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Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel–drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called “iCDI-W2vCom,” was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs via a 1184D vector, ion channel was represented by the word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom via the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target–drug interaction.

Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

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Detecting remote homology proteins is a challenging problem for both basic research and drug development. Although there are a couple of methods to deal with this problem, the benchmark datasets based on which the existing methods were trained and tested contain many high homologous samples as reflected by the fact that the cutoff threshold was set at 95%. In this study, we reconstructed the benchmark dataset by setting the threshold at 40%, meaning none of the proteins included in the benchmark dataset has more than 40% pairwise sequence identity with any other in the same subset. Using the new benchmark dataset, we proposed a new predictor called "dRHP-GreyFun" based on the grey modeling and functional domain approach. Rigorous cross-validations have indicated that the new predictor is superior to its counterparts in both enhancing success rates and reducing computational cost. The predictor can be downloaded from https://github.com/jcilwz/dRHP-GreyFun.

Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew’s correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.

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