Predicting antimicrobial peptides (AMPs’) function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the ‘CNN-BiLSTM-SVM classifier’ and ‘cellular automata image’, a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.

In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugsin silicocan improve the success rate of drug screening. However, most previous works extracted and utilized an effective representation of drugs from a single perspective. These methods merely considered the topological information of drug in the biological entity network, or combined the association information (e.g. knowledge graph KG) between drug and other biomarkers, or only used the chemical structure or sequence information of drug. Consequently, to jointly learn drug features from both the macroscopic biological network and the microscopic drug molecules. We propose a hybrid embedding graph neural network model named idse-HE, which integrates graph embedding module and node embedding module. idse-HE can fuse the drug chemical structure information, the drug substructure sequence information and the drug network topology information. Our model deems the final representation of drugs and side effects as two implicit factors to reconstruct the original matrix and predicts the potential side effects of drugs. In the robustness experiment, idse-HE shows stable performance in all indicators. We reproduce the baselines under the same conditions, and the experimental results indicate that idse-HE is superior to other advanced methods. Finally, we also collect evidence to confirm several real drug side effect pairs in the predicted results, which were previously regarded as negative samples. More detailed information, scientific researchers can access the user-friendly web-server of idse-HE at http://bioinfo.jcu.edu.cn/idse-HE. In this server, users can obtain the original data and source code, and will be guided to reproduce the model results.

Background: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug–target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more effective in revealing the potential connection between drug and target in the bioinformatics network composed of drugs, proteins and other related data. Results: In this work, we have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of DTI classification. This method first obtains the molecular fingerprint information of drugs and the pseudo amino acid composition information of proteins, then extracts the initial features of nodes through Bi-LSTM, and uses the attention mechanism to aggregate heterogeneous neighbors. In several comparative experiments, the overall performance of HGDTI significantly outperforms other state-of-the-art DTI prediction models, and the negative sampling technology is employed to further optimize the prediction power of model. In addition, we have proved the robustness of HGDTI through heterogeneous network content reduction tests, and proved the rationality of HGDTI through other comparative experiments. These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. Conclusions: The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. For the convenience of related researchers, a user-friendly web-server has been established at http://bioinfo.jcu.edu.cn/hgdti.

The hypomethylation of the whole cancer genome and the hypermethylation of the promoter of specific tumor suppressor genes are the important reasons for the rapid proliferation of cancer cells. Therefore, obtaining the distribution of 5-methylcytosine (5mC) in promoters is a key step to further understand the relationship between promoter methylation and mRNA gene expression regulation. Large-scale detection of DNA 5mC through wet experiments is still time-consuming and laborious. Therefore, it is urgent to design a method for identifying the 5mC site of genome-wide DNA promoters. Based on promoter methylation data of the small cell lung cancer (SCLC) from the database named cancer cell line Encyclopedia (CCLE), we built a fusion decision predictor called iPromoter-5mC for identifying methylation modification sites in promoters using deep neural network (DNN). One-Hot Encoding (One-hot) was used to encode the promoter samples for the classification. The method achieves average AUC of 0.957 on the independent testing dataset, indicating that our predictor is robust and reliable. A user-friendly web-server called iPromoter-5mC could be freely accessible at http://www.jci-bioinfo.cn/iPromoter-5mC, which will provide simple and effective means for users to study promoter 5mC modification.

N6,2′-O-dimethyladenosine (m⁶Am) is a post-transcriptional modification that may be associated with regulatory roles in the control of cellular functions. Therefore, it is crucial to accurately identify transcriptome-wide m⁶Am sites to understand underlying m⁶Am-dependent mRNA regulation mechanisms and biological functions. Here, we used three sequence-based feature-encoding schemes, including one-hot, nucleotide chemical property (NCP), and nucleotide density (ND), to represent RNA sequence samples. Additionally, we proposed an ensemble deep learning framework, named DLm6Am, to identify m⁶Am sites. DLm6Am consists of three similar base classifiers, each of which contains a multi-head attention module, an embedding module with two parallel deep learning sub-modules, a convolutional neural network (CNN) and a Bi-directional long short-term memory (BiLSTM), and a prediction module. To demonstrate the superior performance of our model’s architecture, we compared multiple model frameworks with our method by analyzing the training data and independent testing data. Additionally, we compared our model with the existing state-of-the-art computational methods, m6AmPred and MultiRM. The accuracy (ACC) for the DLm6Am model was improved by 6.45% and 8.42% compared to that of m6AmPred and MultiRM on independent testing data, respectively, while the area under receiver operating characteristic curve (AUROC) for the DLm6Am model was increased by 4.28% and 5.75%, respectively. All the results indicate that DLm6Am achieved the best prediction performance in terms of ACC, Matthews correlation coefficient (MCC), AUROC, and the area under precision and recall curves (AUPR). To further assess the generalization performance of our proposed model, we implemented chromosome-level leave-out cross-validation, and found that the obtained AUROC values were greater than 0.83, indicating that our proposed method is robust and can accurately predict m⁶Am sites.