculus, H. sapiens to T. aestivum, and M. musculus to T. aestivum is shown in Fig. 3, which indicates that T. aestivum is different from the mammals. Subsequently, the data of the H. sapiens and M. musculus proteins could be combined to build a prediction model for mammals, thereby building another prediction model for T. aestivum.
Supplementary Fig. S2, shows the AUC performance of each feature with a different window size of peptides. For the mammalian model, the feature AAINDEX, one hot vector, and PSSM resulted in the best AUC using CNN models with a window size of 37, 33, and 35, respectively. Feature AAC and PAAC showed their best AUC using RF models with a window size of 33. Details of the performance are shown in Table 1. Importantly, the models were only tested after the models were chosen. In other words, the test results did not play any role in the model selection. 0852c4b9a8
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