SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings

Trac, Thinh Quang; Phan, Kinh Huy; Nguyen, Cuong Manh; Dang, Hai Thanh

doi:10.1101/793794

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…We calculated the Sn, Sp, MCC, and AUC values to measure the model's performance. Many well-known prediction tools were compared, including GPS2.1 [31], NetPhos [32], PPRED [33], Musite [23], PhosphoSVM [21], SKIPHOS [34], and DeepPhos [27]. The results showed that our model outperformed all other models for the S and T sites.…”

Section: Comparison With Existing Phosphorylation Site Prediction Toolsmentioning

confidence: 95%

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

Wang

Zhang

et al. 2022

IJMS

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Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.

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Section: Comparison With Existing Phosphorylation Site Prediction Toolsmentioning

confidence: 95%

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

Wang

Zhang

et al. 2022

IJMS

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“…In this section, we provide a discussion of the comparative performance of Attenphos for general phosphorylation site prediction using a balanced dataset from human protein phosphorylation. We compared Attenphos with several existing general phosphorylation site prediction tools, including GPS 2.1 [12], NetPhos 3.0 [13], PPRED [14], Musite [15], PhosphoSVM [16], SKIPHOS [17], Deepphos [11], and Transphos [18]. We performed 10-fold cross-validation to train the model using the training datasets of the S, T, and Y sites.…”

Section: Comparison Experiments On Balanced Datasetsmentioning

confidence: 99%

Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism

Song,

Yang,

et al. 2024

IJMS

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Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery.

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“…In addition, biological properties are also used for prediction, such as with the GPS [14] and iGPS [15]; GPS uses the motif length selection (MLS) method, and iGPS is based on GPS [16] and adds information such as protein interactions to predict phosphorylation sites. Other prediction tools are non-specific [17], such as NetPhos [18] and DISPHOS [19]. NetPhos uses neural networks to predict the phosphorylation sites, while DISPHOS uses the amino acid frequency and disorder information to identify phosphorylation sites.…”

Section: Introductionmentioning

confidence: 99%

GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

Chen

Liao

Chang

et al. 2020

IJMS

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Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.

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SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings

Cited by 3 publications

References 29 publications

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism

GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

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