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

Antimicrobial peptides: Opportunities and challenges in overcoming resistance

Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, antimicrobial peptides have emerged as promising candidates against resistant pathogens. Unlike traditional antibiotics with only one target, these peptides can use different mechanisms to destroy bacteria, with low toxicity to mammalian cells compared to many conventional antibiotics. Antimicrobial peptides (AMPs) have encouraging antibacterial properties and are currently employed in the clinical treatment of pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes the mechanisms of antimicrobial peptides against bacteria, discusses the mechanisms of drug resistance, the limitations and challenges of AMPs in peptide drug applications for combating drug-resistant bacterial infections, and strategies to enhance their capabilities.

Metal coordination governs the antimicrobial efficacy of calcitermin derivatives

Antimicrobial peptides are promising alternatives to classical antibiotics. Their microbicidal activity can arise from different mechanisms, one of which is known as nutritional immunity and has metal micronutrients and metal-binding biomolecules as its main players. Calcitermin is an antimicrobial peptide and an effective metal chelator. Its properties as an antibacterial and anti-Candida agent have been recently studied both as a free peptide and in the presence of zinc and copper ions, with which it forms stable complexes. Calcitermin derivatives have also gained attention thanks to the possibility of improving their properties, like metal-binding affinity and/or stability in biological fluids, through ad hoc modifications of the native peptide sequence. In this work, the Ala-to-Ser substitutions close to the coordination site of calcitermin have been introduced to study the impact on the biological activity and metal-binding properties. Our results show that metal coordination has a clear impact on the bioactivity of the studied compounds, to the point that the truncated fragment of calcitermin, solely containing the main metal-binding residues, also shows antimicrobial activity.

Can large language models predict antimicrobial peptide activity and toxicity?

Antimicrobial peptides (AMPs) are naturally occurring or designed peptides up to a few tens of amino acids which may help address the antimicrobial resistance crisis. However, their clinical development is limited by toxicity to human cells, a parameter which is very difficult to control. Given the similarity between peptide sequences and words, large language models (LLMs) might be able to predict AMP activity and toxicity. To test this hypothesis, we fine-tuned LLMs using data from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). GPT-3 performed well but not reproducibly for activity prediction and hemolysis, taken as a proxy for toxicity. The later GPT-3.5 performed more poorly and was surpassed by recurrent neural networks (RNN) trained on sequence-activity data or support vector machines (SVM) trained on MAP4C molecular fingerprint-activity data. These simpler models are therefore recommended, although the rapid evolution of LLMs warrants future re-evaluation of their prediction abilities.

Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning

The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.

Differences in Relevant Physicochemical Properties Correlate with Synergistic Activity of Antimicrobial Peptides

With the urgent need for new medical approaches due to increased bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have been considered as potential treatments for infections. Experiments indicate that combinations of several types of AMPs might be even more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacterial resistance. The molecular mechanisms of AMP-AMP synergistic antimicrobial activity, however, remain not well understood. Here, we present a theoretical approach that allows us to relate the physicochemical properties of AMPs and their antimicrobial cooperativity. It utilizes correlation and bioinformatics analysis. A concept of physicochemical similarity is introduced, and it is found that less similar AMPs with respect to certain physicochemical properties lead to greater synergy because of their complementary antibacterial actions. The analysis of correlations between the similarity and the antimicrobial properties allows us to effectively separate synergistic from nonsynergistic AMP pairs. Our theoretical approach can be used for the rational design of more effective AMP combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems.

Efficacy of Ceragenins in Controlling the Growth of Oral Microorganisms: Implications for Oral Hygiene Management

Ensuring proper dental hygiene is of paramount importance for individuals' general well-being, particularly for patients receiving medical care. There is a prevailing utilization of conventional oral hygiene items, including toothbrushes and mouthwashes, which have gained widespread acceptance; nevertheless, their limitations encourage investigating novel options in this domain. Our study indicates that ceragenins (CSAs) being lipid analogs of host defense peptides, well-recognized for their wide-ranging antimicrobial properties, may be a potentially efficacious means to augment oral hygiene in hospitalized individuals. We demonstrate that ceragenins CSA-13, CSA-44, and CSA-131 as well as undescribed to date CSA-255 display potent antimicrobial activities against isolates of fungi, aerobic, and anaerobic bacteria from Candida, Streptococcus, Enterococcus, and Bacteroides species, which are well-recognized representatives of microbes found in the oral cavity. These effects were further confirmed against mono- and dual-species fungal and bacterial biofilms. While the ceragenins showed similar or slightly diminished efficacy compared to commercially available mouthwashes, they demonstrated a highly favorable toxicity profile toward host cells, that may translate into better maintenance of host mucosal membrane stability. This suggests that incorporating ceragenins into oral hygiene products could be a valuable strategy for reducing the risk of both oral cavity-localized and secondary systemic infections and for improving the overall health outcomes of individuals receiving medical treatment.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Impact of C- and N-terminal protection on the stability, metal chelation and antimicrobial properties of calcitermin

The main limitation to the use of antimicrobial peptides (AMPs) as regular drugs, against antibiotic and antifungal resistance, mainly relates to their rapid degradation by proteolytic enzymes. The introduction of suitable structural changes in the peptide chain can make the peptide less susceptible to the action of proteases, thus overcoming this problem. To improve the plasma stability of calcitermin, a metal-chelating AMP present in the human respiratory tract and investigated in the present study, C- and/or N- terminal modifications have been introduced in the native sequence. Evaluation of peptide stability has been performed to determine the half-life times in human plasma of both native calcitermin and its derivatives. However, the protection of the peptide termini can also affect its metal coordination behaviour. Thus, the characterization of Zn2+ and Cu2+ complexes has been performed by means of several techniques, including potentiometry, high-resolution mass spectrometry, UV-Vis, circular dichroism and EPR. On the basis of the obtained results, it was possible to compare the biological activity of the studied systems, taking into account both the metal-binding ability and the peptide stability to search for a link among them. A significant result of this study is that the N-terminal protection increases the calcitermin half-life over seven times and the formation of metal complexes confers resistance towards degradation almost doubling its half-life.

Novel cationic cryptides in Penaeus vannamei demonstrate antimicrobial and anti-cancer activities

Cryptides are a subfamily of bioactive peptides that exist in all living organisms. They are latently encrypted in their parent sequences and exhibit a wide range of biological activities when decrypted via in vivo or in vitro proteases. Cationic cryptides tend to be drawn to the negatively charged membranes of microbial and cancer cells, causing cell death through various mechanisms. This makes them promising candidates for alternative antimicrobial and anti-cancer therapies, as their mechanism of action is independent of gene mutations. In the current study, we employed an in silico approach to identify novel cationic cryptides with potential antimicrobial and anti-cancer activities in atypical and systematic strategy by reanalysis of a publicly available RNA-seq dataset of Pacific white shrimp (Penaus vannamei) in response to bacterial infection. Out of 12 cryptides identified, five were selected based on their net charges and potential for cell penetration. Following chemical synthesis, the cryptides were assayed in vitro to test for their biological activities. All five cryptides demonstrated a wide range of selective activity against the tested microbial and cancer cells, their anti-biofilm activities against mature biofilms, and their ability to interact with Gram-positive and negative bacterial membranes. Our research provides a framework for a comprehensive analysis of transcriptomes in various organisms to uncover novel bioactive cationic cryptides. This represents a significant step forward in combating the crisis of multi-drug-resistant microbial and cancer cells, as these cryptides neither induce mutations nor are influenced by mutations in the cells they target.

Diversity of Antimicrobial Peptides in Silkworm

Antimicrobial resistance is a phenomenon that the present-day world is witnessing that poses a serious threat to global health. The decline in the development of novel therapeutics over the last couple of decades has exacerbated the situation further. In this scenario, the pursuit of new alternative therapeutics to commonly used antibiotics has gained predominance amongst researchers across the world. Antimicrobial peptides (AMPs) from natural sources have drawn significant interest in the recent years as promising pharmacological substitutes over the conventional antibiotics. The most notable advantage of AMPs is that microorganisms cannot develop resistance to them. Insects represent one of the potential sources of AMPs, which are synthesized as part of an innate immune defence against invading pathogens. AMPs from different insects have been extensively studied, and silkworm is one of them. Diverse classes of AMPs (including attacins, cecropins, defensins, enbocins, gloverins, lebocins and moricins) were identified from silkworm that exhibit antimicrobial property against bacteria, fungi and viruses, indicating their potential therapeutic benefits. This review briefs about the immune responses of silkworm to invading pathogens, the isolation of AMPs from silkworms, AMPs reported in silkworms and their activity against various microorganisms.

A Novel Peptide with Antifungal Activity from Red Swamp Crayfish Procambarus clarkii

The defense system of freshwater crayfish Procambarus clarkii as a diversified source of bioactive molecules with antimicrobial properties was studied. Antimicrobial activity of two polypeptide-enriched extracts obtained from hemocytes and hemolymph of P. clarkii were assessed against Gram positive (Staphylococcus aureus, Enterococcus faecalis) and Gram negative (Pseudomonas aeruginosa, Escherichia coli) bacteria and toward the yeast Candida albicans. The two peptide fractions showed interesting MIC values (ranging from 11 to 700 μg/mL) against all tested pathogens. Polypeptide-enriched extracts were further investigated using a high-resolution mass spectrometry and database search and 14 novel peptides were identified. Some peptides and their derivatives were chemically synthesized and tested in vitro against the bacterial and yeast pathogens. The analysis identified a synthetic derivative peptide, which showed an interesting antifungal (MIC and MFC equal to 31.2 μg/mL and 62.5 μg/mL, respectively) and antibiofilm (BIC₅₀ equal to 23.2 μg/mL) activities against Candida albicans and a low toxicity in human cells.

Efficacy of natural antimicrobial peptides versus peptidomimetic analogues: a systematic review

Aims: This systematic review was carried out to determine whether synthetic peptidomimetics exhibit significant advantages over antimicrobial peptides in terms of in vitro potency. Structural features - molecular weight, charge and length - were examined for correlations with activity. Methods: Original research articles reporting minimum inhibitory concentration  values against Escherichia coli, indexed until 31 December 2020, were searched in PubMed/ScienceDirect/Google Scholar and evaluated using mixed-effects models. Results: In vitro antimicrobial activity of peptidomimetics resembled that of antimicrobial peptides. Net charge significantly affected minimum inhibitory concentration values (p < 0.001) with a trend of 4.6% decrease for increments in charge by +1. Conclusion: AMPs and antibacterial peptidomimetics exhibit similar potencies, providing an opportunity to exploit the advantageous stability and bioavailability typically associated with peptidomimetics.

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.

GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences

Although peptides are regarded as ideal therapeutic agents, only a small proportion of the marketed drugs are peptides. In the past decade, pharmacists have paid great attention to the development of peptide therapeutics. Except a few approved chemically/rationally designed peptides, most attempts failed due to unsatisfactory efficacy or safety. Luckily, computation methods, such as artificial intelligence, have been utilized to accelerate the discovery of therapeutic peptides by predicting the activity, toxicity, and absorption, distribution, metabolism, and excretion of polypeptides. Usually, a specific biological activity of a peptide could be accurately determined by an interest-oriented binary classification constructed of a positive set and another un-experimentally validated negative set regardless of other characteristics, which suggests that it could be challenging to realize the comprehensive evaluation of the research object in the early stage of drug research and development. Herein, we proposed an integrated method (GM-Pep) that contained a conditional variational autoencoder model (CVAE) and a positive sample training multiclassifier (Deep-Multiclassifier) to effectively generate a single bioactive peptide sequence without toxicity and referential side effects. The results showed that our Deep-Multiclassifier model gave a sequence accuracy of up to 96.41% [toxicity (94.48%), antifungal (96.58%), antihypertensive (97.18%), and antibacterial (96.91%), respectively]. The properties of Deep-Multiclassifier and CVAE were validated through 12 first synthesized antibacterial peptides or compared to random peptides. The source code and data sets are available at https://github.com/TimothyChen225/GM-Pep.

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.

In Vitro Evaluation of Antimicrobial Peptides from the Black Soldier Fly (Hermetia Illucens) against a Selection of Human Pathogens

Antimicrobial peptides (AMPs) are being explored as alternatives to traditional antibiotics to combat the rising antimicrobial resistance. Insects have proven to be a valuable source of new, potent AMPs with large structural diversity. For example, the black soldier fly has one of the largest AMP repertoires ever recorded in insects. Currently, however, this AMP collection has not yet undergone antimicrobial evaluation or in-depth in vitro characterization. This study evaluated the activity of a library of 36 black soldier fly AMPs against a panel of human pathogens (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, and Aspergillus fumigatus) and a human cell line (MRC5-SV2). The activity profile of two cecropins (Hill-Cec1 and Hill-Cec10) with potent Gram-negative activity, was further explored by characterizing their hemolysis, time-to-kill kinetics, membrane-permeabilization properties, and anti-biofilm activity. Hill-Cec1 and Hill-Cec10 also showed high activity against other bacterial species, including Klebsiella pneumoniae and multi-drug resistant P. aeruginosa. Both AMPs are bactericidal and have a rapid onset of action with membrane-permeabilizing effects. Hill-Cec1 and Hill-Cec10 were also able to prevent P. aeruginosa biofilm formation, but no relevant effect was seen on biofilm eradication. Overall, Hill-Cec1 and Hill-Cec10 are promising leads for new antimicrobial development to treat critical infections caused by Gram-negative pathogens such as P. aeruginosa.

IMPORTANCE With the ever growing antimicrobial resistance, finding new candidates for antimicrobial drug development is indispensable. Antimicrobial peptides have steadily gained attention as alternatives for conventional antibiotics, due to some highly desirable characteristics, such as their low propensity for resistance development. With this article, we aim to upgrade the knowledge on the activity of black soldier fly antimicrobial peptides and their potential as future therapeutics. To achieve this, we have evaluated for the first time a library of 36 synthetically produced peptides from the black soldier fly against a range of human pathogens and a human cell line. Two selected peptides have undergone additional testing to characterize their antimicrobial profile against P. aeruginosa, a clinically important Gram-negative pathogen with a high established resistance. Overall, this research has contributed to the search for new peptide drug leads to combat the rising antimicrobial resistance.

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.

How to Combat Gram-Negative Bacteria Using Antimicrobial Peptides: A Challenge or an Unattainable Goal?

Antimicrobial peptides (AMPs) represent a promising and effective alternative for combating pathogens, having some advantages compared to conventional antibiotics. However, AMPs must also contend with complex and specialised Gram-negative bacteria envelops. The variety of lipopolysaccharide and phospholipid composition in Gram-negative bacteria strains and species are decisive characteristics regarding their susceptibility or resistance to AMPs. Such biological and structural barriers have created delays in tuning AMPs to deal with Gram-negative bacteria. This becomes even more acute because little is known about the interaction AMP–Gram-negative bacteria and/or AMPs’ physicochemical characteristics, which could lead to obtaining selective molecules against Gram-negative bacteria. As a consequence, available AMPs usually have highly associated haemolytic and/or cytotoxic activity. Only one AMP has so far been FDA approved and another two are currently in clinical trials against Gram-negative bacteria. Such a pessimistic panorama suggests that efforts should be concentrated on the search for new molecules, designs and strategies for combating infection caused by this type of microorganism. This review has therefore been aimed at describing the currently available AMPs for combating Gram-negative bacteria, exploring the characteristics of these bacteria’s cell envelop hampering the development of new AMPs, and offers a perspective regarding the challenges for designing new AMPs against Gram-negative bacteria.

Deciphering lipid transfer between and within membranes with time-resolved small-angle neutron scattering

This review focuses on time-resolved neutron scattering, particularly time-resolved small angle neutron scattering (TR-SANS), as a powerful in situ noninvasive technique to investigate intra- and intermembrane transport and distribution of lipids and sterols in lipid membranes. In contrast to using molecular analogues with potentially large chemical tags that can significantly alter transport properties, small angle neutron scattering relies on the relative amounts of the two most abundant isotope forms of hydrogen: protium and deuterium to detect complex membrane architectures and transport processes unambiguously. This review discusses advances in our understanding of the mechanisms that sustain lipid asymmetry in membranes-a key feature of the plasma membrane of cells-as well as the transport of lipids between membranes, which is an essential metabolic process.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Rational design of hyperstable antibacterial peptides for food preservation

We describe the design of peptides with properties like thermostability, pH stability, and antibacterial activity against a few bacterial food pathogens. Insights obtained from classical structure-function analysis of natural peptides and their mutants through antimicrobial and enzymatic assays are used to rationally develop a set of peptides. pH and thermostability assays were performed to demonstrate robust antimicrobial activity post-treatment with high temperatures and at wide pH ranges. We have also investigated the mode of action of these hyperstable peptides using membrane permeability assays, electron microscopy, and molecular dynamics simulations. Notably, through mutational studies, we show that these peptides elicit their antibacterial action via both membrane destabilization and inhibition of intracellular trypsin-the two functions attributable to separate peptide segments. Finally, toxicity studies and food preservation assays demonstrate the safety and efficacy of the designed peptides for food preservation. Overall, the study provides a general 'blueprint' for the development of stable antimicrobial peptides (AMPs). Insights obtained from this work may also be combined with combinatorial methods in high-throughput studies for future development of antimicrobials for various applications.

Spectrum of deep learning algorithms in drug discovery

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

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.

Switching Bond: Generation of New Antimicrobial Peptides via the Incorporation of an Intramolecular Isopeptide Bond

Antimicrobial peptides (AMPs), which can be modified to kill a broad spectrum of microoganisms or a specific microorganism, are considered as promising alternatives to combat the rapidly widespread, resistant bacterial infections. However, there are still several obstacles to overcome. These include toxicity, stability, and the ability to interfere with the immune response and bacterial resistance. To overcome these challenges, we herein replaced the regular peptide bonds with isopeptide bonds to produce new AMPs based on the well-known synthetic peptides Amp1L and MSI-78 (pexiganan). Two new peptides Amp1EP and MSIEP were generated while retaining properties such as size, sequence, charge, and molecular weight. These new peptides have reduced toxicity toward murine macrophage (RAW 264.7) cells, human monocytic (THP-1) cells, and human red blood cells (hRBCs) and enhanced the stability toward proteolytic degradation. Importantly, the new peptides do not repress the pro-inflammatory cytokine and hence should not modulate the immune response. Structurally, the new peptides, Amp1EP and MSIEP, have a structure of random coils in contrast to the helical structures of the parental peptides as revealed by circular dichroism (CD) analysis. Their mode of action, assessed by flow cytometry, includes permeabilization of the bacterial membrane. Overall, we present here a new approach to modulate AMPs to develop antimicrobial peptides for future therapeutic purposes.

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.

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.

Biomolecular interactions of ultrasmall metallic nanoparticles and nanoclusters

The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-concept to clinical applications, with several NP types meeting regulatory approval or undergoing clinical trials. A new type of metallic nanostructures called ultrasmall nanoparticles (usNPs) and nanoclusters (NCs), while retaining essential properties of the larger (classical) NPs, have features common to bioactive proteins. This combination expands the potential use of usNPs and NCs to areas of diagnosis and therapy traditionally reserved for small-molecule medicine. Their distinctive physicochemical properties can lead to unique in vivo behaviors, including improved renal clearance and tumor distribution. Both the beneficial and potentially deleterious outcomes (cytotoxicity, inflammation) can, in principle, be controlled through a judicious choice of the nanocore shape and size, as well as the chemical ligands attached to the surface. At present, the ability to control the behavior of usNPs is limited, partly because advances are still needed in nanoengineering and chemical synthesis to manufacture and characterize ultrasmall nanostructures and partly because our understanding of their interactions in biological environments is incomplete. This review addresses the second limitation. We review experimental and computational methods currently available to understand molecular mechanisms, with particular attention to usNP-protein complexation, and highlight areas where further progress is needed. We discuss approaches that we find most promising to provide relevant molecular-level insight for designing usNPs with specific behaviors and pave the way to translational applications.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

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.

Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.

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.

Shorter Antibacterial Peptide Having High Selectivity for E. coli Membranes and Low Potential for Inducing Resistance

Antimicrobial peptides (AMPs) have been recognised as a significant therapeutic option for mitigating resistant microbial infections. It has been found recently that Plasmodium falciparum-derived, 20 residue long, peptide 35409 had antibacterial and haemolytic activity, making it an AMP having reduced selectivity, and suggesting that it should be studied more extensively for obtaining new AMPs having activity solely targeting the bacterial membrane. Peptide 35409 was thus used as template for producing short synthetic peptides (<20 residues long) and evaluating their biological activity and relevant physicochemical characteristics for therapeutic use. Four of the sixteen short peptides evaluated here had activity against E. coli without any associated haemolytic effects. The 35409-1 derivative (17 residues long) had the best therapeutic characteristics as it had high selectivity for bacterial cells, stability in the presence of human sera, activity against E. coli multiresistant clinical isolates and was shorter than the original sequence. It had a powerful membranolytic effect and low potential for inducing resistance in bacteria. This peptide’s characteristics highlighted its potential as an alternative for combating infection caused by E. coli multiresistant bacteria and/or for designing new AMPs.

Anti-Acne Action of Peptides Isolated from Burdock Root-Preliminary Studies and Pilot Testing

This work aimed to study the anti-bacterial, anti-biofilm and anti-oxidant potential effects of low molecular weight (LMW) peptides (Br-p) isolated from burdock (Arctium lappa L.) roots. We conducted a preliminary study to exclude or confirm the antibiotic activity of the LMW peptides fraction of this plant. Br-p were isolated using gel filtration and a 10 kDa cut-off membrane. The obtained peptides were identified by MALDI TOF/TOF. Antibacterial activity was tested against acne strains using diffusion tests, MIC and MBC. The fibroblast cytotoxicity of Br-p was tested, and the selectivity index (SI) value was determined. The fraction of 46 Br-p peptides isolated from burdock root with a molecular weight below 5000 Da and theoretic pI (isoelectric point) of 3.67-11.83 showed a narrow spectrum of activity against Gram-positive acne bacterial strains. One of the Br-p peptides assessed on MALDI RapidDeNovo was LRCDYGRFFASKSLYDPLKKRR cationic peptide. It was analogous to that contained in A. lappa protein, and theoretically it was matched as a peptide with antibiotic nature. Br-p did not show toxicity to fibroblasts in the tested concentration up to 10 mg/mL, obtaining CC50 10 mg/mL. The SI value for the tested Propionibacterium strains ranged from 160 to 320. Finally, an active dressing based on chitosan/alginate/genipin was prepared using freeze-drying. The formed dressing was evaluated for its anti-acne activity. To sum up: preliminary biological studies confirmed the anti-acne properties of the isolated peptide fraction from burdock root and pointed to the possibility of using it to create an active dressing on the skin.

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.

Increases in Hydrophilicity and Charge on the Polar Face of Alyteserin 1c Helix Change its Selectivity towards Gram-Positive Bacteria

Recently, resistance of pathogens towards conventional antibiotics has increased, representing a threat to public health globally. As part of the fight against this, studies on alternative antibiotics such as antimicrobial peptides have been performed, and it has been shown that their sequence and structure are closely related to their antimicrobial activity. Against this background, we here evaluated the antibacterial activity of two peptides developed by solid-phase synthesis, Alyteserin 1c (WT) and its mutant derivative (ΔM), which shows increased net charge and reduced hydrophobicity. These structural characteristics were modified as a result of amino acid substitutions on the polar face of the WT helix. The minimum inhibitory concentration (MIC) of both peptides was obtained in Gram-positive and Gram-negative bacteria. The results showed that the rational substitutions of the amino acids increased the activity in Gram-positive bacteria, especially against Staphylococcus aureus, for which the MIC was one-third of that for the WT analog. In contrast to the case for Gram-positive bacteria, these substitutions decreased activity against Gram-negative bacteria, especially in Escherichia coli, for which the MIC was eight-fold higher than that exhibited by the WT peptide. To understand this, models of the peptide behavior upon interacting with membranes of E. coli and S. aureus created using molecular dynamics were studied and it was determined that the helical stability of the peptide is indispensable for antimicrobial activity. The hydrogen bonds between the His20 of the peptides and the phospholipids of the membranes should modulate the selectivity associated with structural stability at the carboxy-terminal region of the peptides.