Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs’ broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew’s correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a shortlength (%30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precisionrecall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata-a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut-for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.

Although the physicochemical properties of antimicrobial peptides (AMPs) and anticancer peptides (ACPs) are very similar, it remains unclear which specific parameter(s) of ACPs confer the major anticancer activity. By answering how to construct a short AMP/ACP that could easily be synthesized in the most cost effective way plus conferring a maximum anticancer effect is a very important scientific breakthrough in the development of protein/peptide drugs. In this study, an 18-amino-acids antimicrobial peptide, AcrAP1 (named AP1-Z1), was used as a template. Bioinformatics algorithms were then performed to design its six mutants (AP1-Z3a, AP1-Z3b, AP1-Z5a, AP1-Z5b, AP1-Z7, and AP1-Z9). After a series of in vitro experiments plus intensive computational analysis, the data demonstrated that AP1-Z5a and AP1-Z5b induced both apoptosis and anti-angiogenic effects to achieve the maximum anticancer activity. Specifically, the most effective mutant, AP1-Z5b, exhibited high selectivity for the charged membrane in molecular dynamics simulations. These findings clearly demonstrated that both charge and hydrophobicity play an important role and are necessary to reach an optimum equilibrium for optimizing the anticancer activity of AMPs. Overall, the present study provides a very crucial theoretical basis and important scientific evidence on the key physicochemical parameters of ACP drugs development.

Potent Ebolavirus (EBOV) inhibitors will help to curtail outbreaks such as that which occurred in 2014–16 in West Africa. EBOV has on its surface a single glycoprotein (GP) critical for viral entry and membrane fusion. Recent high-resolution complexes of EBOV GP with a variety of approved drugs revealed that binding to a common cavity prevented fusion of the virus and endosomal membranes, inhibiting virus infection. We performed docking experiments, screening a database of natural compounds to identify those likely to bind at this site. Using both inhibition assays of HIV-1-derived pseudovirus cell entry and structural analyses of the complexes of the compounds with GP, we show here that two of these compounds attach in the common binding cavity, out of eight tested. In both cases, two molecules bind in the cavity. The two compounds are chemically similar, but the tighter binder has an additional chlorine atom that forms good halogen bonds to the protein and achieves an IC 50 of 50 nM, making it the most potent GP-binding EBOV inhibitor yet identified, validating our screening approach for the discovery of novel antiviral compounds.

Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.