Macrel: antimicrobial peptide screening in genomes and metagenomes

Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

An efficient hybrid deep learning architecture for predicting short antimicrobial peptides

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning

The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibiotics. Here, we use metagenomics combined with machine learning to find antimicrobial peptides from environmental metagenomes and successfully obtained 16,044,909 predicted AMPs. We compared the abundance of potential antimicrobial peptides in natural environments and engineered environments, and found that engineered environments also have great potential. Further, we chose sludge as a typical engineered environmental sample, and tried to mine antimicrobial peptides from it. Through metaproteome analysis and correlation analysis, we mined 27 candidate AMPs from sludge. We successfully synthesized 25 peptides by chemical synthesis, and experimentally verified that 21 peptides had antibacterial activity against the 4 strains tested. Our work highlights the potential for mining new antimicrobial peptides from engineered environments and demonstrates the effectiveness of mining antimicrobial peptides from sludge.

A novel bioinformatic method for the identification of antimicrobial peptides in metagenomes

AIMS

This study aimed to develop a new bioinformatic approach for the identification of novel antimicrobial peptides (AMPs), which did not depend on sequence similarity to known AMPs held within databases, but on structural mimicry of another antimicrobial compound, in this case an ultrashort, synthetic, cationic lipopeptide (C12-OOWW-NH2).

METHODS AND RESULTS

When applied to a collection of metagenomic datasets, our outlined bioinformatic method successfully identified several short (8-10aa) functional AMPs, the activity of which was verified via disk diffusion and minimum inhibitory concentration assays against a panel of 12 bacterial strains. Some peptides had activity comparable to, or in some cases, greater than, those from published studies that identified AMPs using more conventional methods. We also explored the effects of modifications, including extension of the peptides, observing an activity peak at 9-12aa. Additionally, the inclusion of a C-terminal amide enhanced activity in most cases. Our most promising candidate (named PB2-10aa-NH2) was thermally stable, lipid-soluble, and possessed synergistic activity with ethanol but not with a conventional antibiotic (streptomycin).

CONCLUSIONS

While several bioinformatic methods exist to predict AMPs, the approach outlined here is much simpler and can be used to quickly scan huge datasets. Searching for peptide sequences bearing structural similarity to other antimicrobial compounds may present a further opportunity to identify novel AMPs with clinical relevance, and provide a meaningful contribution to the pressing global issue of AMR.

Functional and evolutionary significance of unknown genes from uncultivated taxa

Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Bioprospecting of unexplored halophilic actinobacteria against human infectious pathogens

Human pathogenic diseases received much attention recently due to their uncontrolled spread of antimicrobial resistance (AMR) which causes several threads every year. Effective alternate antimicrobials are urgently required to combat those disease causing infectious microbes. Halophilic actinobacteria revealed huge potentials and unexplored cultivable/non-cultivable actinobacterial species producing enormous antimicrobials have been proved in several genomics approaches. Potential gene clusters, PKS and NRPKS from Nocardia, Salinospora, Rhodococcus, and Streptomyces have wide range coding genes of secondary metabolites. Biosynthetic pathways identification via various approaches like genome mining, In silico, OSMAC (one strain many compound) analysis provides better identification of knowing the active metabolites using several databases like AMP, APD and CRAMPR, etc. Genome constellations of actinobacteria particularly the prediction of BGCs (Biosynthetic Gene Clusters) to mine the bioactive molecules such as pigments, biosurfactants and few enzymes have been reported for antimicrobial activity. Saltpan, saltlake, lagoon and haloalkali environment exploring potential actinobacterial strains Micromonospora, Kocuria, Pseudonocardia, and Nocardiopsis revealed several acids and ester derivatives with antimicrobial potential. Marine sediments and marine macro organisms have been found as significant population holders of potential actinobacterial strains. Deadly infectious diseases (IDs) including tuberculosis, ventilator-associated pneumonia and Candidiasis, have been targeted by halo-actinobacterial metabolites with promising results. Methicillin resistant Staphylococus aureus and virus like Encephalitic alphaviruses were potentially targeted by halophilic actinobacterial metabolites by the compound Homoseongomycin from sponge associated antinobacterium. In this review, we discuss the potential antimicrobial properties of various biomolecules extracted from the unexplored halophilic actinobacterial strains specifically against human infectious pathogens along with prospective genomic constellations.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.

Computational exploration of the global microbiome for antibiotic discovery

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning

Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.

Novel Antimicrobial Peptides from Saline Environments Active against E. faecalis and S. aureus: Identification, Characterisation and Potential Usage

Microorganisms inhabiting saline environments have been known for decades as producers of many valuable bioproducts. These substances include antimicrobial peptides (AMPs), the most recognizable of which are halocins produced by halophilic Archaea. As agents with a different modes of action from that of most conventionally used antibiotics, usually associated with an increase in the permeability of the cell membrane as a result of a formation of channels and pores, AMPs are a currently promising object of research focused on the investigation of antibiotics with non-standard modes of action. The aim of this study was to investigate antimicrobial activity against multidrug-resistant human pathogens of three peptides, which were synthetised based on sequences identified in metagenomes from saline environments. The investigations were performed against Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli and Candida albicans. Subsequently, the cytotoxicity and haemolytic properties of the tested peptides were verified. An in silico analysis of the interaction of the tested peptides with molecular targets for reference antibiotics was also carried out in order to verify whether or not they can act in a similar way. The P1 peptide manifested the growth inhibition of E. faecalis at a MIC₅₀ of 32 µg/mL and the P3 peptide at a MIC₅₀ of 32 µg/mL was shown to inhibit the growth of both E. faecalis and S. aureus. Furthermore, the P1 and P3 peptides were shown to have no cytotoxic or haemolytic activity against human cells.

Exploring resistomes and microbiomes in pilot-scale microalgae-bacteria wastewater treatment systems for use in low-resource settings

Antibiotic resistance genes (ARGs) released into the environment are an emerging human and environmental health concern, including ARGs spread in wastewater treatment effluents. In low-to-middle income countries (LMICs), an alternate wastewater treatment option instead of conventional systems are low-energy, high-rate algal ponds (HRAP) that use microalgae-bacteria aggregates (MABA) for waste degradation. Here we studied the robustness of ARG removal in MABA-based pilot-scale outdoor systems for 140 days of continuous operation. The HRAP system successfully removed 73 to 88 % chemical oxygen demand and up to 97.4 % ammonia, with aggregate size increasing over operating time. Fourteen ARG classes were identified in the HRAP influent, MABA, and effluent using metagenomics, with the HRAP process reducing total ARG abundances by up to 5-fold from influent to effluent. Parallel qPCR analyses showed the HRAP system significantly reduced exemplar ARGs (p < 0.05), with 1.2 to 4.9, 2.7 to 6.3, 0 to 1.5, and 1.2 to 4.8 log-removals for sul1, tetQ, blaKPC, and intl1 genes, respectively. Sequencing of influent, effluent and MABAs samples showed associated microbial communities differed significantly, with influent communities by Enterobacteriales (clinically relevant ARGs carrying bacteria), which were less evident in MABA and effluent. In this sense, such bacteria might be excluded from MABA due to their good settling properties and the presence of antimicrobial peptides. Microalgae-bacteria treatment systems steadily reduced ARGs from wastewater during operation time, using sunlight as the energetic driver, making them ideal for use in LMIC wastewater treatment applications.

Antimicrobial Peptide Arsenal Predicted from the Venom Gland Transcriptome of the Tropical Trap-Jaw Ant Odontomachus chelifer

With about 13,000 known species, ants are the most abundant venomous insects. Their venom consists of polypeptides, enzymes, alkaloids, biogenic amines, formic acid, and hydrocarbons. In this study, we investigated, using in silico techniques, the peptides composing a putative antimicrobial arsenal from the venom gland of the neotropical trap-jaw ant Odontomachus chelifer. Focusing on transcripts from the body and venom gland of this insect, it was possible to determine the gland secretome, which contained about 1022 peptides with putative signal peptides. The majority of these peptides (75.5%) were unknown, not matching any reference database, motivating us to extract functional insights via machine learning-based techniques. With several complementary methodologies, we investigated the existence of antimicrobial peptides (AMPs) in the venom gland of O. chelifer, finding 112 non-redundant candidates. Candidate AMPs were predicted to be more globular and hemolytic than the remaining peptides in the secretome. There is evidence of transcription for 97% of AMP candidates across the same ant genus, with one of them also verified as translated, thus supporting our findings. Most of these potential antimicrobial sequences (94.8%) matched transcripts from the ant's body, indicating their role not solely as venom toxins.

Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity

The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

Discovery of Lactomodulin, a Unique Microbiome-Derived Peptide That Exhibits Dual Anti-Inflammatory and Antimicrobial Activity against Multidrug-Resistant Pathogens

The human body is a superorganism that harbors trillions of microbes, most of which inhabit the gut. To colonize our bodies, these microbes have evolved strategies to regulate the immune system and maintain intestinal immune homeostasis by secreting chemical mediators. There is much interest in deciphering these chemicals and furthering their development as novel therapeutics. In this work, we present a combined experimental and computational approach to identifying functional immunomodulatory molecules from the gut microbiome. Based on this approach, we report the discovery of lactomodulin, a unique peptide from Lactobacillus rhamnosus that exhibits dual anti-inflammatory and antibiotic activities and minimal cytotoxicity in human cell lines. Lactomodulin reduces several secreted proinflammatory cytokines, including IL-8, IL-6, IL-1β, and TNF-α. As an antibiotic, lactomodulin is effective against a range of human pathogens, and is most potent against antibiotic-resistant strains such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). The multifunctional activity of lactomodulin affirms that the microbiome encodes evolved functional molecules with promising therapeutic potential.

A first insight into the Polish Bochnia Salt Mine metagenome

The Bochnia Salt Mine is one of the oldest mines in Europe. It was established in the thirteenth century, and actively operated until 1990. The mine has been placed on the UNESCO World Heritage List. Previous research describing Polish salt mines has been focused on bioaerosol characteristics and the identification of microorganisms potentially important for human health. The use of Polish salt mines as inhalation chambers for patients of health resorts has also been investigated. Nevertheless, the biodiversity of salt mines associated with biotechnological potential has not been well characterized. The present study paper examines the biodiversity of microorganisms in the Bochnia Salt Mine based on 16S rRNA gene and shotgun sequencing. Biodiversity studies revealed a significantly higher relative abundance of Chlamydiae at the first level of the mine (3.5%) compared to the other levels (< 0.1%). Patescibacteria microorganisms constituted a high percentage (21.6%) in the sample from site RA6. Shotgun sequencing identified 16 unique metagenome-assembled genomes (MAGs). Although one was identified as Halobacterium bonnevillei, the others have not yet been assigned to any species; it is possible that these species may be undescribed. Preliminary analyses of the biotechnological and pharmaceutical potential of microorganisms inhabiting the mine were also performed, and the biosynthetic gene cluster (BGC) profiles and antimicrobial peptide (AMP) coding genes in individual samples were characterized. Hundreds of BGCs and dozens of AMP coding genes were identified in metagenomes. Our findings indicate that Polish salt mines are promising sites for further research aimed at identifying microorganisms that are producers of potentially important substances with biotechnological and pharmaceutical applications.

Embedded-AMP: A Multi-Thread Computational Method for the Systematic Identification of Antimicrobial Peptides Embedded in Proteome Sequences

Antimicrobial peptides (AMPs) have gained the attention of the research community for being an alternative to conventional antimicrobials to fight antibiotic resistance and for displaying other pharmacologically relevant activities, such as cell penetration, autophagy induction, immunomodulation, among others. The identification of AMPs had been accomplished by combining computational and experimental approaches and have been mostly restricted to self-contained peptides despite accumulated evidence indicating AMPs may be found embedded within proteins, the functions of which are not necessarily associated with antimicrobials. To address this limitation, we propose a machine-learning (ML)-based pipeline to identify AMPs that are embedded in proteomes. Our method performs an in-silico digestion of every protein in the proteome to generate unique k-mers of different lengths, computes a set of molecular descriptors for each k-mer, and performs an antimicrobial activity prediction. To show the efficiency of the method we used the shrimp proteome, and the pipeline analyzed all k-mers between 10 and 60 amino acids in length to predict all AMPs in less than 20 min. As an application example we predicted AMPs in different rodents (common cuy, common rat, and naked mole rat) with different reported longevities and found a relation between species longevity and the number of predicted AMPs. The analysis shows as the longevity of the species is higher, the number of predicted AMPs is also higher. The pipeline is available as a web service.

Testing Antimicrobial Properties of Selected Short Amyloids

Amyloids and antimicrobial peptides (AMPs) have many similarities, e.g., both kill microorganisms by destroying their membranes, form aggregates, and modulate the innate immune system. Given these similarities and the fact that the antimicrobial properties of short amyloids have not yet been investigated, we chose a group of potentially antimicrobial short amyloids to verify their impact on bacterial and eukaryotic cells. We used AmpGram, a best-performing AMP classification model, and selected ten amyloids with the highest AMP probability for our experimental research. Our results indicate that four tested amyloids: VQIVCK, VCIVYK, KCWCFT, and GGYLLG, formed aggregates under the conditions routinely used to evaluate peptide antimicrobial properties, but none of the tested amyloids exhibited antimicrobial or cytotoxic properties. Accordingly, they should be included in the negative datasets to train the next-generation AMP prediction models, based on experimentally confirmed AMP and non-AMP sequences. In the article, we also emphasize the importance of reporting non-AMPs, given that only a handful of such sequences have been officially confirmed.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 10⁶⁵ unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

Identification of antimicrobial peptides from the human gut microbiome using deep learning

The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data. Of 2,349 sequences identified as candidate AMPs, 216 were chemically synthesized, with 181 showing antimicrobial activity (a positive rate of >83%). Most of these peptides have less than 40% sequence homology to AMPs in the training set. Further characterization of the 11 most potent AMPs showed high efficacy against antibiotic-resistant, Gram-negative pathogens and demonstrated significant efficacy in lowering bacterial load by more than tenfold against a mouse model of bacterial lung infection. Our study showcases the potential of machine learning approaches for mining functional peptides from metagenome data and accelerating the discovery of promising AMP candidate molecules for in-depth investigations.

Unveiling the genomic potential of Pseudomonas type strains for discovering new natural products

Microbes host a huge variety of biosynthetic gene clusters that produce an immeasurable array of secondary metabolites with many different biological activities such as antimicrobial, anticarcinogenic and antiviral. Despite the complex task of isolating and characterizing novel natural products, microbial genomic strategies can be useful for carrying out these types of studies. However, although genomic-based research on secondary metabolism is on the increase, there is still a lack of reports focusing specifically on the genus Pseudomonas. In this work, we aimed (i) to unveil the main biosynthetic systems related to secondary metabolism in Pseudomonas type strains, (ii) to study the evolutionary processes that drive the diversification of their coding regions and (iii) to select Pseudomonas strains showing promising results in the search for useful natural products. We performed a comparative genomic study on 194 Pseudomonas species, paying special attention to the evolution and distribution of different classes of biosynthetic gene clusters and the coding features of antimicrobial peptides. Using EvoMining, a bioinformatic approach for studying evolutionary processes related to secondary metabolism, we sought to decipher the protein expansion of enzymes related to the lipid metabolism, which may have evolved toward the biosynthesis of novel secondary metabolites in Pseudomonas. The types of metabolites encoded in Pseudomonas type strains were predominantly non-ribosomal peptide synthetases, bacteriocins, N-acetylglutaminylglutamine amides and ß-lactones. Also, the evolution of genes related to secondary metabolites was found to coincide with Pseudomonas species diversification. Interestingly, only a few Pseudomonas species encode polyketide synthases, which are related to the lipid metabolism broadly distributed among bacteria. Thus, our EvoMining-based search may help to discover new types of secondary metabolite gene clusters in which lipid-related enzymes are involved. This work provides information about uncharacterized metabolites produced by Pseudomonas type strains, whose gene clusters have evolved in a species-specific way. Our results provide novel insight into the secondary metabolism of Pseudomonas and will serve as a basis for the prioritization of the isolated strains. This article contains data hosted by Microreact.

Machine Learning Prediction of Antimicrobial Peptides

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

The Methods of Digging for "Gold" within the Salt: Characterization of Halophilic Prokaryotes and Identification of Their Valuable Biological Products Using Sequencing and Genome Mining Tools

Halophiles, the salt-loving organisms, have been investigated for at least a hundred years. They are found in all three domains of life, namely Archaea, Bacteria, and Eukarya, and occur in saline and hypersaline environments worldwide. They are already a valuable source of various biomolecules for biotechnological, pharmaceutical, cosmetological and industrial applications. In the present era of multidrug-resistant bacteria, cancer expansion, and extreme environmental pollution, the demand for new, effective compounds is higher and more urgent than ever before. Thus, the unique metabolism of halophilic microorganisms, their low nutritional requirements and their ability to adapt to harsh conditions (high salinity, high pressure and UV radiation, low oxygen concentration, hydrophobic conditions, extreme temperatures and pH, toxic compounds and heavy metals) make them promising candidates as a fruitful source of bioactive compounds. The main aim of this review is to highlight the nucleic acid sequencing experimental strategies used in halophile studies in concert with the presentation of recent examples of bioproducts and functions discovered in silico in the halophile's genomes. We point out methodological gaps and solutions based on in silico methods that are helpful in the identification of valuable bioproducts synthesized by halophiles. We also show the potential of an increasing number of publicly available genomic and metagenomic data for halophilic organisms that can be analysed to identify such new bioproducts and their producers.

iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types

Predicting antimicrobial peptides (AMPs') function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the 'CNN-BiLSTM-SVM classifier' and 'cellular automata image', a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.

Metabolic and molecular modelling of zebrafish gut biome to unravel antimicrobial peptides through metagenomics

Recently efforts have been taken for unravelling mysteries between host-microbe interactions in gut microbiome studies of model organisms through metagenomics. Co-existence and the co-evolution of the microorganisms is the significant cause of the growing antimicrobial menace. There needs a novel approach to develop potential antimicrobials with capabilities to act directly on the resistant microbes with reduced side effects. One such is to tap them from the natural resources, preferably the gut of the most closely related animal model. In this study, we employed metagenomics approaches to identify the large taxonomic genomes of the zebra fish gut. About 256 antimicrobial peptides were identified using gene ontology predictions from Macrel and Pubseed servers. Upon the property predictions, the top 10 antimicrobial peptides were screened based on their action against many resistant bacterial species, including Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, E. coli, and Bacillus cereus. Metabolic modelling and flux balance analysis (FBA) were computed to conclude the antibiotic such as tetracycline, cephalosporins, puromycin, neomycin biosynthesis pathways were adopted by the microbiome as protection strategies. Molecular modelling strategies, including molecular docking and dynamics, were performed to estimate the antimicrobial peptides' binding against the target-putative nucleic acid binding lipoprotein and confirm stable binding. One specific antimicrobial peptide with the sequence "MPPYLHEIQPHTASNCQTELVIKL" showed promising results with 53% hydrophobic residues and a net charge +2.5, significant for the development of antimicrobial peptides. The said peptide also showed promising interactions with the target protein and expressed stable binding with docking energy of -429.34 kcal/mol and the average root mean square deviation of 1 A⁰. The study is a novel approach focusing on tapping out potential antimicrobial peptides to be developed against most resistant bacterial species.