Candidate Antimicrobial Peptides Research Articles

DsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation

Antibiotic resistance is a growing public health challenge. Antimicrobial peptides (AMPs) effectively target microorganisms through non-specific mechanisms, limiting their ability to develop resistance. Therefore, the prediction and design of new AMPs is crucial. Recently, deep learning has spurred interest in computational approaches to peptide drug discovery. This study presents a novel deep learning framework for AMP classification, function prediction, and generation. We developed discoverAMP (dsAMP), a robust AMP predictor using CNN Attention BiLSTM and transfer learning, which outperforms existing classifiers. In addition, dsAMPGAN, a Generative Adversarial Network (GAN)-based model, generates new AMP candidates. Our results demonstrate the superior performance of dsAMP in terms of sensitivity, specificity, Matthew correlation coefficient, accuracy, precision, F1 score, and area under the ROC curve, achieving >95% classification accuracy with transfer learning on a small dataset. Furthermore, dsAMPGAN successfully synthesizes AMPs similar to natural ones, as confirmed by comparisons of physical and chemical properties. This model serves as a reliable tool for the identification of novel AMPs in clinical settings and supports the development of AMPs to effectively combat antibiotic resistance.

Deciphering the Intracellular Action of the Antimicrobial Peptide A11 via an In-Depth Analysis of Its Effect on the Global Proteome of Acinetobacter baumannii.

The potential antimicrobial activity and low propensity to induce the development of bacterial resistance have rendered antimicrobial peptides (AMPs) as novel and ideal candidate therapeutic agents for the treatment of infections caused by drug-resistant pathogenic bacteria. The targeting of bacterial membranes by AMPs has been typically considered their sole mode of action; however, increasing evidence supports the existence of multiple and complementary functions of AMPs that result in bacterial death. An in-depth characterization of their mechanism of action could facilitate further research and development of AMPs with higher potency. The current study employs biophysics and proteomics approaches to unveil the mechanisms underlying the antibacterial activity of A11, a potential candidate AMP, against Acinetobacter baumannii, a leading cause of hospital-acquired infections (HAIs) and consequently, a serious global threat. A11 peptide was found to induce membrane depolarization to a high extent, as revealed by flow cytometry and electron microscopy analyses. The prompt intracellular penetration of A11 peptide, observed using confocal microscopy, was found to occur concomitantly with a very low degree of membrane lysis, suggesting that its mode of action predominantly involves a nonlytic killing mechanism. Quantitative proteomics analysis employed for obtaining insights into the mechanisms underlying the antimicrobial activity of A11 peptide revealed that it disrupted energy metabolism, interfered with protein homeostasis, and inhibited fatty acid synthesis that is essential for cell membrane integrity; all these impacted the cellular functions of A. baumannii. A11 treatment also impacted signal transduction associated with the regulation of biofilm formation, hindered the stress response, and influenced DNA repair processes; these are all crucial survival mechanisms of A. baumannii. Additionally, robust antibacterial activity was exhibited by A11 peptide against multidrug-resistant (MDR) and extensively drug-resistant (XDR) clinical isolates of A. baumannii; moreover, A11 peptide exhibited synergy with levofloxacin and minocycline as well as low propensity for inducing resistance. Taken together, the findings emphasize the therapeutic potential of A11 peptide as an antibacterial agent against drug-resistant A. baumannii and underscore the need for further investigation.

A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides

Background: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective: In this study, we proposed a novel filtration method using knowledge-based approaches to discover encrypted AMPs within a protein sequence. Methods: The encrypted AMPs were selected from a protein sequence, in this case, lactoferrin, based on hydrophobicity, cationicity, alpha-helix structure, helical wheel projection, and binding affinities to gram-negative and positive bacterial membranes. Results: Six out of 20 potential encrypted AMPs were ultimately selected for further assays. Molecular docking of the selected AMPs with outer and inner membranes of gram-negative bacteria and also gram-positive bacterial membranes showed reasonable binding affinity ranging from ‘-6.7 to -7.5’ and ‘- 4.5 to -5.7’ and ‘-4.6 to -5.7’ kcal/mol, respectively. No toxicity was shown in the candidate AMPs. Conclusion: According to in silico results, our method succeeded to discover six new encrypted AMPs from human lactoferrin, designated as lactoferrin-derived peptides (LDPs). Further in silico and experimental assays should also be performed to prove the efficiency of our knowledge-based filtration method.

Peptide-Membrane Docking and Molecular Dynamic Simulation of In Silico Detected Antimicrobial Peptides from Portulaca oleracea's Transcriptome.

The main issue with clinical infections is multidrug resistance to traditional antibiotics. As they are essential to innate immunity, shielding hosts from pathogenic microbes, traditional herbal remedies are an excellent supplier of antimicrobial peptides (AMPs), vital parts of defensive systems. Nevertheless, little is known about the bioactive peptide components of most ethnobotanical species. Our goal in this study was to find new, likely AMPs from Portulaca oleracea (P. oleracea) using in silico studies. The P. oleracea transcriptome was gained from Sequence Read Archive (SRA) and quality controlled, then adapters and other low-quality reads were trimmed. Afterward, de novo assembled and translated open reading frames (ORFs) were determined. Next, the ORFs were filtered based on AMP physiochemical criteria and deep learning methods. Finally, the five selected putative AMPs docked with E. coli and S. aureus membranes that showed penetration in bilayers. In this step, PO2 was chosen as a candidate AMP to analyze with molecular dynamics (MD) simulations. Our data demonstrated that PO2 is more stable in E. coli than in S. aureus. Moreover, these predicted AMPs can be good candidates for in vitro and in vivo analysis.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

Proteomic and antimicrobial peptide analyses of Buffalo colostrum and mature Milk whey: A comparative study

Buffalo colostrum is the initial mammary secretion after parturition, consisting of nutritional and bioactive components. In this study, we conducted a proteomic analysis of buffalo colostrum whey to identify bioactive proteins and peptides. A total of 107 differentially expressed proteins (DEPs) were identified in buffalo colostrum whey compared to those in mature milk. Gene Ontology analysis revealed that DEPs were primarily associated with immune response and tissue development. KEGG pathway enrichment suggested that colostrum actively enhances nascent immunity involved in interleukin and interferon signaling pathways. Furthermore, candidate antimicrobial peptides (AMPs) of whey protein hydrolysates from buffalo colostrum were characterized, which exhibits broad-spectrum activity against gram-positive and gram-negative pathogens. Overall, this study improves our understanding of protein variations in buffalo lactation, and contributes to the development of AMPs from buffalo colostrum.

Diversity and Molecular Evolution of Antimicrobial Peptides in Caecilian Amphibians.

Antimicrobial peptides (AMPs) are key molecules in the innate immune defence of vertebrates with rapid action, broad antimicrobial spectrum, and ability to evade pathogen resistance mechanisms. To date, amphibians are the major group of vertebrates from which most AMPs have been characterised, but most studies have focused on the bioactive skin secretions of anurans (frogs and toads). In this study, we have analysed the complete genomes and/or transcriptomes of eight species of caecilian amphibians (order Gymnophiona) and characterised the diversity, molecular evolution, and antimicrobial potential of the AMP repertoire of this order of amphibians. We have identified 477 candidate AMPs within the studied caecilian genome and transcriptome datasets. These candidates are grouped into 29 AMP families, with four corresponding to peptides primarily exhibiting antimicrobial activity and 25 potentially serving as AMPs in a secondary function, either in their entirety or after cleavage. In silico prediction methods were used to identify 62 of those AMPs as peptides with promising antimicrobial activity potential. Signatures of directional selection were detected for five candidate AMPs, which may indicate adaptation to the different selective pressures imposed by evolutionary arms races with specific pathogens. These findings provide encouraging support for the expectation that caecilians, being one of the least-studied groups of vertebrates, and with ~300 million years of separate evolution, are an underexplored resource of great pharmaceutical potential that could help to contest antibiotic resistance and contribute to biomedical advance.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Exploring the antimicrobial peptidome of nematodes through phylum-spanning in silico analyses highlights novel opportunities for pathogen control.

Antimicrobial Peptides (AMPs) are key constituents of the invertebrate innate immune system and provide critical protection against microbial threat. Nematodes display diverse life strategies where they are exposed to heterogenous, microbe rich, environments highlighting their need for an innate immune system. Within the Ecdysozoa, arthropod AMPs have been well characterised, however nematode-derived AMP knowledge is limited. In this study the distribution and abundance of putative AMP-encoding genes was examined in 134 nematode genomes providing the most comprehensive profile of AMP candidates within phylum Nematoda. Through genome and transcriptome analyses we reveal that phylum Nematoda is a rich source of putative AMP diversity and demonstrate (i) putative AMP group profiles that are influenced by nematode lifestyle where free-living nematodes appear to display enriched putative AMP profiles relative to parasitic species; (ii) major differences in the putative AMP profiles between nematode clades where Clade 9/V and 10/IV species possess expanded putative AMP repertoires; (iii) AMP groups with highly restricted profiles (e.g. Cecropins and Diapausins) and others [e.g. Nemapores and Glycine Rich Secreted Peptides (GRSPs)] which are more widely distributed; (iv) complexity in the distribution and abundance of CSαβ subgroup members; and (v) that putative AMPs are expressed in host-facing life stages and biofluids of key nematode parasites. These data indicate that phylum Nematoda displays diversity in putative AMPs and underscores the need for functional characterisation to reveal their role and importance to nematode biology and host-nematode-microbiome interactions.

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.

Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains

Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them, named GAN-pep 1-8, were selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.

Exploration of Biological Properties and Antibacterial Action against Escherichia coli and Staphylococcus aureus of (LLKK)3‐Derived Peptides

AbstractIn an attempt to develop new antimicrobial peptides (AMPs) with better biological properties, three AMPs of (LLKK)3 analogues were designed and synthesized. The antibacterial activity, cell toxicity and pepsin stability of the three AMPs were determined, and the antibacterial action of the candidate AMP was analyzed. The results showed that the G(LLKK)3L AMP had the stronger antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) compared to the other AMPs. The hemolytic activity and cytotoxicity of the G(LLKK)3L and G(LLKK)3 AMPs were lower than those of (LLKK)3 and (LLKK)3L AMPs, and the pepsin resistance activity of the G(LLKK)3L AMP was higher than that of the other AMPs. Under the treatment of G(LLKK)3L AMP, the cell morphology of the E. coli and S. aureus was irregular and wrinkled, their cell membrane was damaged, and the synthesis of their cell wall peptidoglycan was inhibited. Additionally, the G(LLKK)3L AMP resulted in a decrease of protein expression and inhibition of DNA synthesis. In summary, the G(LLKK)3L AMP exhibited better biological properties, and it can be used as a therapeutic agent against E. coli and S. aureus.

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure-activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4-8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

Krein support vector machine classification of antimicrobial peptides.

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

Application of a deep generative model produces novel and diverse functional peptides against microbial resistance

Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic’s world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.

AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.

Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to traditional antimicrobial drugs. In this study, we developed an improved AMP classification model, called AMP-BERT. We propose a deep learning model with a fine-tuned didirectional encoder representations from transformers (BERT) architecture designed to extract structural/functional information from input peptides and identify each input as AMP or non-AMP. We compared the performance of our proposed model and other machine/deep learning-based methods. Our model, AMP-BERT, yielded the best prediction results among all models evaluated with our curated external dataset. In addition, we utilized the attention mechanism in BERT to implement an interpretable feature analysis and determine the specific residues in known AMPs that contribute to peptide structure and antimicrobial function. The results show that AMP-BERT can capture the structural properties of peptides for model learning, enabling the prediction of AMPs or non-AMPs from input sequences. AMP-BERT is expected to contribute to the identification of candidate AMPs for functional validation and drug development. The code and dataset for the fine-tuning of AMP-BERT is publicly available at https://github.com/GIST-CSBL/AMP-BERT.

Associating Biological Activity and Predicted Structure of Antimicrobial Peptides from Amphibians and Insects.

Antimicrobial peptides (AMPs) are a diverse class of short, often cationic biological molecules that present promising opportunities in the development of new therapeutics to combat antimicrobial resistance. Newly developed in silico methods offer the ability to rapidly discover numerous novel AMPs with a variety of physiochemical properties. Herein, using the rAMPage AMP discovery pipeline, we bioinformatically identified 51 AMP candidates from amphibia and insect RNA-seq data and present their in-depth characterization. The studied AMPs demonstrate activity against a panel of bacterial pathogens and have undetected or low toxicity to red blood cells and human cultured cells. Amino acid sequence analysis revealed that 30 of these bioactive peptides belong to either the Brevinin-1, Brevinin-2, Nigrocin-2, or Apidaecin AMP families. Prediction of three-dimensional structures using ColabFold indicated an association between peptides predicted to adopt a helical structure and broad-spectrum antibacterial activity against the Gram-negative and Gram-positive species tested in our panel. These findings highlight the utility of associating the diverse sequences of novel AMPs with their estimated peptide structures in categorizing AMPs and predicting their antimicrobial activity.

Exploring Active Peptides with Antimicrobial Activity In Planta against Xylella fastidiosa.

Xylella fastidiosa (Xf) is a xylem-limited quarantine plant bacterium and one of the most harmful agricultural pathogens across the world. Despite significant research efforts, neither a direct treatment nor an efficient strategy has yet been developed for combatting Xylella-associated diseases. Antimicrobial peptides (AMPs) have been gaining interest as a promising sustainable tool to control pathogens due to their unique mechanism of action, broad spectrum of activity, and low environmental impact. In this study, we disclose the bioactivity of nine AMPs reported in the literature to be efficient against human and plant pathogen bacteria, i.e., Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa, against Xf, through in vitro and in vivo experiments. Based on viable-quantitative PCR (v-qPCR), fluorescence microscopy (FM), optical density (OD), and transmission electron microscopy (TEM) assays, peptides Ascaphin-8 (GF19), DASamP1 (FF13), and DASamP2 (IL14) demonstrated the highest bactericidal and antibiofilm activities and were more efficient than the peptide PB178 (KL29), reported as one of the most potent AMPs against Xf at present. Furthermore, these AMPs showed low to no toxicity when tested on eukaryotic cells. In in planta tests, no Xf disease symptoms were noticed in Nicotiana tabacum plants treated with the AMPs 40 days post inoculation. This study highlighted the high antagonistic activity of newly tested AMP candidates against Xf, which could lead to the development of promising eco-friendly management of Xf-related diseases.

'Targeting' the search: An upgraded structural and functional repository of antimicrobial peptides for biofilm studies (B-AMP v2.0) with a focus on biofilm protein targets.

Bacterial biofilms, often as multispecies communities, are recalcitrant to conventional antibiotics, making the treatment of biofilm infections a challenge. There is a push towards developing novel anti-biofilm approaches, such as antimicrobial peptides (AMPs), with activity against specific biofilm targets. In previous work, we developed Biofilm-AMP, a structural and functional repository of AMPs for biofilm studies (B-AMP v1.0) with more than 5000 structural models of AMPs and a vast library of AMP annotations to existing biofilm literature. In this study, we present an upgraded version of B-AMP, with a focus on existing and novel bacterial biofilm targets. B-AMP v2.0 hosts a curated collection of 2502 biofilm protein targets across 473 bacterial species, with structural protein models and functional annotations from PDB, UniProt, and PubMed databases. The biofilm targets can be searched for using the name of the source organism, and function and type of protein, and results include designated Target IDs (unique to B-AMP v2.0), UniProt IDs, 3D predicted protein structures, PDBQT files, pre-defined protein functions, and relevant scientific literature. To present an example of the combined applicability of both, the AMP and biofilm target libraries in the repository, we present two case studies. In the first case study, we expand an in silico pipeline to evaluate AMPs against a single biofilm target in the multidrug resistant, bacterial pathogen Corynebacterium striatum, using 3D protein-peptide docking models from previous work and Molecular Dynamics simulations (~1.2µs). In the second case study, we build an in silico pipeline to identify candidate AMPs (using AMPs with both anti-Gram positive and anti-Gram negative activity) against two biofilm targets with a common functional annotation in Pseudomonas aeruginosa and Staphylococcus aureus, widely-encountered bacterial co-pathogens. With its enhanced structural and functional capabilities, B-AMP v2.0 serves as a comprehensive resource for AMP investigations related to biofilm studies. B-AMP v2.0 is freely available at https://b-amp.karishmakaushiklab.com and will be regularly updated with structural models of AMPs and biofilm targets, as well as 3D protein-peptide interaction models for key biofilm-forming pathogens.

Studying Lipid Membrane Interactions of a Super-Cationic Peptide in Model Membranes and Living Bacteria.

The super-cationic peptide dendrimers (SCPD) family is a valuable class of antimicrobial peptide candidates for the future development of antibacterial agents against multidrug-resistant gram-negative bacteria. The deep knowledge of their mechanism of action is a major challenge in research, since it may be the basis for future modifications/optimizations. In this work we have explored the interaction between SCPD and membranes through biophysical and microbiological approaches in the case of the G1OLO-L2OL2 peptide. Results support the idea that the peptide is not only adsorbed or close to the surface of the membrane but associated/absorbed to some extent to the hydrophobic-hydrophilic region of the phospholipids. The presence of low concentrations of the peptide at the surface level is concomitant with destabilization of the cell integrity and this may contribute to osmotic stress, although other mechanisms of action cannot be ruled out.