BACKGROUND: Peptides have emerged as promising therapeutic agents for drug development against cancer, immune disorders, hypertension, and microbial infections. Peptide drugs have the advantage of high selectivity, low production cost, and fewer side effects compared to traditional small molecule-based drugs. However, one main challenge that hinders the adoption of peptide therapeutics is that some peptides are prone to be hemolytic, leading to the disruption of erythrocyte membranes and decreasing the life span of red blood cells. A computational model for hemolytic peptide identification would be a valuable tool for peptide drug discovery. RESULTS: In this study, we present HEPAD, a machine learning predictor to identify hemolytic peptides based on adaptive feature engineering and diverse sequence descriptors. Sequence descriptors were applied for feature encoding, generating a feature vector of nearly 4000 numeric values for each peptide. Next, an adaptive feature engineering method was proposed to produce a customized feature subset for a given dataset. The four datasets considered in this study were associated with 250, 350, 90, and 130 selected features. Five machine learning methods of different rationale were employed to perform cross validation and independent tests. HEPAD yields Matthew's correlation coefficients (MCCs) of 0.973, 0.643, and 0.609, respectively, for three independent datasets. The improvements in MCC compared to existing approaches range from 1.9 to 13.3% for three independent tests. Moreover, data visualization reveals that the customized feature subsets can effectively separate hemolytic peptides from random peptides. CONCLUSIONS: HEPAD offers efficient identification of potential hemolytic peptides, thereby expediting experimental procedures in drug discovery. The source code, datasets, and machine learning models are available at https://github.com/csh07/HEPAD .

Antimicrobial resistance is one of the most serious issue for human health. Compared to existing antibiotics, antimicrobial peptides have the advantage of efficient killing microbes and other pathogens without inducing drug resistance. Large-scale experimental methods to characterize AMPs require wet-lab resources and longer time. In silico prediction of AMP, on the other hand, is an attractive strategy to lower the cost and time in the discovery of new AMPs. In this study, we proposed a CatBoost model for AMP prediction. We included various features for numerical representation of peptides, and then employed a systematic approach to select 130 important features for our machine learning models. The CatBoost model achieves an accuracy, F1-score, MCC, and AUC of 0.758, 0.750, 0.518, and 0.831, respectively, for cross validation. For an independent test based on 188 peptide sequences, the proposed model achieves an accuracy, MCC, and AUC of 0.814, 0.632, and 0.884, respectively, all of which are the best compared to five state-of-art methods. Our model improves the MCC of five existing methods by 2.6% to 21.1%, and improves the AUC of them by 1.3% to 13.3%, respectively. The results demonstrate that our CatBoost model is capable of yielding reliable results, and can be of great help in discovering novel AMPs.

BACKGROUND: The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. RESULTS: In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. The experiment results show that KnowPred site achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred site is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred site. CONCLUSION: KnowPred site demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred site is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred site is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred site prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/.

MOTIVATION: Regulatory proteases modulate proteomic dynamics with a spectrum of specificities against substrate proteins. Predictions of the substrate sites in a proteome for the proteases would facilitate understanding the biological functions of the proteases. High-throughput experiments could generate suitable datasets for machine learning to grasp complex relationships between the substrate sequences and the enzymatic specificities. But the capability in predicting protease substrate sites by integrating the machine learning algorithms with the experimental methodology has yet to be demonstrated. RESULTS: Factor Xa, a key regulatory protease in the blood coagulation system, was used as model system, for which effective substrate site predictors were developed and benchmarked. The predictors were derived from bootstrap aggregation (machine learning) algorithms trained with data obtained from multilevel substrate phage display experiments. The experimental sampling and computational learning on substrate specificities can be generalized to proteases for which the active forms are available for the in vitro experiments. AVAILABILITY: http://asqa.iis.sinica.edu.tw/fXaWeb/