Prediction of Ubiquitination Sites Using UbiNets

Yadav, Sarthak; Gupta, Manoj Kumar; Bist, Ankur Singh

doi:10.1155/2018/5125103

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Our results demonstrated that DL approaches outperformed traditional ML techniques across various evaluation metrics, specifically achieving a macro-F1 score of 0.574 compared to 0.537. Specifically, in contrast to many customized-designed DL network architectures [ 71 ], we found that the LSTM architecture achieved the highest macro-F1 score using only raw amino acid sequences as input. This finding is consistent with previous research [ 14 , 28 ] and shows the capability of DNN architectures to automatically learn meaningful features from protein sequences for ubiquitination site prediction.…”

Section: Discussioncontrasting

confidence: 68%

Machine learning-based approaches for ubiquitination site prediction in human proteins

Pourmirzaei,

Ramazi,

Esmaili

et al. 2023

BMC Bioinformatics

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Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due to the cost- and time-consuming nature of traditional approaches for Ubi-site detection, there has been a growing interest in leveraging artificial intelligence for computer-aided Ubi-site prediction. In this study, we collected experimentally verified Ubi-sites of human proteins from the dbPTM database, then conducted comprehensive state-of-the art computational methods along with standard evaluation metrics and a proper validation strategy for Ubi-site prediction. We presented the effectiveness of our framework by comparing ten machine learning (ML) based approaches in three different categories: feature-based conventional ML methods, end-to-end sequence-based deep learning (DL) techniques, and hybrid feature-based DL models. Our results revealed that DL approaches outperformed the classical ML methods, achieving a 0.902 F1-score, 0.8198 accuracy, 0.8786 precision, and 0.9147 recall as the best performance for a DL model using both raw amino acid sequences and hand-crafted features. Interestingly, our experimental results disclosed that the performance of DL methods had a positive correlation with the length of amino acid fragments, suggesting that utilizing the entire sequence can lead to more accurate predictions in future research endeavors. Additionally, we developed a meticulously curated benchmark for Ubi-site prediction in human proteins. This benchmark serves as a valuable resource for future studies, enabling fair and accurate comparisons between different methods. Overall, our work highlights the potential of ML, particularly DL techniques, in predicting Ubi-sites and furthering our knowledge of protein regulation through ubiquitination in cells.

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Section: Discussioncontrasting

confidence: 68%

Machine learning-based approaches for ubiquitination site prediction in human proteins

Pourmirzaei,

Ramazi,

Esmaili

et al. 2023

BMC Bioinformatics

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“…Another predictor, ESA-UbiSite, which is based on an evolutionary screening algorithm (ESA), uses a set of well-selected physicochemical properties together with an SVM for accurate prediction. In the literature, deep learning models that include UbiNets use densely connected neural networks [ 55 ]. DeepUbi uses a convolutional neural network (CNN) [ 56 ] and Caps-Ubi uses a capsule network [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

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dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

Qiao

Zhao

2019

Journal of Theoretical Biology

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Prediction of Ubiquitination Sites Using UbiNets

Cited by 5 publications

References 25 publications

Machine learning-based approaches for ubiquitination site prediction in human proteins

Machine learning-based approaches for ubiquitination site prediction in human proteins

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

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