Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

Background

Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development.

Methods

In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps.

Results

In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors.

Conclusion

deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy

Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.

In Silico and In vitro Evaluations of the Antibacterial Activities of HIV-1 Nef Peptides against Pseudomonas aeruginosa

One of the burning issues facing healthcare organizations is multidrug-resistant (MDR) bacteria. P. aeruginosa is an MDR opportunistic bacterium responsible for nosocomial and fatal infections in immunosuppressed individuals. According to previous studies, efflux pump activity and biofilm formation are the most common resistance mechanisms in P. aeruginosa. The aim of this study was to propose new antimicrobial peptides (AMPs) that target P. aeruginosa and can effectively address these resistance mechanisms through in silico and in vitro assessments. Since AMPs are an attractive alternative to antibiotics, in vitro experiments were carried out along with bioinformatics analyses on 19 Nef peptides (derived from the HIV-1 Nef protein) in the current study. Several servers, including Dbaasps, Antibp2, CLASSAMP2, ToxinPred, dPABBs and ProtParam were used to predict Nef peptides as AMPs. To evaluate the binding affinities, a molecular docking analysis was performed with the HADDOCK web server for all Nef peptide models against two effective proteins of P. aeruginosa (MexB and PqsR) that play a role in efflux and quorum sensing. Moreover, the antibacterial and antibiofilm activity of the Nef peptides was investigated in a resistant strain of P. aeruginosa. The results of molecular docking revealed that all Nef peptides have a significant binding affinity to the abovementioned proteins. Nef-Peptide-19 has the highest affinity to the active sites of MexB and PqsR with the HADDOCK scores of -136.1 ± 1.7 and -129.4 ± 2, respectively. According to the results of in vitro evaluation, Nef peptide 19 showed remarked activity against P. aeruginosa with minimum inhibitory and bactericidal concen-trations (MIC and MBC) of 10 µM and 20 µM, respectively. In addition, biofilm inhibitory activity was observed at a concentration of 20 µM. Finally, Nef peptide 19 is proposed as a new AMP against P. aeruginosa.

Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus

Introduction: Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited. Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species. Results: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set. Discussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries.

Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDR Acinetobacter baumannii

The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach.

CAMPR4: a database of natural and synthetic antimicrobial peptides

There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMPR4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMPR4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMPR4 will aid to enhance AMP research. CAMPR4 is accessible at http://camp.bicnirrh.res.in/.

Legume Plant Peptides as Sources of Novel Antimicrobial Molecules Against Human Pathogens

Antimicrobial peptides are prominent components of the plant immune system acting against a wide variety of pathogens. Legume plants from the inverted repeat lacking clade (IRLC) have evolved a unique gene family encoding nodule-specific cysteine-rich NCR peptides acting in the symbiotic cells of root nodules, where they convert their bacterial endosymbionts into non-cultivable, polyploid nitrogen-fixing cells. NCRs are usually 30–50 amino acids long peptides having a characteristic pattern of 4 or 6 cysteines and highly divergent amino acid composition. While the function of NCRs is largely unknown, antimicrobial activity has been demonstrated for a few cationic Medicago truncatula NCR peptides against bacterial and fungal pathogens. The advantages of these plant peptides are their broad antimicrobial spectrum, fast killing modes of actions, multiple bacterial targets, and low propensity to develop resistance to them and no or low cytotoxicity to human cells. In the IRLC legumes, the number of NCR genes varies from a few to several hundred and it is possible that altogether hundreds of thousands of different NCR peptides exist. Due to the need for new antimicrobial agents, we investigated the antimicrobial potential of 104 synthetic NCR peptides from M. truncatula, M. sativa, Pisum sativum, Galega orientalis and Cicer arietinum against eight human pathogens, including ESKAPE bacteria. 50 NCRs showed antimicrobial activity with differences in the antimicrobial spectrum and effectivity. The most active peptides eliminated bacteria at concentrations from 0.8 to 3.1 μM. High isoelectric point and positive net charge were important but not the only determinants of their antimicrobial activity. Testing the activity of shorter peptide derivatives against Acinetobacter baumannii and Candida albicans led to identification of regions responsible for the antimicrobial activity and provided insight into their potential modes of action. This work provides highly potent lead molecules without hemolytic activity on human blood cells for novel antimicrobial drugs to fight against pathogens.

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome.

Bio-nano scale modifications of melittin for improving therapeutic efficacy

INTRODUCTION

Melittin (MLT), a natural membrane-active component, is the most prominent cytolytic peptide from bee venom. Remarkable biological properties of MLT, including anti-inflammatory, antimicrobial, anticancer, anti-protozoan, and antiarthritic activities, make it an up-and-coming therapeutic candidate for a wide variety of human diseases. Therapeutic applications of MLT may be hindered due to low stability, high toxicity, and weak tissue penetration. Different bio-nano scale modifications hold promise for improving its functionality and therapeutic efficacy.

AREAS COVERED

In the current review, we aimed to provide a comprehensive insight into strategies used for MLT conjugations and modifications, cellular delivery of modified forms, and their clinical perspectives by reviewing the published literature on PubMed, Scopus, and Google Scholar databases. We also emphasized the MLT structure modifications, mechanism of action, and cellular toxicity.

EXPERT OPINION

Developing new analogs and conjugates of MLT as a natural drug with improved functions and fewer side effects is crucial for the clinical translation of this approach worldwide, especially where the chemicals and synthetic drugs are more expensive or unavailable in the healthcare system. MLT-nanoconjugation may be one of the best-optimized strategies for improving peptide delivery, increasing its therapeutic efficacy, and providing minimal nonspecific cellular lytic activity. [Figure: see text].

Modulation of free energy landscapes as a strategy for the design of antimicrobial peptides

Computational design of antimicrobial peptides (AMPs) is a promising area of research for developing novel agents against drug-resistant bacteria. AMPs are present naturally in many organisms, from bacteria to humans, a time-tested mechanism that makes them attractive as effective antibiotics. Depending on the environment, AMPs can exhibit α-helical or β-sheet conformations, a mix of both, or lack secondary structure; they can be linear or cyclic. Prediction of their structures is challenging but critical for rational design. Promising AMP leads can be developed using essentially two approaches: traditional modeling of the physicochemical mechanisms that determine peptide behavior in aqueous and membrane environments and knowledge-based, e.g., machine learning (ML) techniques, that exploit ever-growing AMP databases. Here, we explore the conformational landscapes of two recently ML-designed AMPs, characterize the dependence of these landscapes on the medium conditions, and identify features in peptide and membrane landscapes that mediate protein-membrane association. For both peptides, we observe greater conformational diversity in an aqueous solvent than in a less polar solvent, and one peptide is seen to alter its conformation more dramatically than the other upon the change of solvent. Our results support the view that structural rearrangement in response to environmental changes is central to the mechanism of membrane-structure disruption by linear peptides. We expect that the design of AMPs by ML will benefit from the incorporation of peptide conformational substates as quantified here with molecular simulations.

An Efficient Evaluation System Accelerates α-Helical Antimicrobial Peptide Discovery and Its Application to Global Human Genome Mining

Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15–25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli, drug-resistance Escherichia coli, and Staphylococcus aureus, respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics.

Novel Secreted Peptides From Rhizopus arrhizus var. delemar With Immunomodulatory Effects That Enhance Fungal Pathogenesis

Secreted fungal peptides are known to influence the interactions between the pathogen and host innate immunity. The aim of this study is to screen and evaluate secreted peptides from the fungus Rhizopus arrhizus var. delemar for their immunomodulatory activity. By using mass spectrometry and immuno-informatics analysis, we identified three secreted peptides CesT (S16), Colicin (S17), and Ca2+/calmodulin-dependent protein kinase/ligand (CAMK/CAMKL; S27). Culturing peripheral blood-derived monocytic macrophages (PBMMs) in the presence of S16 or S17 caused cell clumping, while culturing them with S27 resulted in the formation of spindle-shaped cells. S27-treated PBMMs showed cell cycle arrest at G0 phase and exhibited alternatively activated macrophage phenotype with pronounced reduction in scavenger receptors CD163 and CD206. Homology prediction indicated that IL-4/IL-13 is the immunomodulatory target of S27. Confirming this prediction, S27 initiated macrophage activation through phosphorylation of STAT-6; STAT-6 inhibition reversed the activity of S27 and reduced the formation of spindle-shaped PBMMs. Lastly, S27 treatment of PBMMs was associated with altered expression of key iron regulatory genes including hepcidin, ferroportin, transferrin receptor 1, and ferritin in a pattern consistent with increased cellular iron release; a condition known to enhance Rhizopus infection. Collectively, R. arrhizus var. delemar secretes peptides with immunomodulatory activities that support fungal pathogenesis. Targeting the IL-4/IL-13R/STAT-6 axis is a potential therapeutic approach to enhance the PBMM-mediated fungal phagocytosis. This represents a potential new approach to overcome lethal mucormycosis.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

Novel Antimicrobial Peptides Designed Using a Recurrent Neural Network Reduce Mortality in Experimental Sepsis

The search and development of new antibiotics is relevant due to widespread antibiotic resistance. One of the promising strategies is the de novo design of novel antimicrobial peptides. The amino acid sequences of 198 novel peptides were obtained using a generative long short-term memory recurrent neural network (LSTM RNN). To assess their antimicrobial effect, we synthesized five out of 198 generated peptides. The PEP-38 and PEP-137 peptides were active in vitro against carbapenem-resistant isolates of Klebsiella aerogenes and K. pneumoniae. PEP-137 was also active against Pseudomonas aeruginosa. The remaining three peptides (PEP-36, PEP-136 and PEP-174) showed no antibacterial effect. Then the effect of PEP-38 and PEP-137 (a single intraperitoneal administration of a 100 μg dose 30 min after infection) on animal survival in an experimental murine model of K. pneumoniae-induced sepsis was investigated. As a control, two groups of mice were used: one received sterile saline, and the other received inactive in vitro PEP-36 (a single 100 μg dose). The PEP-36 peptide was shown to provide the highest survival rate (66.7%). PEP-137 showed a survival rate of 50%. PEP-38 was found to be ineffective. The data obtained can be used to develop new antibacterial peptide drugs to combat antibiotic resistance.

Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment

BACKGROUND

Computer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions.

METHODS

The system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales.

RESULTS

The model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods.

CONCLUSION

Our results demonstrate the system's applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives.

GENERAL SIGNIFICANCE

The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis.

AVAILABILITY

The system was implemented as a web application, available at .

Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques

Background

Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed.

Results

In this study, by using an up-to-date dataset, a machine learning model has been trained successfully to predict the toxicity of antimicrobial peptides. The comprehensive set of features of both physico-chemical and linguistic-based with local and global essences have undergone feature selection to identify key properties behind toxicity of antimicrobial peptides. After feature selection, the hybrid model showed the best performance with a recall of 0. 876 and a F1 score of 0. 849.

Conclusions

The obtained model can be useful in extracting AMPs with low toxicity from AMP libraries in clinical applications. On the other hand, several properties with local nature including positions of strand forming and hydrophobic residues in final selected features show that these properties are critical definer of peptide properties and should be considered in developing models for activity prediction of peptides. The executable code is available at https://git.io/JRZaT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04468-y.

Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question).

Antimicrobial Peptides from Plants: A cDNA-Library Based Isolation, Purification, Characterization Approach and Elucidating Their Modes of Action

Even in a natural ecosystem, plants are continuously threatened by various microbial diseases. To save themselves from these diverse infections, plants build a robust, multilayered immune system through their natural chemical compounds. Among the several crucial bioactive compounds possessed by plants’ immune systems, antimicrobial peptides (AMPs) rank in the first tier. These AMPs are environmentally friendly, anti-pathogenic, and do not bring harm to humans. Antimicrobial peptides can be isolated in several ways, but recombinant protein production has become increasingly popular in recent years, with the Escherichia coli expression system being the most widely used. However, the efficacy of this expression system is compromised due to the difficulty of removing endotoxin from its system. Therefore, this review suggests a high-throughput cDNA library-based plant-derived AMP isolation technique using the Bacillus subtilis expression system. This method can be performed for large-scale screening of plant sources to classify unique or homologous AMPs for the agronomic and applied field of plant studies. Furthermore, this review also focuses on the efficacy of plant AMPs, which are dependent on their numerous modes of action and exceptional structural stability to function against a wide range of invaders. To conclude, the findings from this study will be useful in investigating how novel AMPs are distributed among plants and provide detailed guidelines for an effective screening strategy of AMPs.

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

Prediction and Characterization of Cationic Arginine-Rich Plant Antimicrobial Peptide SM-985 From Teosinte (Zea mays ssp. mexicana)

Antimicrobial peptides (AMPs) are effective against different plant pathogens and newly considered as part of plant defense systems. From prokaryotes to eukaryotes, AMPs can exist in all forms of life. SM-985 is a cationic AMP (CAMP) isolated from the cDNA library of Mexican teosinte (Zea mays ssp. mexicana). A computational prediction server running with different algorithms was used to screen the teosinte cDNA library for AMPs, and the SM-985 peptide was predicted as an AMP with high probability prediction values. SM-985 is an arginine-rich peptide and composed of 21 amino acids (MW: 2671.06 Da). The physicochemical properties of SM-985 are very promising as an AMP, including the net charge (+8), hydrophobicity ratio of 23%, Boman index of 5.19 kcal/mol, and isoelectric point of 12.95. The SM-985 peptide has amphipathic α-helix conformations. The antimicrobial activity of SM-985 was confirmed against six bacterial plant pathogens, and the MIC of SM-985 against Gram-positive indicators was 8 μM, while the MIC of SM-985 against Gram-negative indicators was 4 μM. The SM-985 interacting with the bacterial membrane and this interaction were examined by treatment of the bacterial indicators with FITC-SM-985 peptide, which showed a high binding affinity of SM-985 to the bacterial membrane (whether Gram-positive or Gram-negative). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images of the treated bacteria with SM-985 demonstrated cell membrane damage and cell lysis. In vivo antimicrobial activity was examined, and SM-985 prevented leaf spot disease infection caused by Pst DC3000 on Solanum lycopersicum. Moreover, SM-985 showed sensitivity to calcium chloride salt, which is a common feature of CAMPs.

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.

Toward insights on determining factors for high activity in antimicrobial peptides via machine learning

The continued and general rise of antibiotic resistance in pathogenic microbes is a well-recognized global threat. Host defense peptides (HDPs), a component of the innate immune system have demonstrated promising potential to become a next generation antibiotic effective against a plethora of pathogens. While the effectiveness of antimicrobial HDPs has been extensively demonstrated in experimental studies, theoretical insights on the mechanism by which these peptides function is comparably limited. In particular, experimental studies of AMP mechanisms are limited in the number of different peptides investigated and the type of peptide parameters considered. This study makes use of the random forest algorithm for classifying the antimicrobial activity as well for identifying molecular descriptors underpinning the antimicrobial activity of investigated peptides. Subsequent manual interpretation of the identified important descriptors revealed that polarity-solubility are necessary for the membrane lytic antimicrobial activity of HDPs.

Collection of antimicrobial peptides database and its derivatives: Applications and beyond

Collection of antimicrobial peptides (CAMP), CAMPSign, and ClassAMP are open-access resources that have been developed to enhance research on antimicrobial peptides (AMPs). Comprehensive information on AMPs and machine learning-based predictive models are made available for users through these resources. As of date, CAMP_R3 has 10,247 sequences, 757 structures, and 114 family-specific signatures of AMPs along with associated tools for AMP sequence and structure analysis. CAMPSign uses family-specific sequence conservation, in the form of patterns and hidden Markov models for identification of AMPs. ClassAMP can be used to classify AMPs as antibacterial, antifungal, or antiviral based on sequence information. Here we describe CAMP and its derivatives and illustrate, with a few examples, the contribution of these online resources to the advancement of our current understanding of AMPs.

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20-mer candidate peptides and scored them for charge, α-helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

Comment on: ‘Empirical comparison of web-based antimicrobial peptide prediction tools’

Supplementary information: Supplementary data are available at Bioinformatics online.

Antimicrobial cell penetrating peptides with bacterial cell specificity: pharmacophore modelling, quantitative structure activity relationship and molecular dynamics simulation

Current research has shown cell-penetrating peptides and antimicrobial peptides (AMPs) as probable vectors for use in drug delivery and as novel antibiotics. It has been reported that the higher the therapeutic index (TI) the higher would be the bacterial cell penetrating ability. To the best of our knowledge, no in-silico study has been performed to determine bacterial cell specificity of the antimicrobial cell penetrating peptides (aCPP's) based on their TI. The aim of this study was to develop a quantitative structure activity relationship (QSAR) model, which can estimate antimicrobial potential and cell-penetrating ability of aCPPs against S. aureus, to confirm the relationship between the TI and aCPPs and to identify specific descriptors responsible for aCPPs penetrating ability. Molecular dynamics (MD) simulation was also performed to confirm the membrane insertion of the most active aCPPs obtained from the QSAR study. The most appropriate pharmacophore was identified to predict the aCPP's activity. The statistical results confirmed the validity of the model. The QSAR model was successful in identifying the optimal aCPP with high activity prediction and provided insights into the structural requirements to correlate their TI to cell penetrating ability. MD simulation of the best aCPP with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer confirmed its interaction with the membrane and the C-terminal residues of the aCPP played a key role in membrane penetration. The strategy of combining QSAR and molecular dynamics, allowed for optimal estimation of ligand-target interaction and confirmed the importance of Trp and Lys in interacting with the POPC bilayer. Communicated by Ramaswamy H. Sarma.

Empirical comparison of web-based antimicrobial peptide prediction tools

Motivation

Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.

Results

We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks.

Contact

gaberemu@ngha.med.sa or william-noble@uw.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Designing Antibacterial Peptides with Enhanced Killing Kinetics

Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide.

Small bacterial and phagic proteins: an updated view on a rapidly moving field

Small proteins, that is, polypeptides of 50 amino acids (aa) or less, are increasingly recognized as important regulators in bacteria. Secreted or not, their small size make them versatile proteins, involved in a wide range of processes. They may allow bacteria to sense and to respond to stresses, to send signals and communicate, and to modulate infections. Bacteriophages also produce small proteins to influence lysogeny/lysis decisions. In this review, we update the present view on small proteins functions, and discuss their possible applications.

Myticalins: A Novel Multigenic Family of Linear, Cationic Antimicrobial Peptides from Marine Mussels (Mytilus spp.)

The application of high-throughput sequencing technologies to non-model organisms has brought new opportunities for the identification of bioactive peptides from genomes and transcriptomes. From this point of view, marine invertebrates represent a potentially rich, yet largely unexplored resource for de novo discovery due to their adaptation to diverse challenging habitats. Bioinformatics analyses of available genomic and transcriptomic data allowed us to identify myticalins, a novel family of antimicrobial peptides (AMPs) from the mussel Mytilus galloprovincialis, and a similar family of AMPs from Modiolus spp., named modiocalins. Their coding sequence encompasses two conserved N-terminal (signal peptide) and C-terminal (propeptide) regions and a hypervariable central cationic region corresponding to the mature peptide. Myticalins are taxonomically restricted to Mytiloida and they can be classified into four subfamilies. These AMPs are subject to considerable interindividual sequence variability and possibly to presence/absence variation. Functional assays performed on selected members of this family indicate a remarkable tissue-specific expression (in gills) and broad spectrum of activity against both Gram-positive and Gram-negative bacteria. Overall, we present the first linear AMPs ever described in marine mussels and confirm the great potential of bioinformatics tools for the de novo discovery of bioactive peptides in non-model organisms.

Myticalins: A Novel Multigenic Family of Linear, Cationic Antimicrobial Peptides from Marine Mussels (Mytilus spp.)

The application of high-throughput sequencing technologies to non-model organisms has brought new opportunities for the identification of bioactive peptides from genomes and transcriptomes. From this point of view, marine invertebrates represent a potentially rich, yet largely unexplored resource for de novo discovery due to their adaptation to diverse challenging habitats. Bioinformatics analyses of available genomic and transcriptomic data allowed us to identify myticalins, a novel family of antimicrobial peptides (AMPs) from the mussel Mytilus galloprovincialis, and a similar family of AMPs from Modiolus spp., named modiocalins. Their coding sequence encompasses two conserved N-terminal (signal peptide) and C-terminal (propeptide) regions and a hypervariable central cationic region corresponding to the mature peptide. Myticalins are taxonomically restricted to Mytiloida and they can be classified into four subfamilies. These AMPs are subject to considerable interindividual sequence variability and possibly to presence/absence variation. Functional assays performed on selected members of this family indicate a remarkable tissue-specific expression (in gills) and broad spectrum of activity against both Gram-positive and Gram-negative bacteria. Overall, we present the first linear AMPs ever described in marine mussels and confirm the great potential of bioinformatics tools for the de novo discovery of bioactive peptides in non-model organisms.

Effect of physicochemical properties of peptides from soy protein on their antimicrobial activity

Antimicrobial peptides (AMPs) kill microbial cells through insertion and damage/permeabilization of the cytoplasmic cell membranes and has applications in food safety and antibiotic replacement. Soy protein is an attractive, abundant natural source for commercial production of AMPs. In this research, explicit solvent molecular dynamics (MD) simulation was employed to investigate the effects of (i) number of total and net charges, (ii) hydrophobicity (iii) hydrophobic moment and (iv) helicity of peptides from soy protein on their ability to bind to lipid bilayer and their transmembrane aggregates to form pores. Interaction of possible AMP segments from soy protein with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPC/POPG) bilayers, a mimic of bacterial cell membrane, was investigated. Pore formation was insensitive to helicity and occurred for hydrophobicity threshold in the range of -0.3-0kcal/mol, hydrophobic moment threshold of 0.3kcal/mol, net charge threshold of 2. Though low hydrophobicity and high number of charges help in the formation of water channel for transmembrane aggregates, insertion of peptides with these properties requires overcome of energy barrier, as shown by potential of mean force calculations, thereby resulting in low antimicrobial activity. Experimental evaluation of antimicrobial activity of these peptides against Gram positive L. monocytogenes and Gram negative E. coli as obtained by spot-on-lawn assay was consistent with simulation results. These results should help in the development of guidelines for selection of peptides with antimicrobial activity based on their physicochemical properties.

PepBio: predicting the bioactivity of host defense peptides

A large-scale QSAR study of host defense peptides sheds light on the origin of their bioactivities (antibacterial, anticancer, antiviral and antifungal).

Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types

MOTIVATION

With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional types has led to many studies, and various predictors using machine learning have been developed. However, there is room for improvement; in particular, no predictor takes into account the lack of balance among different functional AMPs.

RESULTS

In this paper, a new synthetic minority over-sampling technique on imbalanced and multi-label datasets, referred to as ML-SMOTE, was designed for processing and identifying AMPs' functional families. A novel multi-label classifier, MLAMP, was also developed using ML-SMOTE and grey pseudo amino acid composition. The classifier obtained 0.4846 subset accuracy and 0.16 hamming loss.

AVAILABILITY AND IMPLEMENTATION

A user-friendly web-server for MLAMP was established at http://www.jci-bioinfo.cn/MLAMP CONTACTS: linweizhong@jci.edu.cn or xudong@missouri.edu.

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens

Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.

DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides

Antimicrobial peptides (AMPs) are anti-infectives that may represent a novel and untapped class of biotherapeutics. Increasing interest in AMPs means that new peptides (natural and synthetic) are discovered faster than ever before. We describe herein a new version of the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2, which is freely accessible at http://dbaasp.org). This iteration of the database reports chemical structures and empirically-determined activities (MICs, IC50, etc.) against more than 4200 specific target microbes for more than 2000 ribosomal, 80 non-ribosomal and 5700 synthetic peptides. Of these, the vast majority are monomeric, but nearly 200 of these peptides are found as homo- or heterodimers. More than 6100 of the peptides are linear, but about 515 are cyclic and more than 1300 have other intra-chain covalent bonds. More than half of the entries in the database were added after the resource was initially described, which reflects the recent sharp uptick of interest in AMPs. New features of DBAASPv.2 include: (i) user-friendly utilities and reporting functions, (ii) a ‘Ranking Search’ function to query the database by target species and return a ranked list of peptides with activity against that target and (iii) structural descriptions of the peptides derived from empirical data or calculated by molecular dynamics (MD) simulations. The three-dimensional structural data are critical components for understanding structure–activity relationships and for design of new antimicrobial drugs. We created more than 300 high-throughput MD simulations specifically for inclusion in DBAASP. The resulting structures are described in the database by novel trajectory analysis plots and movies. Another 200+ DBAASP entries have links to the Protein DataBank. All of the structures are easily visualized directly in the web browser.

Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes

Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).

DBAASP: database of antimicrobial activity and structure of peptides

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is a manually curated database for those peptides for which antimicrobial activity against particular targets has been evaluated experimentally. The database is a depository of complete information on: the chemical structure of peptides; target species; target object of cell; peptide antimicrobial/haemolytic/cytotoxic activities; and experimental conditions at which activities were estimated. The DBAASP search page allows the user to search peptides according to their structural characteristics, complexity type (monomer, dimer and two-peptide), source, synthesis type (ribosomal, nonribosomal and synthetic) and target species. The database prediction algorithm provides a tool for rational design of new antimicrobial peptides. DBAASP is accessible at http://www.biomedicine.org.ge/dbaasp/.