SUMOhunt: Combining Spatial Staging between Lysine and SUMO with Random Forests to Predict SUMOylation

Ijaz, Amna

doi:10.1155/2013/671269

Cited by 14 publications

(9 citation statements)

References 62 publications

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“…The identification of SUMOylation sites and SUMO-interaction motifs in proteins is fundamental for understanding biological functions and regulatory mechanisms of SUMOs. Recently several bioinformatics software tools have been developed to predict SUMOylation modification ( table 2 ), including SUMO sp [ 70 , 71 ], SUMO plot [ 72 ], SUMO pre [ 73 ], F ind SUMO [ 74 ], SUM m O n [ 75 ], SUMO tr [ 76 ], S ee SUMO [ 77 ], SUMO hydro [ 78 ] and SUMO hunt [ 79 ]. The SUMO sp and SUMO plot approaches predict SUMO modification sites mainly based on the conserved sequence ψ–K–X–E/D.…”

Section: Approaches To Identify Sumoylation Sitementioning

confidence: 99%

Protein SUMOylation modification and its associations with disease

et al. 2017

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SUMOylation, as a post-translational modification, plays essential roles in various biological functions including cell growth, migration, cellular responses to stress and tumorigenesis. The imbalance of SUMOylation and deSUMOylation has been associated with the occurrence and progression of various diseases. Herein, we summarize and discuss the signal crosstalk between SUMOylation and ubiquitination of proteins, protein SUMOylation relations with several diseases, and the identification approaches for SUMOylation site. With the continuous development of bioinformatics and mass spectrometry, several accurate and high-throughput methods have been implemented to explore small ubiquitin-like modifier-modified substrates and sites, which is helpful for deciphering protein SUMOylation-mediated molecular mechanisms of disease.

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Section: Approaches To Identify Sumoylation Sitementioning

confidence: 99%

Protein SUMOylation modification and its associations with disease

et al. 2017

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“…For a classification task, the output of testing sample x is majority vote of m decision trees. Due to advantages such as cheap computation time, ability to deal with high-dimensional data, and better performance, the random forest has been widely applied to classification and regression [ 57 , 58 , 59 , 60 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites

Huang

Zhao

2018

Molecules

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Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs.

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“…The selection of classification algorithm is very important for constructing efficient classification models. In this study, a powerful and classic classification algorithm, RF [12], was adopted, which has been widely used to tackle several problems in bioinformatics [9,16,[20][21][22][23][24][25][26][27][28]. Its brief description was as below.…”

Section: Random Forestmentioning

confidence: 99%

Similarity-Based Machine Learning Model for Predicting the Metabolic Pathways of Compounds

2020

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Metabolic pathways refer to the continuous chemical reactions in the metabolic process in vivo. Compounds are the major participant for most metabolic pathways. It is essential to determine which compounds can constitute a metabolic pathway. This problem can be converted to the identification of the metabolic pathways of compounds. Although traditional experiments can provide solid results, they are always of low efficiency and high cost. To date, several machine leaning models have been proposed to address this problem. However, almost all models only identified metabolic pathway types of compounds rather than actual metabolic pathways. This study proposed a novel model for predicting actual metabolic pathways for given compounds. The pairs of compounds and metabolic pathways were termed as samples, thereby modeling a binary classification problem. With the concept of "similarity", each sample was represented by seven features, extracted from seven associations of compounds, which measure compound linkages from different aspects. The model adopted random forest as the classification algorithm. Two types of tenfold cross-validation were adopted to evaluate the performance of the model, indicating its utility. A feature analysis was also performed to determine which compound association was highly related to the identification of metabolic pathways of compounds.

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SUMOhunt: Combining Spatial Staging between Lysine and SUMO with Random Forests to Predict SUMOylation

Cited by 14 publications

References 62 publications

Protein SUMOylation modification and its associations with disease

Protein SUMOylation modification and its associations with disease

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites

Similarity-Based Machine Learning Model for Predicting the Metabolic Pathways of Compounds

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