DeephageTP: a convolutional neural network framework for identifying phage-specific proteins from metagenomic sequencing data

Bacteriophages (phages) are the most abundant and diverse biological entity on Earth. Due to the lack of universal gene markers and database representatives, there about 50–90% of genes of phages are unable to assign functions. This makes it a challenge to identify phage genomes and annotate functions of phage genes efficiently by homology search on a large scale, especially for newly phages. Portal (portal protein), TerL (large terminase subunit protein), and TerS (small terminase subunit protein) are three specific proteins of Caudovirales phage. Here, we developed a CNN (convolutional neural network)-based framework, DeephageTP, to identify the three specific proteins from metagenomic data. The framework takes one-hot encoding data of original protein sequences as the input and automatically extracts predictive features in the process of modeling. To overcome the false positive problem, a cutoff-loss-value strategy is introduced based on the distributions of the loss values of protein sequences within the same category. The proposed model with a set of cutoff-loss-values demonstrates high performance in terms of Precision in identifying TerL and Portal sequences (94% and 90%, respectively) from the mimic metagenomic dataset. Finally, we tested the efficacy of the framework using three real metagenomic datasets, and the results shown that compared to the conventional alignment-based methods, our proposed framework had a particular advantage in identifying the novel phage-specific protein sequences of portal and TerL with remote homology to their counterparts in the training datasets. In summary, our study for the first time develops a CNN-based framework for identifying the phage-specific protein sequences with high complexity and low conservation, and this framework will help us find novel phages in metagenomic sequencing data. The DeephageTP is available at https://github.com/chuym726/DeephageTP

Momordica Grosvenori Shell-Derived Porous Carbon Materials for High-Efficiency Symmetric Supercapacitors

Porous carbon materials derived from waste biomass have received broad interest in supercapacitor research due to their high specific surface area, good electrical conductivity, and excellent electrochemical performance. In this work, Momordica grosvenori shell-derived porous carbons (MGCs) were synthesized by high-temperature carbonization and subsequent activation by potassium hydroxide (KOH). As a supercapacitor electrode, the optimized MGCs-2 sample exhibits superior electrochemical performance. For example, a high specific capacitance of 367 F∙g−1 is achieved at 0.5 A∙g−1. Even at 20 A∙g−1, more than 260 F∙g−1 can be retained. Moreover, it also reveals favorable cycling stability (more than 96% of capacitance retention after 10,000 cycles at 5 A∙g−1). These results demonstrate that porous carbon materials derived from Momordica grosvenori shells are one of the most promising electrode candidate materials for practical use in the fields of electrochemical energy storage and conversion