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.
Motivation
Therapeutic peptide prediction is important for the discovery of efficient therapeutic peptides and drug development. Researchers have developed several computational methods to identify different therapeutic peptide types. However, these computational methods focus on identifying some specific types of therapeutic peptides, failing to predict the comprehensive types of therapeutic peptides. Moreover, it is still challenging to utilize different properties to predict the therapeutic peptides.
Results
In this study, an adaptive multi-view based on the tensor learning framework TPpred-ATMV is proposed for predicting different types of therapeutic peptides. TPpred-ATMV constructs the class and probability information based on various sequence features. We constructed the latent subspace among the multi-view features and constructed an auto-weighted multi-view tensor learning model to utilize the high correlation based on the multi-view features. Experimental results showed that the TPpred-ATMV is better than or highly comparable with the other state-of-the-art methods for predicting eight types of therapeutic peptides.
Availability
The code of TPpred-ATMV is accessed at: https://github.com/cokeyk/TPpred-ATMV.
Supplementary information
Supplementary data are available at Bioinformatics online.
Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides. In this study, a predictor called PreTP-EL has been proposed via employing the ensemble learning approach to fuse the different features and machine learning techniques in order to capture the different characteristics of various therapeutic peptides. Experimental results showed that PreTP-EL outperformed other competing methods. Availability and implementation: A user-friendly web-server of PreTP-EL predictor is available at http://bliulab.net/PreTP-EL.
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