HemoNet: Predicting hemolytic activity of peptides with integrated feature learning

HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information

Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.

Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Isolation and Optimization of a Broad-Spectrum Synthetic Antimicrobial Peptide, Ap920-WI, from Arthrobacter sp. H5 for the Biological Control of Plant Diseases

Antimicrobial peptides (AMPs) are naturally occurring molecules found in various organisms that can help to defend against invading microorganisms and reduce the likelihood of drug resistance development. This study focused on the isolation of new AMPs from the genome library of a Gram-positive bacterium called Arthrobacter sp. H5. To achieve this, we used the Bacillus subtilis expression system and employed bioinformatics techniques to optimize and modify the peptides, resulting in the development of a new synthetic antimicrobial peptide (SAMP). Ap920 is expected to be a new antimicrobial peptide with a high positive charge (+12.5). Through optimization, a new synthetic antimicrobial peptide, Ap920-WI, containing only 15 amino acids, was created. Thereafter, the antimicrobial and antifungal activities of Ap920-WI were determined using minimum inhibitory concentration (MIC) and the concentration for 50% of maximal effect (EC₅₀). The Ap920-WI peptide was observed to target the outer membrane of fungal hyphae, leading to inhibition of growth in Rhizoctonia Solani, Sclerotinia sclerotiorum, and Botrytis cinerea. In plants, Ap920-WI showed significant antifungal activity and inhibited the infestation of S. sclerotiorum on rape leaves. Importantly, Ap920-WI was found to be safe for mammalian cells since it did not show any hemolytic activity against sheep red blood cells. Overall, the study found that the new synthetic antimicrobial peptide Ap920-WI exhibits broad-spectrum activity against microorganisms and may offer a new solution for controlling plant diseases, as well as hold potential for drug development.

PreTP-Stack: Prediction of Therapeutic Peptides Based on the Stacked Ensemble Learing

Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic peptides. Moreover, it is still challenging to utilize different properties features to predict the therapeutic peptides. In this study, a novel stacking framework PreTP-Stack is proposed for predicting different types of therapeutic peptide. PreTP-Stack is constructed based on ten different features and four predictors (Random Forest, Linear Discriminant Analysis, XGBoost and Support Vector Machine). Then the proposed method constructs an auto-weighted multi-view learning model as a final meta-classifier to enhance the performance of the basic models. Experimental results showed that the proposed method achieved better or highly comparable performance with the state-of-the-art methods for predicting eight types of therapeutic peptides A user-friendly web-server predictor is available at http://bliulab.net/PreTP-Stack.

De Novo Design of AC-P19M, a Novel Anticancer Peptide with Apoptotic Effects on Lung Cancer Cells and Anti-Angiogenic Activity

Despite the current developments in cancer therapeutics, efforts to excavate new anticancer agents continue rigorously due to obstacles, such as side effects and drug resistance. Anticancer peptides (ACPs) can be utilized to treat cancer because of their effectiveness on a variety of molecular targets, along with high selectivity and specificity for cancer cells. In the present study, a novel ACP was de novo designed using in silico methods, and its functionality and molecular mechanisms of action were explored. AC-P19M was discovered through functional prediction and sequence modification based on peptide sequences currently available in the database. The peptide exhibited anticancer activity against lung cancer cells, A549 and H460, by disrupting cellular membranes and inducing apoptosis while showing low toxicity towards normal and red blood cells. In addition, the peptide inhibited the migration and invasion of lung cancer cells and reversed epithelial-mesenchymal transition. Moreover, AC-P19M showed anti-angiogenic activity through the inhibition of vascular endothelial growth factor receptor 2 signaling. Our findings suggest that AC-P19M is a novel ACP that directly or indirectly targets cancer cells, demonstrating the potential development of an anticancer agent and providing insights into the discovery of functional substances based on an in silico approach.

The Role of a Natural Amphibian Skin-Based Peptide, Ranatensin, in Pancreatic Cancers Expressing Dopamine D2 Receptors

Despite the progress in early diagnostic and available treatments, pancreatic cancer remains one of the deadliest cancers. Therefore, there is an urgent need for novel anticancer agents with a good safety profile, particularly in terms of possible side-effects. Recently dopaminergic receptors have been widely studied as they were proven to play an important role in cancer progression. Although various synthetic compounds are known for their interactions with the dopaminergic system, peptides have recently made a great comeback. This is because peptides are relatively safe, easy to correct in terms of the improvement of their physicochemical and biological properties, and easy to predict. This paper aims to evaluate the anticancer activity of a naturally existing peptide-ranatensin, toward three different pancreatic cancer cell lines. Additionally, since there is no sufficient information confirming the exact character of the interaction between ranatensin and dopaminergic receptors, we provide, for the first time, binding properties of the compound to such receptors.

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.