Hemolytic Peptide Research Articles

Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.

HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.

Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the hemolytic activity of therapeutic peptides is essential for the development of peptide therapies. In this study, a multi-feature cross-fusion model, HemoFuse, for hemolytic peptide identification is proposed. The feature vectors of peptide sequences are transformed by word embedding technique and four hand-crafted feature extraction methods. We apply multi-head cross-attention mechanism to hemolytic peptide identification for the first time. It captures the interaction between word embedding features and hand-crafted features by calculating the attention of all positions in them, so that multiple features can be deeply fused. Moreover, we visualize the features obtained by this module to enhance its interpretability. On the comprehensive integrated dataset, HemoFuse achieves ideal results, with ACC, SP, SN, MCC, F1, AUC, and AP of 0.7575, 0.8814, 0.5793, 0.4909, 0.6620, 0.8387, and 0.7118, respectively. Compared with HemoDL proposed by Yang et al., it is 3.32%, 3.89%, 5.93%, 10.6%, 8.17%, 5.88%, and 2.72% higher. Other ablation experiments also prove that our model is reasonable and efficient. The codes and datasets are accessible at https://github.com/z11code/Hemo .

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Enterolyin S, a Polythiazole-containing Hemolytic Peptide from Enterococcus caccae.

The β-hemolytic factor streptolysin S (SLS) is an important linear azol(in)e-containing peptide (LAP) that contributes significantly to the virulence of Streptococcus pyogenes. Despite its discovery 85 years ago, SLS has evaded structural characterizing owing to its notoriously problematic physicochemical properties. Here, we report the discovery and characterization of a structurally analogous hemolytic peptide from Enterococcus caccae, termed enterolysin S (ELS). Through heterologous expression, site-directed mutagenesis, chemoselective modification, and high-resolution mass spectrometry, we found that ELS contains an intriguing contiguous octathiazole moiety. The discovery of ELS expands our knowledge of hemolytic LAPs by adding a new member to this virulence-promoting family of modified peptides.

EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work. Proposed framework utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely Acc, Sn, Pr, Fs, Sp, Ba, and Mcc obtained by proposed framework for test data is ≈ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

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.

Evaluation of a Novel Synthetic Peptide Derived from Cytolytic Mycotoxin Candidalysin.

The importance of neuroinflammation in neurology is becoming increasingly apparent. In addition to neuroinflammatory diseases such as multiple sclerosis, the role of neuroinflammation has been identified in many non-inflammatory neurological disorders such as stroke, epilepsy, and cancer. The immune response within the brain involves the presence of CNS resident cells; mainly glial cells, such as microglia, the CNS resident macrophages. We evaluated the peptide Ca-MAP1 bioinspired on the C. albicans immature cytolytic toxin candidalysin to develop a less hemolytic peptide with anti-neuroinflammatory, antibacterial, and cytotoxic activity against tumor cells. In silico and in vitro studies were performed at various concentrations. Ca-MAP1 exhibits low hemolytic activity at lower concentrations and was not cytotoxic to MRC-5 and BV-2 cells. Ca-MAP1 showed activity against Acinetobacter baumannii, Escherichia coli ATCC, E. coli KPC, Klebsiella pneumoniae ATCC, Pseudomonas aeruginosa, and Staphylococcus aureus ATCC. Furthermore, Ca-MAP1 exhibits anti-neuroinflammatory activity in the BV-2 microglia model, with 93.78% inhibition of nitrate production at 18.1 µM. Ca-MAP1 presents cytotoxic activity against tumor cell line NCI-H292 at 36.3 μM, with an IC50 of 38.4 µM. Ca-MAP1 demonstrates results that qualify it to be evaluated in the next steps to promote the control of infections and provide an alternative antitumor therapy.

Machine Learning Prediction of Antimicrobial Peptides.

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

Staphylococcus lugdunensis.

Historically, staphylococci are grouped as coagulase-negative or coagulase-positive, according to their ability to induce clotting of mammalian serum through the activity of a secreted coagulase. S. lugdunensis belongs to the coagulase-negative staphylococci (CoNS) but it is in many ways unusual as it shares several features with the coagulase-positive and highly invasive opportunistic pathogen Staphylococcus aureus. Like S. aureus, many S. lugdunensis strains express a cell wall-anchored molecule (Fbl) promoting adhesion to fibrinogen [ 8. Geoghegan J.A. et al. Molecular characterization of the interaction of staphylococcal microbial surface components recognizing adhesive matrix molecules (MSCRAMM) ClfA and Fbl with fibrinogen. J. Biol. Chem. 2010; 285: 6208-6216 Abstract Full Text Full Text PDF PubMed Scopus (52) Google Scholar ]. This mediates clumping of fibrinogen-coated latex beads, which are frequently used for the rapid identification of S. aureus. This can lead to misidentification of S. lugdunensis in clinical samples. Furthermore, S. lugdunensis is more virulent than other CoNS, a feature especially apparent in its ability to cause infective endocarditis. In fact, S. lugdunensis encodes several putative virulence factors, including the hemolytic SLUSH peptides, which allow the release of hemoglobin (Hb) from erythrocytes [ 9. Donvito B. et al. Synergistic hemolytic activity of Staphylococcus lugdunensis is mediated by three peptides encoded by a non-agr genetic locus. Infect. Immun. 1997; 65: 95-100 Crossref PubMed Google Scholar ], sortase A [ 4. Heilbronner S. et al. Sortase A promotes virulence in experimental Staphylococcus lugdunensis endocarditis. Microbiology. 2013; 159: 2141-2152 Crossref PubMed Scopus (30) Google Scholar ], a streptolysin-like toxin [ 10. Lebeurre J. et al. Comparative genome analysis of Staphylococcus lugdunensis shows clonal complex-dependent diversity of the putative virulence factor, ess/type VII locus. Front. Microbiol. 2019; 10: 2479 Crossref PubMed Scopus (10) Google Scholar ], and an ess/type VII locus [ 10. Lebeurre J. et al. Comparative genome analysis of Staphylococcus lugdunensis shows clonal complex-dependent diversity of the putative virulence factor, ess/type VII locus. Front. Microbiol. 2019; 10: 2479 Crossref PubMed Scopus (10) Google Scholar ]. Additionally, like many bacterial pathogens, S. lugdunensis possesses systems (Isd and Lha) for the acquisition of heme that is stripped from the host Hb, enabling its cytosolic degradation and the release of nutritional iron [ 11. Jochim A. et al. An ECF-type transporter scavenges heme to overcome iron-limitation in Staphylococcus lugdunensis. eLife. 2020; 9e57322 Crossref PubMed Scopus (8) Google Scholar ]. Further factors that might promote immune evasion, killing of immune cells, or adhesion to host tissues have been suggested but they have not been experimentally validated so far [ 3. Heilbronner S. Foster T.J. Staphylococcus lugdunensis: a skin commensal with invasive pathogenic potential. Clin. Microbiol. Rev. 2021; 34e00205-20 Crossref PubMed Scopus (8) Google Scholar ]. Finally, the extracellular matrix of S. lugdunensis biofilms is not enriched in polysaccharides but in proteinaceous factors [ 12. Frank K.L. Patel R. Poly-N-acetylglucosamine is not a major component of the extracellular matrix in biofilms formed by icaADBC-positive Staphylococcus lugdunensis isolates. Infect. Immun. 2007; 75: 4728-4742 Crossref PubMed Scopus (83) Google Scholar ], and biofilm formation depends on the action of the autolysin AtlL [ 13. Hussain M. et al. The adhesive properties of the Staphylococcus lugdunensis multifunctional autolysin AtlL and its role in biofilm formation and internalization. Int. J. Med. Microbiol. 2015; 305: 129-139 Crossref PubMed Scopus (32) Google Scholar ].

Study of membrane defects induced by antimicrobial and hemolytic peptide Ltc1 in erythrocyte membrane

Study of membrane defects induced by antimicrobial and hemolytic peptide Ltc1 in erythrocyte membrane

Lipid Headgroup Charge Controls Melittin Oligomerization in Membranes: Implications in Membrane Lysis.

Melittin, a hemolytic peptide present in bee venom, represents one of the most well-studied amphipathic antimicrobial peptides, particularly in terms of its membrane interaction and activity. Nevertheless, no consensus exists on the oligomeric state of membrane-bound melittin. We previously reported on the differential microenvironments experienced by melittin in zwitterionic and negatively charged phospholipid membranes. In this work, we explore the role of negatively charged lipids in the oligomerization of membrane-bound melittin (labeled with 7-nitrobenz-2-oxa-1,3-diazol-4-yl (NBD)) utilizing a quantitative photobleaching homo-FRET assay. Our results show that the presence of negatively charged lipids decreases melittin oligomeric size to ∼50% of that observed in zwitterionic membranes. This is possibly due to differential energetics of binding of the peptide monomer to membranes of different compositions and could explain the reduced lytic activity yet tighter binding of melittin in negatively charged membranes. These results constitute one of the first experimental observations on the role of phospholipid headgroup charge in the oligomerization of melittin in membranes and is relevant in light of previous apparently contradictory reports on oligomerization of membrane-bound melittin. Our results highlight the synergistic interplay of peptide-membrane binding events and peptide oligomerization in modulating the organization, dynamics, and function of amphipathic α-helical peptides.

Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

Light-Controlled Release of Therapeutic Proteins from Red Blood Cells

Protein therapeutics are a powerful class of drugs known for their selectivity and potency. However, the potential efficacy of these therapeutics is commonly offset by short circulatory half-lives and undesired action at otherwise healthy tissue. We describe herein a targeted protein delivery system that employs engineered red blood cells (RBCs) as carriers and light as the external trigger that promotes hemolysis and drug release. RBCs internally loaded with therapeutic proteins are readily surface modified with a dormant hemolytic peptide. The latter is activated via easily assigned wavelengths that extend into the optical window of tissue. We have demonstrated that photorelease transpires with spatiotemporal control and that the liberated proteins display the anticipated biological effects in vitro. Furthermore, we have confirmed targeted delivery of a clot-inducing enzyme in a mouse model. Finally, we anticipate that this strategy is not limited to RBC carriers but also should be applicable to nano- and microtransporters comprised of bilayer lipid membranes.

Machine learning-guided discovery and design of non-hemolytic peptides

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks

The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide’s primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of 85.7% and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn, allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.

Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its activity from the given peptides is one of the challenging tasks in immunoinformatics, which is essential for drug development and basic research. Although there are a few computational methods that have been proposed for this aspect, none of them are able to identify HLPs and their activities simultaneously. In this study, we proposed a two-layer prediction framework, called HLPpred-Fuse, that can accurately and automatically predict both hemolytic peptides (HLPs or non-HLPs) as well as HLPs activity (high and low). More specifically, feature representation learning scheme was utilized to generate 54 probabilistic features by integrating six different machine learning classifiers and nine different sequence-based encodings. Consequently, the 54 probabilistic features were fused to provide sufficiently converged sequence information which was used as an input to extremely randomized tree for the development of two final prediction models which independently identify HLP and its activity. Performance comparisons over empirical cross-validation analysis, independent test and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity. For the convenience of experimental scientists, a web-based tool has been established at http://thegleelab.org/HLPpred-Fuse. glee@ajou.ac.kr or watshara.sho@mahidol.ac.th or bala@ajou.ac.kr. Supplementary data are available at Bioinformatics online.

Novel Stapling by Lysine Tethering Provides Stable and Low Hemolytic Cationic Antimicrobial Peptides.

Cationic antimicrobial peptides (CAMPs) are potent therapeutics for drug-resistant bacterial infections. However, the clinical application of CAMPs is hampered by its poor proteolytic stability and hemolytic activity toward eukaryotic cells. Great efforts have been made to design and generate derivatives of CAMPs with improved pharmacological properties. Here, we report a novel stapling protocol, which tethers two ε-amino groups of the lysine residue by the N-alkylation reaction on the hydrophilic face of amphiphilic antimicrobial peptides. A series of lysine-tethered stapled CAMPs were synthesized, employing the antimicrobial peptide OH-CM6 as a model. Biological screening of the stapled CAMPs provided an analogue with strong antimicrobial activity, high proteolytic stability, and low hemolytic activity. This novel stapling approach offers an important chemical tool for developing CAMP-based antibiotics.

Escherichia coli Lipopolysaccharide Modulates Biological Activities of Human-β-Defensin Analogues but Not Non-Ribosomally Synthesized Peptides.

Human-β-defensins (HBD1-3) are antibacterial peptides containing three disulphide bonds. In the present study, the effect of Escherichia coli lipopolysaccharide (LPS) on the antibacterial activities of HBD2-3, C-terminal analogues having a single disulphide bond, Phd1-3, and their corresponding myristoylated analogues MPhd1-3 were investigated. The effect of LPS on the activities of linear amphipathic peptides melittin, LL37 and non-ribosomally synthesized peptides, polymyxin B, alamethicin, gramicidin A, and gramicidin S was also examined. The antibacterial activity of HBD 2-3, Phd1-3, and MPhd1-3 in the presence of LPS against E. coli and Staphylococcus aureus was inhibited. While LPS inhibited the antibacterial activity of LL37, the inhibition of melittin activity was partial. The hemolytic activity exhibited by MPhd1, MPhd3, melittin, and LL37 was inhibited in the presence of LPS. HBD2-3, Phd1-3, and MPhd1-3 also showed endotoxin neutralizing activity. The antibacterial and hemolytic activities of polymyxin B, alamethicin, gramicidin A, and gramicidin S were not inhibited in the presence of LPS. Fluorescence assays employing dansyl cadaverine showed that HBD2-3 and defensin analogues bind to LPS more strongly as compared to alamethicin, gramicidin A, and gramicidin S. Electron microscopy images indicated that peptides disintegrate the structure of LPS. The inhibition of the antibacterial activity of native defensins and analogues in the presence of LPS indicates that the initial interaction with the bacterial surface is similar. The native defensin sequence or structure is also not essential, although cationic charges are necessary for binding to LPS. Hydrophobic interaction is the main driving force for association of non-ribosomally synthesized polymyxin B, alamethicin, gramicidin A, and gramicidin S with LPS. It is likely that these peptides rapidly insert into membranes and do not interact with the bacterial cell surface, whereas cationic peptides such as β-defensin and their analogues, melittin and LL37, first interact with the bacterial cell surface and then the membrane. Our results suggest that evaluating interaction of antibacterial and hemolytic peptides with LPS is a compelling way of elucidating the mechanism of bacterial killing or hemolysis.

A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure.

In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server “HemoPImod” to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).