Discovering new drugs is a complicated, time-consuming, costly, risky and failure-prone process. However, about 80% of the drugs that have been approved so far are targeted at protein targets, and 99% of them only target specific proteins. This means that there are still a large number of protein targets that are considered “useless”. By exploring miRNA as a potential therapeutic target, we can expand the range of target selection and improve the efficiency of drug development. Therefore, it is of great significance to search for potential miRNA-drug interactions (MDIs) through reasonable computational methods. In this paper, a dual-channel network model, MDIDCN, based on Temporal Convolutional Network (TCN) and Bi-directional Long Short-Term Memory (BiLSTM), was proposed to predict MDIs. Specifically, we first used a known bipartite network to represent the interaction between miRNAs and drugs, and the graph embedding technique of BiNE was applied to learn the topological features of both. Secondly, we used TCN to learn the MACCS fingerprints of drugs, BiLSTM to learn the k-mer of miRNA, and concatenated the topological and structural features of the two together as their fusion features. Finally, the fusion features of miRNA and drug underwent max-pooling, and they were input into the Softmax layer to obtain the predicted scores of both, so as to obtain the potential miRNA-drug interaction pairs. In this paper, the prediction performance of the model was evaluated on three different datasets by using 5-fold cross-validation, and the average AUC were 0.9567, 0.9365, and 0.8975, respectively. In addition, case studies on the drugs Gemcitabine and hsa-miR-155-5p were also conducted in this paper, and the results showed that the model had high accuracy and reliability. In conclusion, the MDIDCN model can accurately and efficiently predict MDIs, which has important implications for drug development.

Synthetic binding proteins (SBPs) with small size, marked solubility and stability, and high affinity are important for protein-based research, treatment, and diagnostics. Over the last several decades, site-directed mutagenesis and directed evolution of privileged protein scaffold make up the great majority of SBPs. The groundbreaking advancement of deep learning (DL) in recent years has revolutionized the problem of protein structure prediction and design. Here, for the first time, the cutting-edge DL framework ProteinMPNN was applied to fulfill the de novo design of 7,245 new synthetic proteins covering 55 different scaffolds based on the original SBPs collected in our SYNBIP database. Comprehensive bioinformatics analysis indicated that, in addition to the excellent performance of sequence recovery, the designed synthetic proteins have a significant improvement in solubility and thermal stability compared to the currently known SBPs. Meanwhile, 8 incredibly suitable protein scaffolds for ProteinMPNN have been identified, from which the designed synthetic proteins calculate displayed good performance on binding ability to their corresponding protein targets. Therefore, the DL-based framework shown great potential in target-directed de novo generation of synthetic protein library with high quality, which could assist experimental biologists to rational protein engineering to discover novel functional protein binders.