DMCTOP: Topology Prediction of AlphaHelical Transmembrane Protein Based on Deep MultiScale Convolutional Neural Network Alphahelical transmembrane proteins ( alpha textTMPs ) belong to an important category of integral membranes. Their structures are highly valuable in relevant research, but costly to solve experimentally. Sequencebased topology prediction provides a practical computational approach to characterize the structure features. Although much progress had been made in the past decade, there is significant room for improvement in predicting the topology structure. Deep learning brings a great opportunity for its capability of mining new features from data. In this work, we propose a novel  alpha textTMP  topology prediction method DMCTOP using a Deep MultiScale Convolutional Neural Network (DMCNN), which composes of two deep convolutional blocks to extract local and global contextual features. Consequently, the inputs of DMCTOP is simplified to a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the Nterminal orientation for an  alpha textTMP  sequence. In the testing experiments, the prediction accuracy was calculated according to the whole sequence, the transmembrane segments and the traditional criterion. Our stateoftheart method achieved the highest prediction accuracy compared to all the previous methods. The standalone tool is available at https:github.comNENUBioComputeDMCTOP.

Winwin Cooperation: A Novel DualModal DualLabel Algorithm for Membrane Proteins Function Prescreen Integral membrane proteins (MPs) make up a large proportion of the genomes of many organisms and the amount of sequenced proteins is growing at an unprecedented pace. As a result, MPs function prescreen is of great necessity. To be functional, MPs must be expressed and localized through a series of elaborate subcellular processes. It is worth noting that subtle changes in sequence may lead to drastic changes in expression and localization. In light of the above observation, taking the complementary information of sequencebased proteins into consideration, a novel boosting DualModal DualLabel (DMDL) algorithm with hypothesis reuse is proposed. On the one hand, DMDL treats sequence features and structure features as dualmodal. On the other hand, DMDL considers eukaryotic expression and plasma membrane localization as duallabel, which would be of great value as a prescreen for MPs function. Meanwhile, two label sets interact, which can not only make the best use of two modal features, but also could effectively exploit the relationship between two label sets. An assessment of realworld channelrhodopsins (ChRs) chimeras clearly validate the effectiveness of DMDL algorithm compared with stateoftheart algorithms. In addition, extensive experiments are performed on adapted public datasets, showing effeceiveness of hypothesis reuse mechanism in DMDL.