Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitute for antibiotics. New candidates need to fight antibiotic resistance, while discovering novel AMPs through wet-lab screening approaches is inefficient and expensive.

Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them, named GAN-pep 1–8, were selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.

Antiviral peptides from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified antiviral peptides and the advances in deep-learning theory, it is reasonable to experiment with peptide drug design using in-silico methods. We collected the most up-to-date antiviral peptides and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of antiviral peptides in the positive training dataset and enable our deep-learning convolutional neural network model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary data are available at Bioinformatics Advances online

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates’ anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare Supplementary data are available at Bioinformatics online.

Motivation: Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. Results: Our results show that our prediction model is superior to the existing AMP predictors. Availability: AI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contact: cylin@iis.sinica.edu.tw