Bacterial resistance to antimicrobial peptides

Antimicrobial peptides (AMPs) or host defense peptides (HDPs) are vital components of human innate defense system targeting human‐related bacteria. Many bacteria have various mechanisms interfering with AMP activity, causing resistance to AMPs. Since AMPs are considered as potential novel antimicrobial drugs, understanding the mechanisms of bacterial resistance to direct killing of AMPs is of great significance. In this review, a comparative overview of bacterial strategies for resistance to direct killing of various AMPs is presented. Such strategies include bacterial cell envelope modification, AMP degradation, sequestration, expelling, and capsule.     

Computational resources and tools for antimicrobial peptides

Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure–activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.     

Detecting antimicrobial peptides by exploring the mutual information of their sequences

Abstract

The rise of antibiotic resistance in pathogenic bacteria is a growing concern for every part of the world. The present study shows the prediction efficiency of mutual information for the classification of antimicrobial peptides. The proven role of antimicrobial peptides (AMPs) to fight against multidrug-resistant pathogens and AMP’s low toxic properties laid the foundation of computational methods to play their role in detecting AMPs from non-AMPs. Mutual information vectors (MIV) were created for AMP/non-AMP sequences and then fed to different machine learning classifiers out of which a random forest (RF) classifier showed best results for predicting AMPs. Random forest classifiers were evaluated on benchmark datasets by 10-fold cross-validation. The proposed MIV-RF method showed better prediction accuracy, MCC (Matthews correlation coefficient), and AUC-ROC (Area Under The Curve-Receiver Operating Characteristics) than available methods for detecting AMPs.

Communicated by Ramaswamy H. Sarma     

Large scale ab initio modeling of structurally uncharacterized antimicrobial peptides reveals known and novel folds

Antimicrobial resistance within a wide range of infectious agents is a severe and growing public health threat. Antimicrobial peptides (AMPs) are among the leading alternatives to current antibiotics, exhibiting broad spectrum activity. Their activity is determined by numerous properties such as cationic charge, amphipathicity, size, and amino acid composition. Currently, only around 10% of known AMP sequences have experimentally solved structures. To improve our understanding of the AMP structural universe we have carried out large scale ab initio 3D modeling of structurally uncharacterized AMPs that revealed similarities between predicted folds of the modeled sequences and structures of characterized AMPs. Two of the peptides whose models matched known folds are Lebocin Peptide 1A (LP1A) and Odorranain M, predicted to form β-hairpins but, interestingly, to lack the intramolecular disulfide bonds, cation-π or aromatic interactions that generally stabilize such AMP structures. Other examples include Ponericin Q42, Latarcin 4a, Kassinatuerin 1, Ceratotoxin D, and CPF-B1 peptide, which have α-helical folds, as well as mixed αβ folds of human Histatin 2 peptide and Garvicin A which are, to the best of our knowledge, the first linear αββ fold AMPs lacking intramolecular disulfide bonds. In addition to fold matches to experimentally derived structures, unique folds were also obtained, namely for Microcin M and Ipomicin. These results help in understanding the range of protein scaffolds that naturally bear antimicrobial activity and may facilitate protein design efforts towards better AMPs.     

A New Multi-label Classifier for Identifying the Functional Types of Singleplex and Multiplex Antimicrobial Peptides

Antimicrobial peptides (AMPs) play an important role in the innate immune system that evolved in most living organisms. As a kind of natural antibiotics, it is promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop a fast and effective computational method for accurately predicting the functional types of AMPs, because the biological functions of AMPs are correlated with the type it belongs to. Although many efforts have been made in this area, to the best of our knowledge, most of the existing predictors only has the ability to deal with whether a peptide is an AMP or not, or a peptide belongs to which one type. However, there are many AMPs have two or more functional types, the phenomenon should worthy of our special notice, because they may have some unique biological functions for new drug design and disease treatment. In this study, in order to reflect the characteristic of multiplex AMPs, a new multi-label classifier based on sequence information and multi-label learning with label-specific features (LIFT) algorithm was developed. It was observed that, the absolute-true with jackknife test by the new predictor on a newly stringent benchmark dataset is 0.5040, and the success rates achieved by the new predictor are 5 % higher than this by iAMP-2L in the same dataset, indicating that our method is quite promising. We hope that the predictor may become a useful high-through tool in identifying the functional types of AMPs.     

Sap transporter mediated import and subsequent degradation of antimicrobial peptides in Haemophilus

Antimicrobial peptides (AMPs) contribute to host innate immune defense and are a critical component to control bacterial infection. Nontypeable Haemophilus influenzae (NTHI) is a commensal inhabitant of the human nasopharyngeal mucosa, yet is commonly associated with opportunistic infections of the upper and lower respiratory tracts. An important aspect of NTHI virulence is the ability to avert bactericidal effects of host-derived antimicrobial peptides (AMPs). The Sap (sensitivity to antimicrobial peptides) ABC transporter equips NTHI to resist AMPs, although the mechanism of this resistance has remained undefined. We previously determined that the periplasmic binding protein SapA bound AMPs and was required for NTHI virulence in vivo. We now demonstrate, by antibody-mediated neutralization of AMP in vivo, that SapA functions to directly counter AMP lethality during NTHI infection. We hypothesized that SapA would deliver AMPs to the Sap inner membrane complex for transport into the bacterial cytoplasm. We observed that AMPs localize to the bacterial cytoplasm of the parental NTHI strain and were susceptible to cytoplasmic peptidase activity. In striking contrast, AMPs accumulated in the periplasm of bacteria lacking a functional Sap permease complex. These data support a mechanism of Sap mediated import of AMPs, a novel strategy to reduce periplasmic and inner membrane accumulation of these host defense peptides.     

改良型抗菌肽的研究进展

长期滥用抗生素导致了耐药菌株“超级细菌”的出现,增加了动物、人类健康和环境污染风险．寻找抗生素替代品正成为全球研究热点,抗菌肽因其高效抗菌效果和不同于抗生素的独特作用机制引起了各国研究者的关注,并进行了相关研究．然而抗菌肽的安全性、稳定性、生产成本等问题限制了其生产与应用．为了克服这些不利因素,研究者们对抗菌肽进行了多种方式的改造,产生了模拟型、同源型、杂合型、轭合型、稳定型和固位型等改良型抗菌肽,并有望在畜牧业、食品业、医药业等领域得到广泛的应用．本文主要综述了这些改良型抗菌肽近年来的研究进展．     

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew’s correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.     

基于BERT与Text-CNN的抗菌肽识别方法

抗菌肽(antimicrobial peptides,AMPs)广泛存在于生命体中,是一种具有广谱抗菌活性、免疫调节功能的小分子多肽。抗菌肽不易产生耐药性,适用范围广,具有极大的临床价值,是传统抗生素的有力竞争者。识别抗菌肽是抗菌肽研究领域中的重要研究方向,湿实验法在进行大规模抗菌肽识别时存在成本高、效率低、周期长等难点,计算机辅助识别法是抗菌肽识别手段的重要补充,如何提升准确率是其中的关键问题。蛋白质序列可以被近似地看作是由氨基酸组成的语言,运用自然语言处理(natural language processing,NLP)技术可能提取到丰富的特征。本文将自然语言处理领域中的预训练模型BERT和微调结构Text-CNN结合,对蛋白质语言进行建模,提供了开源可用的抗菌肽识别工具,并与已发表的5种抗菌肽识别工具进行了比较。结果表明,优化“预训练-微调”策略带来了准确率、敏感度、特异性和马修相关系数的整体提升,为进一步研究抗菌肽识别算法提供了新思路。     

New trends in peptide-based anti-biofilm strategies: a review of recent achievements and bioinformatic approaches

Antimicrobial peptides (AMPs) have a broad spectrum of activity and unspecific mechanisms of action. Therefore, they are seen as valid alternatives to overcome clinically relevant biofilms and reduce the chance of acquired resistance. This paper reviews AMPs and anti-biofilm AMP-based strategies and discusses ongoing and future work. Recent studies report successful AMP-based prophylactic and therapeutic strategies, several databases catalogue AMP information and analysis tools, and novel bioinformatics tools are supporting AMP discovery and design. However, most AMP studies are performed with planktonic cultures, and most studies on sessile cells test AMPs on growing rather than mature biofilms. Promising preliminary synergistic studies have to be consubstantiated and the study of functionalized coatings with AMPs must be further explored. Standardized operating protocols, to enforce the repeatability and reproducibility of AMP anti-biofilm tests, and automated means of screening and processing the ever-expanding literature are still missing.     

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.     

Therapeutic antimicrobial peptides may compromise natural immunity

Antimicrobial peptides (AMPs) have been proposed as a promising new class of antimicrobials despite warnings that therapeutic use could drive the evolution of pathogens resistant to our own immunity peptides. Using experimental evolution, we demonstrate that Staphylococcus aureus rapidly evolved resistance to pexiganan, a drug-candidate for diabetic leg ulcer infections. Evolved resistance was costly in terms of impaired growth rate, but costs-of-resistance were completely ameliorated by compensatory adaptation. Crucially, we show that, in some populations, experimentally evolved resistance to pexiganan provided S. aureus with cross-resistance to human-neutrophil-defensin-1, a key component of the innate immune response to infection. This unintended consequence of therapeutic use could drastically undermine our innate immune system's ability to control and clear microbial infections. Our results therefore highlight grave potential risks of AMP therapies, with implications for their development.     

Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries

Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world’s first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/. A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.     

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

BackgroundComputer-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.MethodsThe 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.ResultsThe model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods.ConclusionOur 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 significanceThe 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.AvailabilityThe system was implemented as a web application, available at <http://portoreports.com/stm/>.     

抗菌肽结构改造与人工智能研发策略

抗菌肽是一类广泛存在于生物体内的小分子肽,参与构成生物体先天免疫,可以有效抵抗病原微生物的入侵。抗菌肽具有广谱抗菌活性,且不易产生耐药性等特点,在治疗感染性疾病方面具有独特的优势,有望成为理想的抗感染药物。然而,由于部分抗菌肽尚存在稳定性差、毒性高等问题,限制了抗菌肽的广泛应用。由于人工智能算法能有效合成具有高稳定性、低毒性的抗菌肽,在探索天然抗菌肽中展现了巨大的潜力,因此本文简述了抗菌肽的抗菌机制、结构改造以及利用机器学习和深度学习等人工智能算法进行新型抗菌肽研发的优化策略,以期为抗菌肽结构优化及研发提供新思路。     

Design and characterization of novel hybrid antimicrobial peptides based on cecropin A, LL-37 and magainin II

Antimicrobial peptides (AMPs) are a naturally occurring component of the innate immune response of many organisms and can have activity against both Gram-negative and Gram-positive bacterial species. In order to optimize and improve the direct antimicrobial effect of AMPs against a broad spectrum of bacterial species, novel synthetic hybrids were rationally designed from cecropin A, LL-37 and magainin II. AMPs were selected based on their α-helical secondary structure and fragments of these were analyzed and combined in silico to determine which hybrid peptides would form the best amphipathic cationic α-helices. Four hybrid peptides were synthesized (CaLL, CaMA, LLaMA and MALL) and evaluated for direct antimicrobial activity against a range of bacterial species (Bacillus anthracis, Burkholderia cepacia, Francisella tularensis LVS and Yersinia pseudotuberculosis) alongside the original 'parent' AMPs. The hybrid peptides showed greater antimicrobial effects than the parent AMPs (in one case a parent is completely ineffective while a hybrid based on it removes all traces of bacteria by 3h), although they also demonstrated higher hemolytic properties. Modifications were then carried out to the most toxic hybrid AMP (CaLL) to further improve the therapeutic index. Modifications made to the hybrid lowered hemolytic activity and also lowered antimicrobial activity by various degrees. Overall, this work highlights the potential for rational design and synthesis of improved AMPs that have the capability to be used therapeutically for treatment of bacterial infections.     

Interactions of two enantiomers of a designer antimicrobial peptide with structural components of the bacterial cell envelope

Antimicrobial peptides (AMPs) have great potential in treating multi-drug resistant bacterial infections. The antimicrobial activity of d -enantiomers is significantly higher than l -enantiomers and sometimes selectively enhanced against Gram-positive bacteria. Unlike phospholipids in the bacterial plasma membrane, the role of other bacterial cell envelop components is often overlooked in the mode of action of AMPs. In this work, we explored the structural interactions between the main different structural components in Gram-negative/Gram-positive bacteria and the two enantiomers of a designer AMP, GL13K. We observed that both l -GL13K and d -GL13K formed self-assembled amyloid-like nanofibrils when the peptides interacted with lipopolysaccharide and lipoteichoic acid, components of the outer membrane of Gram-negative bacteria and cell wall of Gram-positive bacteria, respectively. Another cell wall component, peptidoglycan, showed strong interactions exclusively with d -GL13K and formed distinct laminar structures. This specific interaction between peptidoglycans and d -GL13K might contribute to the enhanced activity of d -GL13K against Gram-positive bacteria as they have a much thicker peptidoglycan layer than Gram-negative bacteria. A better understanding of the specific role of bacterial cell envelop components in the AMPs mechanism of action can guide the design of more effective Gram-selective AMPs.     

Expression systems for heterologous production of antimicrobial peptides

Antimicrobial peptides (AMPs) consist of molecules that act on the defense systems of numerous organisms toward multiple pathogens such as bacteria, fungi, parasites and viruses. These compounds have become extremely significant due to the increasing resistance of microorganisms to common antibiotics. However, the low quantity of peptides obtained from direct purification is, to date, still a remarkable bottleneck for scientific and industrial research development. Therefore, this review describes the main heterologous systems currently used for AMP production, including bacteria, fungi and plants, and also the related strategies for reaching greater functional peptide production. The main difficulties of each system are also described in order to provide some directions for AMP production. In summary, data revised here indicate that large-scale production of AMPs can be obtained using biotechnological tools, and the products may be applied in the pharmaceutical industry as well as in agribusiness.     

Mechanisms and Fitness Costs of Resistance to Antimicrobial Peptides LL-37, CNY100HL and Wheat Germ Histones

Antimicrobial peptides (AMPs) represent a potential new class of antimicrobial drugs with potent and broad-spectrum activities. However, knowledge about the mechanisms and rates of resistance development to AMPs and the resulting effects on fitness and cross-resistance is limited. We isolated antimicrobial peptide (AMP) resistant Salmonella typhimurium LT2 mutants by serially passaging several independent bacterial lineages in progressively increasing concentrations of LL-37, CNY100HL and Wheat Germ Histones. Significant AMP resistance developed in 15/18 independent bacterial lineages. Resistance mutations were identified by whole genome sequencing in two-component signal transduction systems (pmrB and phoP) as well as in the LPS core biosynthesis pathway (waaY, also designated rfaY). In most cases, resistance was associated with a reduced fitness, observed as a decreased growth rate, which was dependent on growth conditions and mutation type. Importantly, mutations in waaY decreased bacterial susceptibility to all tested AMPs and the mutant outcompeted the wild type parental strain at AMP concentrations below the MIC for the wild type. Our data suggests that resistance to antimicrobial peptides can develop rapidly through mechanisms that confer cross-resistance to several AMPs. Importantly, AMP-resistant mutants can have a competitive advantage over the wild type strain at AMP concentrations similar to those found near human epithelial cells. These results suggest that resistant mutants could both be selected de novo and maintained by exposure to our own natural repertoire of defence molecules.