Antimicrobial peptides design by evolutionary multiobjective optimization

Development strategies and application of antimicrobial peptides as future alternatives to in-feed antibiotics

The use of in-feed antibiotics has been widely restricted due to the significant environmental pollution and food safety concerns they have caused. Antimicrobial peptides (AMPs) have attracted widespread attention as potential future alternatives to in-feed antibiotics owing to their demonstrated antimicrobial activity and environment friendly characteristics. However, the challenges of weak bioactivity, immature stability, and low production yields of natural AMPs impede practical application in the feed industry. To address these problems, efforts have been made to develop strategies for approaching the AMPs with enhanced properties. Herein, we summarize approaches to improving the properties of AMPs as potential alternatives to in-feed antibiotics, mainly including optimization of structural parameters, sequence modification, selection of microbial hosts, fusion expression, and industrially fermentation control. Additionally, the potential for application of AMPs in animal husbandry is discussed. This comprehensive review lays a strong theoretical foundation for the development of in-feed AMPs to achieve the public health globally.

sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

Unnatural amino acids: promising implications for the development of new antimicrobial peptides

The increasing incidence and rapid spread of bacterial resistance to conventional antibiotics are a serious global threat to public health, highlighting the need to develop new antimicrobial alternatives. Antimicrobial peptides (AMPs) represent a class of promising natural antibiotic candidates due to their broad-spectrum activity and low tendency to induce resistance. However, the development of AMPs for medical use is hampered by several obstacles, such as moderate activity, lability to proteolytic degradation, and low bioavailability. To date, many researchers have focussed on the optimization or design of novel artificial AMPs with desired properties. Unnatural amino acids (UAAs) are valuable building blocks in the manufacture of a variety of pharmaceuticals, and have been used to develop artificial AMPs with specific structural and physicochemical properties. Rational incorporation of UAAs has become a very promising approach to endow AMPs with strong and long-lasting activity but no toxicity. This review aims to summarize key approaches that have been used to incorporate UAAs to develop novel AMPs with improved properties and better performance. It is anticipated that this review will guide future design considerations for UAA-based antimicrobial applications.

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Impact of ANN in Revealing of Viral Peptides

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.

In silico and experimental validation of a new modified arginine-rich cell penetrating peptide for plasmid DNA delivery

Cell-penetrating peptides (CPPs) attracted great attention because of the capability to deliver various types of cargo molecules across into the cells. In this study, we presented a new arginine rich CPP, named MR, for efficient transporting plasmid DNA. We used a combined bioinformatic-based approach to improve the speed and accuracy of CPP evaluation. MR protein properties, structural models, interaction with DNA, as well as cell localization and membrane interaction were evaluated through multiple servers. Importantly, analysis using different algorithms showed the high CPP prediction confidence of MR. Experimental results also revealed the capacity of this gene delivery system in vitro for efficient plasmid DNA transfection. Additionally, in vitro mechanistically studies together with bioinformatic investigation suggested that MR peptide may internalize into the cell through endocytosis pathways. Moreover, in silico safety analysis such as immunogenicity, allergenicity, toxicity, and hemolysis activity as well as MTT assay also confirmed the safety of MR peptide. This study illustrated that MR peptide could be presented as remarkable potential gene delivery system for promising transport of plasmid DNA towards the therapeutic applications.

Identification of Bacterial Membrane Selectivity of Romo1-Derived Antimicrobial Peptide AMPR-22 via Molecular Dynamics

The abuse or misuse of antibiotics has caused the emergence of extensively drug-resistant (XDR) bacteria, rendering most antibiotics ineffective and increasing the mortality rate of patients with bacteremia or sepsis. Antimicrobial peptides (AMPs) are proposed to overcome this problem; however, many AMPs have attenuated antimicrobial activities with hemolytic toxicity in blood. Recently, AMPR-11 and its optimized derivative, AMPR-22, were reported to be potential candidates for the treatment of sepsis with a broad spectrum of antimicrobial activity and low hemolytic toxicity. Here, we performed molecular dynamics (MD) simulations to clarify the mechanism of lower hemolytic toxicity and higher efficacy of AMPR-22 at an atomic level. We found four polar residues in AMPR-11 bound to a model mimicking the bacterial inner/outer membranes preferentially over eukaryotic plasma membrane. AMPR-22 whose polar residues were replaced by lysine showed a 2-fold enhanced binding affinity to the bacterial membrane by interacting with bacterial specific lipids (lipid A or cardiolipin) via hydrogen bonds. The MD simulations were confirmed experimentally in models that partially mimic bacteremia conditions in vitro and ex vivo. The present study demonstrates why AMPR-22 showed low hemolytic toxicity and this approach using an MD simulation would be helpful in the development of AMPs.

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.

A review on antimicrobial peptides databases and the computational tools

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5–30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.

Machine learning designs non-hemolytic antimicrobial peptides

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.

The lexicon of antimicrobial peptides: a complete set of arginine and tryptophan sequences

Our understanding of the activity of cationic antimicrobial peptides (AMPs) has focused on well-characterized natural sequences, or limited sets of synthetic peptides designed de novo. We have undertaken a comprehensive investigation of the underlying primary structural features that give rise to the development of activity in AMPs. We consider a complete set of all possible peptides, up to 7 residues long, composed of positively charged arginine (R) and / or hydrophobic tryptophan (W), two features most commonly associated with activity. We found the shortest active peptides were 4 or 5 residues in length, and the overall landscapes of activity against gram-positive and gram-negative bacteria and a yeast were positively correlated. For all three organisms we found a single activity peak corresponding to sequences with around 40% R; the presence of adjacent W duplets and triplets also conferred greater activity. The mechanistic basis of these activities comprises a combination of lipid binding, particularly to negatively charged membranes, and additionally peptide aggregation, a mode of action previously uninvestigated for such peptides. The maximum specific antimicrobial activity appeared to occur in peptides of around 10 residues, suggesting ‘diminishing returns’ for developing larger peptides, when activity is considered per residue of peptide.

Clark et al. comprehensively explore the primary structural features underlying the activity of a complete set of antimicrobial peptides (AMPs). They find that the shortest active peptides were 4 or 5 residues in length, with activity being associated with 40% arginine, and multiple adjacent tryptophan residues. This study provides insights into the design of effective AMPs.

Engineering Selectively Targeting Antimicrobial Peptides

The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds with potentially attractive therapeutic properties, including the ability to target specific groups of bacteria. In nature, AMPs exhibit remarkable structural and functional diversity, which may be further enhanced through genetic engineering, high-throughput screening, and chemical modification strategies. In this review, we discuss the molecular mechanisms underlying AMP selectivity and highlight recent computational and experimental efforts to design selectively targeting AMPs. While there has been an extensive effort to find broadly active and highly potent AMPs, it remains challenging to design targeting peptides to discriminate between different bacteria on the basis of physicochemical properties. We also review approaches for measuring AMP activity, point out the challenges faced in assaying for selectivity, and discuss the potential for increasing AMP diversity through chemical modifications.

A Safe and Multitasking Antimicrobial Decapeptide: The Road from De Novo Design to Structural and Functional Characterization

Antimicrobial peptides (AMPs) are excellent candidates to fight multi-resistant pathogens worldwide and are considered promising bio-preservatives to control microbial spoilage through food processing. To date, designing de novo AMPs with high therapeutic indexes, low-cost synthesis, high resistance, and bioavailability, remains a challenge. In this study, a novel decapeptide, named RiLK1, was rationally designed starting from the sequence of the previously characterized AMP 1018-K6, with the aim of developing short peptides, and promoting higher selectivity over mammalian cells, antibacterial activity, and structural resistance under different salt, pH, and temperature conditions. Interestingly, RiLK1 displayed a broad-spectrum of bactericidal activity against Gram-positive and Gram-negative bacteria, including multidrug resistant clinical isolates of Salmonella species, with Minimal Bactericidal Concentration (MBC) values in low micromolar range, and it was effective even against two fungal pathogens with no evidence of cytotoxicity on human keratinocytes and fibroblasts. Moreover, RiLK1-activated polypropylene films were revealed to efficiently prevent the growth of microbial spoilage, possibly improving the shelf life of fresh food products. These results suggested that de novo designed peptide RiLK1 could be the first candidate for the development of a promising class of decameric and multitask antimicrobial agents to overcome drug-resistance phenomena.

Predicting Antimicrobial and Other Cysteine-Rich Peptides in 1267 Plant Transcriptomes

Antimicrobial peptides (AMPs) are a key component of innate immunity in various organisms including bacteria, insects, mammals, and plants. Their mode of action decreases the probability of developing resistance in pathogenic organisms, which makes them a promising object of study. However, molecular biology methods for searching for AMPs are laborious and expensive, especially for plants. Earlier, we developed a computational pipeline for identifying potential AMPs based on the cysteine motifs they usually possess. Since most motifs are too species-specific, a wide-scale screening of novel data is required to maintain the accuracy of searching algorithms. We have performed a search for potential AMPs in 1267 plant transcriptomes using our pipeline. On average, 50–150 peptides were revealed in each transcriptome. The data was verified by a BLASTp search in nr database to confirm peptide functions and by using random nucleotide sequences to estimate the fraction of erroneous predictions. The datasets obtained will be useful both for molecular biologists investigating AMPs in various organisms and for bioinformaticians developing novel algorithms of motif searching in transcriptomic and genomic sequences. The results obtained will represent a good reference point for future investigations in the fields mentioned above.

Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods

The delivery of drugs is a topic of intense research activity in both academia and industry with potential for positive economic, health, and societal impacts. The selection of the appropriate formulation (carrier and drug) with optimal delivery is a challenge investigated by researchers in academia and industry, in which millions of dollars are invested annually. Experiments involving different carriers and determination of their capacity for drug loading are very time-consuming and therefore expensive; consequently, approaches that employ computational/theoretical chemistry to speed have the potential to make hugely beneficial economic, environmental, and health impacts through savings in costs associated with chemicals (and their safe disposal) and time. Here, we report the use of computational tools (data mining of the available literature, principal component analysis, hierarchical clustering analysis, partial least squares regression, autocovariance calculations, molecular dynamics simulations, and molecular docking) to successfully predict drug loading into model drug delivery systems (gelatin nanospheres). We believe that this methodology has the potential to lead to significant change in drug formulation studies across the world.

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.

Combination of Gemcitabine with Cell-Penetrating Peptides: A Pharmacokinetic Approach Using In Silico Tools

Gemcitabine is an anticancer drug used to treat a wide range of solid tumors and is a first line treatment for pancreatic cancer. Our group has previously developed novel conjugates of gemcitabine with cell-penetrating peptides (CPP), and here we report some preliminary data regarding the pharmacokinetics of gemcitabine, two gemcitabine-CPP conjugates and respective CPP gathered from GastroPlus™, and analyze these results considering our previous evaluation of gemcitabine release and conjugates’ bioactivity. Additionally, seeking to shed some light on the relation between the penetration ability of CPP and their physicochemical properties, chemical descriptors for the 20 natural amino acids were calculated, a new principal property scale (z-scale) was created and CPP prediction models were developed, establishing quantitative structure-activity relationships (QSAR). The z-scores of the peptides conjugated with gemcitabine are presented and analyzed with the aforementioned data.

Antiviral peptides as promising therapeutic drugs

While scientific advances have led to large-scale production and widespread distribution of vaccines and antiviral drugs, viruses still remain a major cause of human diseases today. The ever-increasing reports of viral resistance and the emergence and re-emergence of viral epidemics pressure the health and scientific community to constantly find novel molecules with antiviral potential. This search involves numerous different approaches, and the use of antimicrobial peptides has presented itself as an interesting alternative. Even though the number of antimicrobial peptides with antiviral activity is still low, they already show immense potential to become pharmaceutically available antiviral drugs. Such peptides can originate from natural sources, such as those isolated from mammals and from animal venoms, or from artificial sources, when bioinformatics tools are used. This review aims to shed some light on antimicrobial peptides with antiviral activities against human viruses and update the data about the already well-known peptides that are still undergoing studies, emphasizing the most promising ones that may become medicines for clinical use.

S, Aprameya IV, Sarma SP, Chakravortty D, Chandra N. Ω76: A designed antimicrobial peptide to combat carbapenem- and tigecycline-resistant Acinetobacter baumannii

Drug resistance is a public health concern that threatens to undermine decades of medical progress. ESKAPE pathogens cause most nosocomial infections, and are frequently resistant to carbapenem antibiotics, usually leaving tigecycline and colistin as the last treatment options. However, increasing tigecycline resistance and colistin's nephrotoxicity severely restrict use of these antibiotics. We have designed antimicrobial peptides using a maximum common subgraph approach. Our best peptide (Ω76) displayed high efficacy against carbapenem and tigecycline-resistant Acinetobacter baumannii in mice. Mice treated with repeated sublethal doses of Ω76 displayed no signs of chronic toxicity. Sublethal Ω76 doses co-administered alongside sublethal colistin doses displayed no additive toxicity. These results indicate that Ω76 can potentially supplement or replace colistin, especially where nephrotoxicity is a concern. To our knowledge, no other existing antibiotics occupy this clinical niche. Mechanistically, Ω76 adopts an α-helical structure in membranes, causing rapid membrane disruption, leakage, and bacterial death.

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.

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a well-chosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

A Uniform In Vitro Efficacy Dataset to Guide Antimicrobial Peptide Design

Antimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data are now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms of Gram-negative, Gram-positive, mycobacterial, and fungal origin. We also present circular dichroism spectra for all antimicrobial peptides. We draw simple inferences from this data, and we discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.

Reassessing the Host Defense Peptide Landscape

Current research has demonstrated that small cationic amphipathic peptides have strong potential not only as antimicrobials, but also as antibiofilm agents, immune modulators, and anti-inflammatories. Although traditionally termed antimicrobial peptides (AMPs) these additional roles have prompted a shift in terminology to use the broader term host defense peptides (HDPs) to capture the multi-functional nature of these molecules. In this review, we critically examined the role of AMPs and HDPs in infectious diseases and inflammation. It is generally accepted that HDPs are multi-faceted mediators of a wide range of biological processes, with individual activities dependent on their polypeptide sequence. In this context, we explore the concept of chemical space as it applies to HDPs and hypothesize that the various functions and activities of this class of molecule exist on independent but overlapping activity landscapes. Finally, we outline several emerging functions and roles of HDPs and highlight how an improved understanding of these processes can potentially be leveraged to more fully realize the therapeutic promise of HDPs.

Cysmotif Searcher Pipeline for Antimicrobial Peptide Identification in Plant Transcriptomes

In this paper, we present the new Cysmotif searcher pipeline for identification of various antimicrobial peptides (AMPs), the most important components of innate immunity, in plant transcriptomes. Cysmotif searcher reveals and classifies short cysteine-rich amino acid sequences containing an open reading frame and a signal peptide cleavage site. Due to the combination of various search methods, Cysmotif searcher allows to obtain the most complete repertoire of AMPs for one or more transcriptomes in a short amount of time. The pipeline performance is estimated on the model plant Arabidopsis thaliana and nine other plants, including cultivated and wild species. The obtained results are compared to the existing annotation (A. thaliana) and results of conventional homology search (other plants). The comparison is carried out for known families of plant AMPs and newly discovered peptides that could not be assigned to existing families. The applicability of Cysmotif searcher in detecting new AMPs is discussed, and some practical recommendations on the pipeline usage for end users are given. The Cysmotif searcher pipeline is free for academic use and can be downloaded from Github (http://github.com/fallandar/cysmotifsearcher).

Machine learning antimicrobial peptide sequences: Some surprising variations on the theme of amphiphilic assembly

Antimicrobial peptides (AMPs) collectively constitute a key component of the host innate immune system. They span a diverse space of sequences and can be α-helical, β-sheet, or unfolded in structure. Despite a wealth of knowledge about them from decades of experiments, it remains difficult to articulate general principles governing such peptides. How are they different from other molecules that are also cationic and amphiphilic? What other functions, in immunity and otherwise, are enabled by these simple sequences? In this short review, we present some recent work that engages these questions using methods not usually applied to AMP studies, such as machine learning. We find that not only do AMP-like sequences confer membrane remodeling activity to an unexpectedly broad range of protein classes, their cationic and amphiphilic signature also allows them to act as meta-antigens and self-assemble with immune ligands into nanocrystalline complexes for multivalent presentation to Toll-like receptors.

Breaking the frontiers of cosmetology with antimicrobial peptides

Antimicrobial peptides (AMPs) are mostly endogenous, cationic, amphipathic polypeptides, produced by many natural sources. Recently, many biological functions beyond antimicrobial activity have been attributed to AMPs, and some of these have attracted the attention of the cosmetics industry. AMPs have revealed antioxidant, self-renewal and pro-collagen effects, which are desirable in anti-aging cosmetics. Additionally, AMPs may also be customized to act on specific cellular targets. Here, we review the recent literature that highlights the many possibilities presented by AMPs, focusing on the relevance and impact that this potentially novel class of active cosmetic ingredients might have in the near future, creating new market outlooks for the cosmetic industry with these molecules as a viable alternative to conventional cosmetics.

Joker: An algorithm to insert patterns into sequences for designing antimicrobial peptides

Innovative alternatives to control bacterial infections are need due to bacterial resistance rise. Antimicrobial peptides (AMPs) have been considered as the new generation of antimicrobial agents. Based on the fact that AMPs are sequence-dependent, a linguistic model for designing AMPs was previously developed, considering AMPs as a formal language with a grammar (patterns or motifs) and a vocabulary (amino acids). Albeit promising, that model has been poorly exploited mainly because thousands of sequences need to be generated, and the outcome has high similarity to already known AMPs. Here we present Joker, an innovative algorithm that improves the application of the linguistic model for rational design of antimicrobial peptides. We modelled the AMPs as a card game, where Joker combines the cards in the hand (patterns) with the cards in the table (sequence templates), generating a few variants. Our algorithm is capable of improving existing AMPs or even creating new AMPs from inactive peptides. A standalone version of Joker is available for download at <http://github.com/williamfp7/Joker> and requires a Linux 32-bit machine.

In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or  high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics.

Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.

Machine learning and data science in soft materials engineering

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria

There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum β-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.

What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.

Machine learning-enabled discovery and design of membrane-active peptides

Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action.

Analogs of the Frog-skin Antimicrobial Peptide Temporin 1Tb Exhibit a Wider Spectrum of Activity and a Stronger Antibiofilm Potential as Compared to the Parental Peptide

The frog skin-derived peptide Temporin 1Tb (TB) has gained increasing attention as novel antimicrobial agent for the treatment of antibiotic-resistant and/or biofilm-mediated infections. Nevertheless, such a peptide possesses a preferential spectrum of action against Gram-positive bacteria. In order to improve the therapeutic potential of TB, the present study evaluated the antibacterial and antibiofilm activities of two TB analogs against medically relevant bacterial species. Of the two analogs, TB_KKG6A has been previously described in the literature, while TB_L1FK is a new analog designed by us through statistical-based computational strategies. Both TB analogs displayed a faster and stronger bactericidal activity than the parental peptide, especially against Gram-negative bacteria in planktonic form. Differently from the parental peptide, TB_KKG6A and TB_L1FK were able to inhibit the formation of Staphylococcus aureus biofilms by more than 50% at 12 μM, while only TB_KKG6A prevented the formation of Pseudomonas aeruginosa biofilms at 24 μM. A marked antibiofilm activity against preformed biofilms of both bacterial species was observed for the two TB analogs when used in combination with EDTA. Analysis of synergism at the cellular level suggested that the antibiofilm activity exerted by the peptide-EDTA combinations against mature biofilms might be due mainly to a disaggregating effect on the extracellular matrix in the case of S. aureus, and to a direct activity on biofilm-embedded cells in the case of P. aeruginosa. Both analogs displayed a low hemolytic effect at the active concentrations and, overall, TB_L1FK resulted less cytotoxic toward mammalian cells. Collectively, the results obtained demonstrated that subtle changes in the primary sequence of TB may provide TB analogs that, used alone or in combination with adjuvant molecules such as EDTA, exhibit promising features against both planktonic and biofilm cells of medically relevant bacteria.

Mapping membrane activity in undiscovered peptide sequence space using machine learning

There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance σ from the SVM hyperplane (thus maximize its "antimicrobialness") and its ⍺-helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric σ correlates not with a peptide's minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences.

Antibiofilm Peptides: Potential as Broad-Spectrum Agents

The treatment of bacterial diseases is facing twin threats, with increasing bacterial antibiotic resistance and relatively few novel compounds or strategies under development or entering the clinic. Bacteria frequently grow on surfaces as biofilm communities encased in a polymeric matrix. The biofilm mode of growth is associated with 65 to 80% of all clinical infections. It causes broad adaptive changes; biofilm bacteria are especially (10- to 1,000-fold) resistant to conventional antibiotics and to date no antimicrobials have been developed specifically to treat biofilms. Small synthetic peptides with broad-spectrum antibiofilm activity represent a novel approach to treat biofilm-related infections. Recent developments have provided evidence that these peptides can inhibit even developed biofilms, kill multiple bacterial species in biofilms (including the ESKAPE [Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species] pathogens), show strong synergy with several antibiotics, and act by targeting a universal stress response in bacteria. Thus, these peptides represent a promising alternative treatment to conventional antibiotics and work effectively in animal models of biofilm-associated infections.

A Requirement for Metamorphic Interconversion in the Antimicrobial Activity of Chemokine XCL1

Chemokines make up a superfamily of ∼50 small secreted proteins (8-12 kDa) involved in a host of physiological processes and disease states, with several previously shown to have direct antimicrobial activity comparable to that of defensins in efficacy. XCL1 is a unique metamorphic protein that interconverts between the canonical chemokine fold and a novel all-β-sheet dimer. Phylogenetic analysis suggests that, within the chemokine family, XCL1 is most closely related to CCL20, which exhibits antibacterial activity. The in vitro antimicrobial activity of WT-XCL1 and structural variants was quantified using a radial diffusion assay (RDA) and in solution bactericidal assays against Gram-positive and Gram-negative species of bacteria. Comparisons of WT-XCL1 with variants that limit metamorphic interconversion showed a loss of antimicrobial activity when restricted to the conserved chemokine fold. These results suggest that metamorphic folding of XCL1 is required for potent antimicrobial activity.

Critical Evaluation and Compilation of Physicochemical Determinants and Membrane Interactions of MMGP1 Antifungal Peptide

A growing issue of pathogen resistance to antibiotics has fostered the development of innovative approaches for novel drug development. Here, we report the physicochemical and biological properties of an antifungal peptide, MMGP1, based on computational analysis. Computation of physicochemical properties has revealed that the natural biological activities of MMGP1 are coordinated by its intrinsic properties such as net positive charge (+5.04), amphipathicity, high hydrophobicity, low hydrophobic moment, and higher isoelectric point (11.915). Prediction of aggregation hot spots in MMGP1 had revealed the presence of potentially aggregation-prone segments that can nucleate in vivo aggregation (on the membrane), whereas no aggregating regions were predicted for in vitro aggregation (in solutions) of MMGP1. This ability of MMGP1 to form oligomeric aggregates on membrane further substantiates its direct-cell penetrating potency. Monte Carlo simulation of the interactions of MMGP1 in the aqueous phase and different membrane environments revealed that increasing the proportion of acidic lipids on membrane had led to increase in the peptide helicity. Furthermore, the peptide adopts energetically favorable transmembrane configuration, by inserting peptide loop and helix termini into the membrane containing >60% of anionic lipids. The charged lipid-based insertion of MMGP1 into membrane might be responsible for the selectivity of peptide toward fungal cells. Additionally, MMGP1 possessed DNA-binding property. Computational docking has identified DNA-binding residues (TRP3, SER4, MET7, ARG8, PHE10, ALA11, GLY20, THR21, ARG22, MET23, TRP34, and LYS36) in MMGP1 crucial for its DNA-binding property. Furthermore, computational mutation analysis revealed that aromatic amino acids are crucial for in vivo aggregation, membrane insertion, and DNA-binding property of MMGP1. These data provide new insight into the molecular determinants of MMGP1 antifungal activity and also serves as the template for the design of novel peptide antibiotics.

dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides

Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/.

Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications

Biopharmaceuticals hold great promise for the future of drug discovery. Nevertheless, rational drug design strategies are mainly focused on the discovery of small synthetic molecules. Herein we present matched peptides, an innovative analysis technique for biological data related to peptide and protein sequences. It represents an extension of matched molecular pair analysis toward macromolecular sequence data and allows quantitative predictions of the effect of single amino acid substitutions on the basis of statistical data on known transformations. We demonstrate the application of matched peptides to a data set of major histocompatibility complex class II peptide ligands and discuss the trends captured with respect to classical quantitative structure–activity relationship approaches as well as structural aspects of the investigated protein–peptide interface. We expect our novel readily interpretable tool at the interface of cheminformatics and bioinformatics to support the rational design of biopharmaceuticals and give directions for further development of the presented methodology.

Antimicrobial peptides and their interaction with biofilms of medically relevant bacteria

Biofilm-associated infections represent one of the major threats of modern medicine. Biofilm-forming bacteria are encased in a complex mixture of extracellular polymeric substances (EPS) and acquire properties that render them highly tolerant to conventional antibiotics and host immune response. Therefore, there is a pressing demand of new drugs active against microbial biofilms. In this regard, antimicrobial peptides (AMPs) represent an option taken increasingly in consideration. After dissecting the peculiar biofilm features that may greatly affect the development of new antibiofilm drugs, the present article provides a general overview of the rationale behind the use of AMPs against biofilms of medically relevant bacteria and on the possible mechanisms of AMP-antibiofilm activity. An analysis of the interactions of AMPs with biofilm components, especially those constituting the EPS, and the obstacles and/or opportunities that may arise from such interactions in the development of new AMP-based antibiofilm strategies is also presented and discussed. This article is part of a Special Issue entitled: Antimicrobial Peptides edited by Karl Lohner and Kai Hilpert.

BaAMPs: the database of biofilm-active antimicrobial peptides

Antimicrobial peptides (AMPs) are increasingly being considered as novel agents against biofilms. The development of AMP-based anti-biofilm strategies strongly relies on the design of sequences optimized to target specific features of sessile bacterial/fungal communities. Although several AMP databases have been created and successfully exploited for AMP design, all of these use data collected on peptides tested against planktonic microorganisms. Here, an open-access, manually curated database of AMPs specifically assayed against microbial biofilms (BaAMPs) is presented for the first time. In collecting relevant data from the literature an effort was made to define a minimal standard set of essential information including, for each AMP, the microbial species and biofilm conditions against which it was tested, and the specific assay and peptide concentration used. The availability of these data in an organized framework will benefit anti-biofilm research and support the design of novel molecules active against biofilm. BaAMPs is accessible at http://www.baamps.it.

In silico design of antimicrobial peptides

The rapid spread of drug-resistant pathogenic microbial strains has created an urgent need for the development of new anti-infective molecules, having different mechanism of action in comparison to existing drugs. Natural antimicrobial peptides (AMPs) represent a novel class of molecules with a broad spectrum of activity and a low rate in inducing bacterial resistance. In particular, linear alpha-helical cationic antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement of the innate defense against microbes. However, until now, many AMPs have failed in clinical trials because of several drawbacks that strongly limit their applicability such as degradation, cytotoxicity, and high production cost. Thus, to overcome the limitations of native peptides, a rational in silico approach to AMPs design becomes a promising strategy that drastically reduce production costs and the time required for evaluation of activity and toxicity. This chapter focuses on the strategies and methods for de novo design of potentially active AMPs. In particular, statistical-based design strategies and MD methods for modelling AMPs are elucidated.

Distinct profiling of antimicrobial peptide families

Motivation: The increased prevalence of multi-drug resistant (MDR) pathogens heightens the need to design new antimicrobial agents. Antimicrobial peptides (AMPs) exhibit broad-spectrum potent activity against MDR pathogens and kills rapidly, thus giving rise to AMPs being recognized as a potential substitute for conventional antibiotics. Designing new AMPs using current in-silico approaches is, however, challenging due to the absence of suitable models, large number of design parameters, testing cycles, production time and cost. To date, AMPs have merely been categorized into families according to their primary sequences, structures and functions. The ability to computationally determine the properties that discriminate AMP families from each other could help in exploring the key characteristics of these families and facilitate the in-silico design of synthetic AMPs.

Results: Here we studied 14 AMP families and sub-families. We selected a specific description of AMP amino acid sequence and identified compositional and physicochemical properties of amino acids that accurately distinguish each AMP family from all other AMPs with an average sensitivity, specificity and precision of 92.88%, 99.86% and 95.96%, respectively. Many of our identified discriminative properties have been shown to be compositional or functional characteristics of the corresponding AMP family in literature. We suggest that these properties could serve as guides for in-silico methods in design of novel synthetic AMPs. The methodology we developed is generic and has a potential to be applied for characterization of any protein family.

Contact:vladimir.bajic@kaust.edu.sa

Supplementary information:Supplementary data are available at Bioinformatics online.