HyperVR: a hybrid deep ensemble learning approach for simultaneously predicting virulence factors and antibiotic resistance genes

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction

DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting to environmental changes. The prediction of DNA-binding proteins has been a hot topic in the field of bioinformatics. The key to accurately classifying DNA-binding proteins is to find suitable feature sources and explore the information they contain. Although there are already many models for predicting DNA-binding proteins, there is still room for improvement in mining feature source information and calculation methods. In this study, we created a model called DBPboost to better identify DNA-binding proteins. The innovation of this study lies in the use of eight feature extraction methods, the improvement of the feature selection step, which involves selecting some features first and then performing feature selection again after feature fusion, and the optimization of the differential evolution algorithm in feature fusion, which improves the performance of feature fusion. The experimental results show that the prediction accuracy of the model on the UniSwiss dataset is 89.32%, and the sensitivity is 89.01%, which is better than most existing models.

Plant-derived virulence arresting drugs as novel antimicrobial agents: Discovery, perspective, and challenges in clinical use

Antimicrobial resistance (AMR) emerges as a severe crisis to public health and requires global action. The occurrence of bacterial pathogens with multi-drug resistance appeals to exploring alternative therapeutic strategies. Antivirulence treatment has been a positive substitute in seeking to circumvent AMR, which aims to target virulence factors directly to combat bacterial infections. Accumulated evidence suggests that plant-derived natural products, which have been utilized to treat infectious diseases for centuries, can be abundant sources for screening potential virulence-arresting drugs (VADs) to develop advanced therapeutics for infectious diseases. This review sums up some virulence factors and their actions in various species of bacteria, as well as recent advances pertaining to plant-derived natural products as VAD candidates. Furthermore, we also discuss natural VAD-related clinical trials and patents, the perspective of VAD-based advanced therapeutics for infectious diseases and critical challenges hampering clinical use of VADs, and genomics-guided identification for VAD therapeutic. These newly discovered natural VADs will be encouraging and optimistic candidates that may sustainably combat AMR.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.