TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model

Yan, Ke; Lv, Hongwu; Guo, Yichen; Chen, Yongyong; Wu, Hao; Liu, Bin

doi:10.1093/bioinformatics/btac200

Cited by 44 publications

(21 citation statements)

References 48 publications

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“…acids were encoded into nature numbers {1, 2, 3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 20}, respectively. In our benchmark dataset, the length of the peptide sequences varied from 5 bp (the minimum length) to 50 bp (the maximum length), but the model could only process peptide sequences with a fixed dimension.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Thus far, random forest, extra trees and extreme gradient boosting algorithms have successfully identified tumor homing peptide (THP) [11], anti-cancer peptide (ACP) [12], and anti-parasitic peptide (APP) [13]. Furthermore, TPpred-ATMV used BioSeq-BLM tool to extract various peptide sequence features to optimize the prediction of therapeutic peptides [14,15]. Among these traditional ML methods, suitable feature sets are very important to distinguish functional and nonfunctional peptides and achieve excellent performance.…”

Section: Introductionmentioning

confidence: 99%

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PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

et al. 2022

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Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic peptides (MFTP) is growing rapidly, which requires new computational schemes to be proposed to facilitate MFTP discovery. In this study, based on multi-head self-attention mechanism and class weight optimization algorithm, we propose a novel model called PrMFTP for MFTP prediction. PrMFTP exploits multi-scale convolutional neural network, bi-directional long short-term memory, and multi-head self-attention mechanisms to fully extract and learn informative features of peptide sequence to predict MFTP. In addition, we design a class weight optimization scheme to address the problem of label imbalanced data. Comprehensive evaluation demonstrate that PrMFTP is superior to other state-of-the-art computational methods for predicting MFTP. We provide a user-friendly web server of PrMFTP, which is available at http://bioinfo.ahu.edu.cn/PrMFTP.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

et al. 2022

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“…Compared with the traditional antibiotic, AMPs interact with microbial membranes and penetration to promote the death of the target microbe and reduce the development of drug-resistant ( Gaspar et al , 2013 ; Wei et al , 2021a , b ). Therefore, AMPs identification and investigation are important for understanding the mechanism of new drug design ( de la Fuente-Nunez et al , 2017 ; Yan et al , 2022a , b ).…”

Section: Introductionmentioning

confidence: 99%

“…First, the predictors constructed based on conventional methodologies. Among those methods, Support Vector Machine (SVM), Random Forest (RF), decision tree (DT) and ensemble learning methods are the widely used methods, such as TP-MV ( Yan et al , 2022a , b ), iProt-Sub ( Song et al , 2019 ), etc. AVPpred ( Thakur et al , 2012 ) is the first attempt to predict the anti-virus peptides by integrating amino acid composition and physicochemical features with SVM.…”

Section: Introductionmentioning

confidence: 99%

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure

Yan

Guo

et al. 2022

Bioinformatics

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Motivation Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. Results In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. Availability A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. Supplementary information Supplementary data are available at Bioinformatics online.

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“…In recent years, drug development based on peptides has attracted wide attention in the industry due to its highly selective, relatively safe, well tolerated and low production costs (Yan, et al, 2022). Antiviral peptides (AVPs), with 8 to 40 amino acids typically (Schaduangrat, et al, 2019), are a promising resource for the treatment of viral diseases.…”

Section: Introductionmentioning

confidence: 99%

HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

Xia

Jiang

et al. 2022

Preprint

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Motivation: The virus poses a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, accurate and rapid identification of antiviral peptides using computational methods is critical. However, it is still challenging to extract effective feature representations from the sequences for the accurate identification of antiviral peptides. Results: This study introduces a novel two-step approach, named HybAVPnet, with a hybrid network architecture to identify antiviral peptides based on neural networks and traditional machine learning methods. Firstly, eighteen kinds of features are extracted to predict labels and probabilities by the neural network classifier and LightGBM classifier, respectively. Secondly, the support vector machine classifier is carried out using the predicted probability of the first step to make the final prediction. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, especially on independent datasets, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability. Availability and implementation: The predicted model could be downloaded from: https://github.com/greyspring/HybAVPnet Contact: gespring@hdu.edu.cn; yp.cai@siat.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

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TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model

Cited by 44 publications

References 48 publications

PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure

HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

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