SeqTMPPI: Sequence-Based Transmembrane Protein Interaction Prediction

Transmembrane proteins (TMPs) play important roles in diverse cellular processes, they are the most common drug targets. The interaction between TMPs and nontransmembrane proteins (nonTMPs) is an important part to form the pathway crossing biomembranes, which is directly related to TMPs associated signal transduction, substance transport, and drug metabolism. However, it is difficult for biologists to identify interactions between TMPs and nonTMPs by wet-lab experiments since TMPs are embedded in the phospholipid bilayer while most nonTMPs are soluble proteins. Predicting protein-protein interactions (PPIs) has always been a hot topic in bioinformatics. However, TMP related PPIs occupy a small proportion of those researches, and those methods designed for soluble protein PPI may have limitations to predict the interaction of TMPs and non-TMPs due to the difference between the aqueous and lipid senvironments. There still lack of deep-learning-based predictors using primary sequence information on TMP-nonTMP interactions. In this work, we constructed a benchmark dataset for TMP-nonTMP interactions, adopted the one-hot vector to encode protein sequence pairs and built a Convolutional Neural Network (CNN) based model, SeqTMPPI, predicting the TMP-nonTMP interactions. The experimental results indicated that our method achieved a good performance on an independent testing where the Matthews Correlation Coefficient (MCC) achieved at 0.7066. The predicted interactions were further analyzed in the scope of distribution of the protein family and species. Materials related are available in https://github.com/JulseJiang/SeqTMPPI.