Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desirable. In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with an accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of 8.0% accuracy and 16.0% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research. The user-friendly web server of the proposed BERT4Bitter is freely accessible at http://pmlab.pythonanywhere.com/BERT4Bitter. Supplementary data are available at Bioinformatics online.

In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

Aim: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates. Materials & methods: This study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred). Results: This approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on α-helix and β-sheet, respectively, on the hemolytic activity. Conclusion: A sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.