Discovery Of Antimicrobial Peptides Research Articles

Artificial Intelligence Tools Addressing Challenges of Cancer Progression Due to Antimicrobial Resistance in Pathogenic Biofilm Systems

Infections, inflammation, and progression of multifactorial diseases are found to be integratively linked, including most Cancers. Dysfunctional microbiomes are also associated with several cancers in their tumor microenvironments. Antimicrobial peptides (AMPs) are short, positively charged peptides found in a diverse range of species, including bacteria and humans. As host defense peptides, they can destroy pathogenic infections, particularly those that are multidrug resistant. AMPs have raised hopes in the biomedical and pharmaceutical industries as fresh non-antibiotic strategies for combating infectious diseases. However, in vitro and in vivo verification of AMPs is problematic and may miss new antimicrobial drugs. Creating computational methods for quick and precise identification of AMPs and their functional forms is critical for developing new and more effective antimicrobial drugs. Machine learning techniques were recently discovered effective at mining, predicting, and producing efficient antimicrobial peptides from a large AMP database. We reviewed 76 articles, after following literature search rubrics to come to the following conclusions. Distance metric-constant K-based nearest neighbor algorithms (KNN), hidden Markov models (HMMs), support vector machine models (SVMs), random forest models (RFs), decision tree models, and deep neural network (DNN)-based models are some of the most popular AI tools for detecting antimicrobial activity in peptide sequence-derived structure and function. Knowledge graphs can further assist in identifying hub genes and antimicrobial peptides that target and block quorum sensing (QS) signals within the microbial networks. In conclusion, we state that currently no single AI method has been found appropriate for AMP discovery and accurately capable of predicting high-efficacy AMPs. Our current literature review and analysis identify cutting-edge algorithms or innovations that might be included in hybrid machine-learning approaches for the most effective AMP identification, creation, and prediction. Non-peptide, natural molecule-based approaches to AMR reduction are also being studied for development, with natural peptide scaffolds serving as the foundation.

AI Methods for Antimicrobial Peptides: Progress and Challenges.

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.

TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model.

Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery. However, these models tend to suffer from the lack of diversity in generating AMPs. The denoising diffusion probabilistic model serves as a good candidate for solving this issue. We proposed a three-stage Text-Guided Conditional Denoising Diffusion Probabilistic Model (TG-CDDPM) to generate novel and homologous AMPs. In the first two stages, contrastive learning and inferring models are crafted to create better conditions for guiding AMP generation, respectively. In the last stage, a pre-trained conditional denoising diffusion probabilistic model is leveraged to enrich the peptide knowledge and fine-tuned to learn feature representation in downstream. TG-CDDPM was compared to the state-of-the-art generative models for AMP generation, and it demonstrated competitive or better performance with the assistance of text description as supervised information. The membrane penetration capabilities of the identified candidate AMPs by TG-CDDPM were also validated through molecular weight dynamics experiments.

Discovery of AMPs from random peptides via deep learning-based model and biological activity validation

The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of Staphylococcus aureus wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.

Exploration and Characterization of Antimicrobial Peptides from Shrimp Litopenaeus Vannamei by A Genomic and Transcriptomic Approach.

Antimicrobial peptides (AMPs) are crucial in the humoral immunity aspect of invertebrates' innate immune systems. However, studies on AMP discovery in the Pacific white shrimp (Litopenaeus vannamei) using omics data have been limited. Addressing the growing concern of antibiotic resistance in aquaculture, this study focused on the identification and characterization of AMPs in L. vannamei using advanced genomic and transcriptomic techniques. The genome of L. vannamei was performed to predict and identify a total of 754 AMP-derived genes, distributed across most chromosomes and spanning 24 distinct AMP families, and further identified 236 AMP-derived genes at the mRNA level in hemocytes. A subset of 20 chemically synthesized peptides, derived from these genes, exhibited significant antimicrobial activity, with over 85% showing effectiveness against key bacterial strains such as Staphylococcus aureus and Vibrio parahaemolyticus. The expression patterns of these AMPs were also investigated in different shrimp tissues and at various infection stages, revealing dynamic responses to pathogenic challenges. These findings highlight the significant potential of AMPs in L. vannamei as novel, effective alternatives to traditional antibiotics in aquaculture, offering insights into their diverse structural properties and biological functions. Together, this comprehensive characterization of the AMP repertoire in L. vannamei demonstrates the efficacy of using omics data for AMP discovery and lays the groundwork for their potential applications.

Structure-aware deep learning model for peptide toxicity prediction.

Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming and costly. We introduce tAMPer, a novel multi-modal deep learning model designed to predict peptide toxicity by integrating the underlying amino acid sequence composition and the three-dimensional structure of peptides. tAMPer adopts a graph-based representation for peptides, encoding ColabFold-predicted structures, where nodes represent amino acids and edges represent spatial interactions. Structural features are extracted using graph neural networks, and recurrent neural networks capture sequential dependencies. tAMPer's performance was assessed on a publicly available protein toxicity benchmark and an AMP hemolysis data we generated. On the latter, tAMPer achieves an F1-score of 68.7%, outperforming the second-best method by 23.4%. On the protein benchmark, tAMPer exhibited an improvement of over 3.0% in the F1-score compared to current state-of-the-art methods. We anticipate tAMPer to accelerate AMP discovery and development by reducing the reliance on laborious toxicity screening experiments.

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 invitro and invivo. 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.

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features

Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/.

Structure-aware machine learning strategies for antimicrobial peptide discovery

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86–88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Morphological Transformations of SARS-CoV-2 Nucleocapsid Protein Biocondensates Mediated by Antimicrobial Peptides.

Recently, the discovery of antimicrobial peptides (AMPs) as excellent candidates for overcoming antibiotic resistance has attracted significant attention. AMPs are short peptides active against bacteria, cancer cells, and viruses. It has been shown that the SARS-CoV-2 nucleocapsid protein (N-P) undergoes liquid-liquid phase separation in the presence of RNA, resulting in biocondensate formation. These biocondensates are crucial for viral replication as they concentrate the viral RNA with the host cell's protein machinery required for viral protein expression. Thus, N-P biocondensates are promising targets to block or slow down viral RNA transcription and consequently virion assembly. We investigated the ability of three AMPs to interfere with N-P/RNA condensates. Using microscopy techniques, supported by biophysical characterization, we found that the AMP LL-III partitions into the condensate, leading to clustering. Instead, the AMP CrACP1 partitions into the droplets without affecting their morphology but reducing their dynamics. Conversely, GKY20 leads to the formation of fibrillar structures after partitioning. It can be expected that such morphological transformation severely impairs the normal functionality of the N-P droplets and thus virion assembly. These results could pave the way for the development of a new class of AMP-based antiviral agents targeting biocondensates.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models.

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Screening and Functional Analyses of Novel Cecropins from Insect Transcriptome.

Antibiotic resistance is a significant and growing threat to global public health. However, antimicrobial peptides (AMPs) have shown promise as they exhibit a broad spectrum of antibacterial activities with low potential for resistance development. Insects, which inhabit a wide range of environments and are incredibly diverse, remain largely unexplored as a source of novel AMPs. To address this, we conducted a screening of the representative transcriptomes from the 1000 Insect Transcriptome Evolution (1KITE) dataset, focusing on the homologous reference genes of Cecropins, the first identified AMPs in insects known for its high efficiency. Our analysis identified 108 Cecropin genes from 105 insect transcriptomes, covering all major hexapod lineages. We validated the gene sequences and synthesized mature peptides for three identified Cecropin genes. Through minimal inhibition concentration and agar diffusion assays, we confirmed that these peptides exhibited antimicrobial activities against Gram-negative bacteria. Similar to the known Cecropin, the three Cecropins adopted an alpha-helical conformation in membrane-like environments, efficiently disrupting bacterial membranes through permeabilization. Importantly, none of the three Cecropins demonstrated cytotoxicity in erythrocyte hemolysis tests, suggesting their safety in real-world applications. Overall, this newly developed methodology provides a high-throughput bioinformatic pipeline for the discovery of AMP, taking advantage of the expanding genomic resources available for diverse organisms.

Discovery of Antimicrobial Peptides That Can Accelerate Culture Diagnostics of Slow-Growing Mycobacteria Including Mycobacterium tuberculosis.

Antimicrobial peptides (AMPs) can directly kill Gram-positive bacteria, Gram-negative bacteria, mycobacteria, fungi, enveloped viruses, and parasites. At sublethal concentrations, some AMPs and also conventional antibiotics can stimulate bacterial response increasing their resilience, also called the hormetic response. This includes stimulation of growth, mobility, and biofilm production. Here, we describe the discovery of AMPs that stimulate the growth of certain mycobacteria. Peptide 14 showed a growth stimulating effect on Mycobacteria tuberculosis (MTB), M. bovis, M. avium subsp. paratuberculosis (MAP), M. marinum, M. avium-intracellulare, M. celatum, and M. abscessus. The effect was more pronounced at low bacterial inocula. The peptides induce a faster transition from the lag phase to the log phase and keep the bacteria longer in the log phase before entering stationary phase when compared to nontreated controls. In some cases, an increase in the division rate was observed. An initial screen using MAP and a collection of 75 peptides revealed 13 peptides with a hormetic effect. For MTB, a collection of 25 artificial peptides were screened and 13 were found to reduce the time to positivity (TTP) by at least 5%, improving growth. A screen of 43 naturally occurring peptides, 11 fragments of naturally occurring peptides and 5 designed peptides, all taken from the database APD3, identified a further 44 peptides that also lowered TTP by at least 5%. Lasioglossin LL-III (Bee) and Ranacyclin E (Frog) were the most active natural peptides, and the human cathelicidin LL37 fragment GF-17 and a porcine cathelicidin protegrin-1 fragment were the most active fragments of naturally occurring peptides. Peptide 14 showed growth-stimulating activity between 10 ng/mL and 10 µg/mL, whereas the stability-optimised Peptide 14D had a narrow activity range of 0.1-1 µg/mL. Peptides identified in this study are currently in commercial use to improve recovery and culture for the diagnostics of mycobacteria in humans and animals.

Novel integrated computational AMP discovery approaches highlight diversity in the helminth AMP repertoire.

Antimicrobial Peptides (AMPs) are immune effectors that are key components of the invertebrate innate immune system providing protection against pathogenic microbes. Parasitic helminths (phylum Nematoda and phylum Platyhelminthes) share complex interactions with their hosts and closely associated microbiota that are likely regulated by a diverse portfolio of antimicrobial immune effectors including AMPs. Knowledge of helminth AMPs has largely been derived from nematodes, whereas the flatworm AMP repertoire has not been described. This study highlights limitations in the homology-based approaches, used to identify putative nematode AMPs, for the characterisation of flatworm AMPs, and reveals that innovative algorithmic AMP prediction approaches provide an alternative strategy for novel helminth AMP discovery. The data presented here: (i) reveal that flatworms do not encode traditional lophotrochozoan AMP groups (Big Defensin, CSαβ peptides and Myticalin); (ii) describe a unique integrated computational pipeline for the discovery of novel helminth AMPs; (iii) reveal >16,000 putative AMP-like peptides across 127 helminth species; (iv) highlight that cysteine-rich peptides dominate helminth AMP-like peptide profiles; (v) uncover eight novel helminth AMP-like peptides with diverse antibacterial activities, and (vi) demonstrate the detection of AMP-like peptides from Ascaris suum biofluid. These data represent a significant advance in our understanding of the putative helminth AMP repertoire and underscore a potential untapped source of antimicrobial diversity which may provide opportunities for the discovery of novel antimicrobials. Further, unravelling the role of endogenous worm-derived antimicrobials and their potential to influence host-worm-microbiome interactions may be exploited for the development of unique helminth control approaches.

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.

Drug Discovery Efforts at George Mason University.

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

Antimicrobial Proteins: Structure, Molecular Action, and Therapeutic Potential.

Second- and third-line treatments of patients with antibiotic-resistant infections can have serious side effects, such as organ failure with prolonged care and recovery. As clinical practices such as cancer therapies, chronic disease treatment, and organ transplantation rely on the ability of available antibiotics to fight infection, the increased resistance of microbial pathogens presents a multifaceted, serious public health concern worldwide. The pipeline of traditional antibiotics is exhausted and unable to overcome the continuously developing multi-drug resistance. To that end, the widely observed limitation of clinically utilized antibiotics has prompted researchers to find a clinically relevant alternate antimicrobial strategy. In recent decades, the discovery of antimicrobial peptides (AMPs) as an excellent candidate to overcome antibiotic resistance has received further attention, particularly from scientists, health professionals, and the pharmaceutical industry. Effective AMPs are characterized by a broad spectrum of antimicrobial activities, high pathogen specificity, and low toxicity. In addition to their antimicrobial activity, AMPs have been found to be involved in a variety of biological functions, including immune regulation, angiogenesis, wound healing, and antitumor activity. This review provides a current overview of the structure, molecular action, and therapeutic potential of AMPs.

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.

Antibiotic discovery in the artificial intelligence era.

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.