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.

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.

There are numerous means to improve the tilapia aquaculture industry, and one is to develop disease resistance through selective breeding using molecular markers. In this study, 11 disease-resistance-associated microsatellite markers including 3 markers linked to hamp2, 4 linked to hamp1, 1 linked to pgrn2, 2 linked to pgrn1, and 1 linked to piscidin 4 (TP4) genes were established for tilapia strains farmed in Taiwan after challenge with Streptococcus inae. The correlation analysis of genotypes and survival revealed a total of 55 genotypes related to survival by the chi-square and Z-test. Although fewer markers were found in B and N2 strains compared with A strain, they performed well in terms of disease resistance. It suggested that this may be due to the low potency of some genotypes and the combinatorial arrangement between them. Therefore, a predictive model was built by the genotypes of the parental generation and the mortality rate of different combinations was calculated. The results show the same trend of predicted mortality in the offspring of three new disease-resistant strains as in the challenge experiment. The present findings is a nonkilling method without requiring the selection by challenge with bacteria or viruses and might increase the possibility of utilization of selective breeding using SSR markers in farms.