DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

A novel antimicrobial peptide WBp-1 from wheat bran: Purification, characterization and antibacterial potential against Listeria monocytogenes

This study introduces a novel antimicrobial peptide (AMP), WBp-1, isolated from wheat bran and purified via reversed-phase high-performance liquid chromatography. The amino acid sequence, determined as IITGASSGIGKAIAKHFI by LC-MS/MS, was composed predominantly of alkaline and hydrophobic residues. WBp-1 was predicted to be a stable, hydrophobic, cationic peptide with an α-helical structure. Moreover, it displayed significant antibacterial efficacy against Listeria monocytogenes, with a minimum inhibitory concentration of 150 μg/mL. Further mechanistic studies suggest that WBp-1 exerts its bactericidal activity by disrupting cell membrane integrity, impeding peptidoglycan synthesis by binding to penicillin-binding protein 4 via hydrogen bonding, increasing cell permeability, altering membrane potential and fluidity, and altering surface hydrophobicity. Interestingly, WBp-1 showed minimal hemolytic activity and cytotoxicity against LO2 cells, even at 16× MIC. These findings highlight the strong potential of WBp-1 as a novel antibacterial agent and food preservative against Listeria monocytogenes.

Antibacterial mechanism and structure-activity relationships of Bombyx mori cecropin A

Bombyx mori cecropin A (Bmcecropin A) has antibacterial, antiviral, anti-filamentous fungal and tumour cell inhibition activities and is considered a potential succedaneum for antibiotics. We clarified the antibacterial mechanism and structure-activity relationships and then directed the structure-activity optimization of Bmcecropin A. Firstly, we found Bmcecropin A shows a strong binding force and permeability to cell membranes like a detergent; Bmcecropin A could competitively bind to the cell membrane with the cell membrane-specific dye DiI, then damaged the membrane for the access of DiI into the cytoplasm and leading to the leakage of electrolyte and proteins. Secondly, we found Bmcopropin A could also bind to and degrade DNA; furthermore, DNA library polymerase chain reaction (PCR) results indicated that Bmcecropin A inhibited DNA replication by non-specific binding. In addition, we have identified C-terminus amidation and serine-lysine- glycine (SLG) amino acids of Bmcecropin A played critical roles in the membrane damage and DNA degradation. Based on the above results, we designed a mutant of Bmcecropin A (E9 to H, D17 to K, K33 to A), which showed higher antibacterial activity, thermostability and pH stability than ampicillin but no haemolytic activity. Finally, we speculated that Bmcecropin A damaged the cell membrane through a carpet model and drew the schematic diagram of its antibacterial mechanism, based on the antibacterial mechanism and the three-dimensional configuration. These findings yield insights into the mechanism of antimicrobial peptide-pathogen interaction and beneficial for the development of new antibiotics.

Expression and characterization of the new antimicrobial peptide AP138L-arg26 anti Staphylococcus aureus

The low activity and yield of antimicrobial peptides (AMPs) are pressing problems. The improvement of activity and yield through modification and heterologous expression, a potential way to solve the problem, is a research hot-pot. In this work, a new plectasin-derived variant L-type AP138 (AP138L-arg26) was constructed for the study of recombination expression and druggablity. As a result, the total protein concentration of AP138L-arg26 was 3.1 mg/mL in Pichia pastoris X-33 supernatant after 5 days of induction expression in a 5-L fermenter. The recombinant peptide AP138L-arg26 has potential antibacterial activity against selected standard and clinical Gram-positive bacteria (G+, minimum inhibitory concentration (MIC) 2-16 µg/mL) and high stability under different conditions (temperature, pH, ion concentration) and 2 × MIC of AP138L-arg26 could rapidly kill Staphylococcus aureus (S. aureus) (> 99.99%) within 1.5 h. It showed a high safety in vivo and in vivo and a long post-antibiotic effect (PAE, 1.91 h) compared with vancomycin (1.2 h). Furthermore, the bactericidal mechanism was revealed from two dimensions related to its disruption of the cell membrane resulting in intracellular potassium leakage (2.5-fold higher than control), and an increase in intracellular adenosine triphosphate (ATP), and reactive oxygen species (ROS), the decrease of lactate dehydrogenase (LDH) and further intervening metabolism in S. aureus. These results indicate that AP138L-arg26 as a new peptide candidate could be used for more in-depth development in the future. KEY POINTS: • The AP138L-arg26 was expressed in the P. pastoris expression system with high yield • The AP138 L-arg26 showed high stability and safety in vitro and in vivo • The AP138L-arg26 killed S. aureus by affecting cell membranes and metabolism.

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Integrated computational approaches for advancing antimicrobial peptide development

The increasing prevalence of antimicrobial resistance has intensified the need for novel antimicrobial drugs. Antimicrobial peptides (AMPs) are promising alternative antibiotics due to their broad-spectrum activity and slower resistance development. However, the time-consuming, costly development and challenge of systematic optimization limit their translation into the clinic. Recently, integrating computational methods have led to breakthroughs in the precise design and optimization of AMPs, reduced resource consumption, and accelerated AMP development process. We highlight the application of these integrated approaches in AMP molecule discovery, optimization, and delivery and demonstrate the synergy of these strategies to fuel AMP development.

Novel antimicrobial peptides based on Protegrin-1: In silico and in vitro assessments

The development of antibiotic resistance has caused significant health problems. Antimicrobial peptides (AMPs) are considered next-generation antibiotics. Protegrin-1 (PG-1) is a β-hairpin AMP with a membrane-binding capacity. This study used twelve PG-1 analogs with different amino acid substitutions. Coarse-grained molecular dynamics (MD) simulations were used to assess these analogs, and their physicochemical properties were computed using the Antimicrobial Peptide Database. Three AMPs, PEP-D, PEP-C, and PEP-H, were chosen and synthesized for antibacterial testing. The microbroth dilution technique and hemolytic assays evaluated the antimicrobial efficacy and cellular toxicity. The checkerboard method was used to test the combined activity of AMP and standard antibiotics. Cell membrane permeability and electron microscopy were used to evaluate the mode of action. The chemical stability of the selective AMP, PEP-D, was assessed by a validated HPLC method. PEP-D consists of 16-18 amino acid residues and has a charge of +7 and a hydrophobicity of 44 %, similar to PG-1. It can efficiently inactivate bacteria by disrupting cell membranes and significantly reducing hemolytic activity. Chemical stability studies indicated that AMP was stable at 40 °C for six months under autoclave conditions. This study could introduce the potential therapeutic application of selective AMP as an anti-infective agent.

Beyond the venom: Exploring the antimicrobial peptides from Androctonus species of scorpion

Prevalent worldwide, the Androctonus scorpion genus contributes a vital role in scorpion envenoming. While diverse scorpionisms are observed because of several different species, their secretions to protect themselves have been identified as a potent source of antimicrobial peptide (AMP)-like compounds. Distinctly, the venom of these species contains around 24 different AMPs, with definite molecules studied for their therapeutic potential as antimicrobial, antifungal, antiproliferative and antiangiogenic agents. Our review focuses on the therapeutic potential of native and synthetic AMPs identified so far in the Androctonus scorpion genus, identifying research gaps in peptide therapeutics and guiding further investigations. Certain AMPs have demonstrated remarkable compatibility to be prescribed as anticancer drug to reduce cancer cell proliferation and serve as a potent antibiotic alternative. Besides, analyses were performed to explore the characteristics and affinities of peptides for membranes. Overall, the study of AMPs derived from the Androctonus scorpion genus provides valuable insights into their potential applications in medicine and drug development.

Unveiling Encrypted Antimicrobial Peptides from Cephalopods' Salivary Glands: A Proteolysis-Driven Virtual Approach

Antimicrobial peptides (AMPs) have potential against antimicrobial resistance and serve as templates for novel therapeutic agents. While most AMP databases focus on terrestrial eukaryotes, marine cephalopods represent a promising yet underexplored source. This study reveals the putative reservoir of AMPs encrypted within the proteomes of cephalopod salivary glands via in silico proteolysis. A composite protein database comprising 5,412,039 canonical and noncanonical proteins from salivary apparatus of 14 cephalopod species was subjected to digestion by 5 proteases under three protocols, yielding over 9 million of nonredundant peptides. These peptides were effectively screened by a selection of 8 prediction and sequence comparative tools, including machine learning, deep learning, multiquery similarity-based models, and complex networks. The screening prioritized the antimicrobial activity while ensuring the absence of hemolytic and toxic properties, and structural uniqueness compared to known AMPs. Five relevant AMP datasets were released, ranging from a comprehensive collection of 542,485 AMPs to a refined dataset of 68,694 nonhemolytic and nontoxic AMPs. Further comparative analyses and application of network science principles helped identify 5466 unique and 808 representative nonhemolytic and nontoxic AMPs. These datasets, along with the selected mining tools, provide valuable resources for peptide drug developers.

In-silico and in-vitro study of novel antimicrobial peptide AM1 from Aegle marmelos against drug-resistant Staphylococcus aureus

Antimicrobial peptides have garnered increasing attention as potential alternatives due to their broad-spectrum antimicrobial activity and low propensity for developing resistance. This is for the first time; proteome sequences of Aegle marmelos were subjected to in-silico digestion and AMP prediction were performed using DBAASP server. After screening the peptides on the basis of different physiochemical property, peptide sequence GKEAATKAIKEWGQPKSKITH (AM1) shows the maximum binding affinity with - 10.2 Kcal/mol in comparison with the standard drug (Trimethoprim) with - 7.4 kcal/mol and - 6.8 Kcal/mol for DHFR and SaTrmK enzyme respectively. Molecular dynamics simulation performed for 300ns, it has been found that peptide was able to stabilize the protein more effectively, analysed by RMSD, RMSF, and other statistical analysis. Free binding energy for DHFR and SaTrmK interaction from MMPBSA analysis with peptide was found to be -47.69 and - 44.32 Kcal/mol and for Trimethoprim to be -13.85 Kcal/mol and - 11.67 Kcal/mol respectively. Further in-vitro study was performed against Methicillin Susceptible Staphylococcus aureus (MSSA), Methicillin Resistant Staphylococcus aureus (MRSA), Multi-Drug Resistant Staphylococcus aureus (MDR-SA) strain, where MIC values found to be 2, 4, and 8.5 µg/ml lesser in comparison to trimethoprim which has higher MIC values 2.5, 5, and 9.5 µg/ml respectively. Thus, our study provides the insight for the further in-vivo study of the peptides against multi-drug resistant S. aureus.

Human microbiome derived synthetic antimicrobial peptides with activity against Gram-negative, Gram-positive, and antibiotic resistant bacteria

The prevalence of antibacterial resistance has become one of the major health threats of modern times, requiring the development of novel antibacterials. Antimicrobial peptides are a promising source of antibiotic candidates, mostly requiring further optimization to enhance druggability. In this study, a series of new antimicrobial peptides derived from lactomodulin, a human microbiome natural peptide, was designed, synthesized, and biologically evaluated. Within the most active region of the parent peptide, linear peptide LM6 with the sequence LSKISGGIGPLVIPV-NH2 and its cyclic derivatives LM13a and LM13b showed strong antibacterial activity against Gram-positive bacteria, including resistant strains, and Gram-negative bacteria. The peptides were found to have a rapid onset of bactericidal activity and transmission electron microscopy clearly shows the disintegration of the cell membrane, suggesting a membrane-targeting mode of action.

Host defense peptides in crocodilians - A comprehensive review

Amphibians and reptiles, like all animals, are prone to periodic infections. However, crocodilians stand out for their remarkable ability to remain generally healthy and infection-free despite frequent exposure to a wide variety of microorganisms in their habitats and often sustaining significant injuries. These animals have evolved highly active immune mechanisms that provide rapid and effective defense. This is evidenced by the superior hemolytic capacity of their plasma compared to that of other organisms. To date, several host defense peptides (HDPs) have been identified in crocodilians, including cathelicidins, beta-defensins, hepcidins, leucrocins, hemocidins, and omwaprins. These peptides exhibit potent and broad-spectrum antimicrobial, antibiofilm, antifungal, and anticancer activities. Due to the relatively low but diverse evolutionary rate of crocodilians, the HDPs found in this species offer valuable insights into proteins and mechanisms of action that are highly conserved across many animals related to immune defense. The potential applications of HDPs in modern medicine represent a promising strategy for developing new therapeutic agents. Their novelty and the vast variability with which peptide sequences can be designed and modified expand the field of application for HDPs almost infinitely. This review addresses the urgent need for innovative and more effective drugs to combat the rise of antimicrobialresistant infections and evaluates the potential of crocodilian HDPs. It presents recent advances in the identification of crocodilian HDPs, particularly antimicrobial peptides (AMPs), including previously underexplored topics such as the sequential and structural conformation of different peptide types in crocodilians and the use of bioinformatics tools to enhance native peptides.

Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties

Encrypted peptides (EPs) have been recently described as a new class of antimicrobial molecules. They have been found in numerous organisms and have been proposed to have a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these EPs are found embedded in proteins unrelated to the immune system, suggesting that immunological responses extend beyond traditional host immunity proteins. To test this idea, we synthesized and analyzed representative peptides derived from non-immune human proteins for their ability to exert antimicrobial and immunomodulatory properties. Most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein-1 (MCP-1). Moreover, eight of the peptides identified, collagenin-3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may have a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.

AVR/I/SSAPDB: a comprehensive & specialised knowledgebase of antimicrobial peptides to combat VRSA, VISA, and VSSA

The rise of multi-drug resistant (MDR) bacteria, especially strains of Staphylococcus aureus like Vancomycin-resistant S. aureus (VRSA), Vancomycin-intermediate S. aureus (VISA), and Vancomycin-susceptible S. aureus (VSSA), poses a severe threat to global health. This situation underscores the urgent need for novel antimicrobial agents to combat these resistant strains effectively. Here, we are introducing the Anti-Vancomycin-Resistant/Intermediate/Susceptible Staphylococcus aureus Peptide Database (AVR/I/SSAPDB), a manually curated comprehensive and specialised knowledgebase dedicated to antimicrobial peptides (AMPs) that target VRSA, VISA, and VSSA with clinical and non-clinical significance. Our database sources data from PubMed, cataloging 491 experimentally validated AMPs with detailed annotations on peptides, activity, and cross-references to external databases like PubMed, UniProt, PDB, and DrugBank. AVR/I/SSAPDB offers a user-friendly interface with simple to advanced and list-based search capabilities, enabling researchers to explore AMPs against VRSA, VISA, and VSSA. We are hoping that this resource will be helpful to the scientific community in developing targeted peptide-based therapeutics, providing a crucial tool for combating VRSA, VISA, and VSSA, and addressing a major public health concern. AVR/I/SSAPDB is freely accessible via any web-browser at URL: https://bblserver.org.in/avrissa/ .

AbAMPdb: a database of Acinetobacter baumannii specific antimicrobial peptides

Acinetobacter baumannii has emerged as a prominent nosocomial pathogen, exhibiting a progressive rise in resistance to therapeutic interventions. This rise in resistance calls for alternative strategies. Here, we propose an alternative yet specialized resource on antimicrobial peptides (AMPs) against A. baumannii. Database 'AbAMPdb' is the manually curated collection of 300 entries containing the 250 experimental AMP sequences and 50 corresponding synthetic or mutated AMP sequences. The mutated sequences were modified with reported amino acid substitutions intended for decreasing the toxicity and increasing the antimicrobial potency. AbAMPdb also provides 3D models of all 300 AMPs, comprising 250 natural and 50 synthetic or mutated AMPs. Moreover, the database offers docked complexes comprising 5000 AMPs and their corresponding A. baumannii target proteins. These complexes, accessible in Protein Data Bank format, enable the 2D visualization of the interacting amino acid residues. We are confident that this comprehensive resource furnishes vital information concerning AMPs, encompassing their docking interactions with virulence factors and antibiotic resistance proteins of A. baumannii. To enhance clinical relevance, the characterized AMPs could undergo further investigation both in vitro and in vivo. Database URL: https://abampdb.mgbio.tech/.

MicroEpitope: an atlas of immune epitopes derived from cancer microbiomes

The majority of human cancers harbor molecular evidence of intratumoral microbiota. Microbiota-derived epitopes as molecular mimics of tumor antigens can bind human leukocyte antigen (HLA), thereby modulating host immunity. However, many questions remain regarding the mechanisms underlying the interactions between microbiota and the host's immune system in cancer. Here, MicroEpitope (http://bio-bigdata.hrbmu.edu.cn/MicroEpitope) was developed to provide and analyze the atlas of microbiota-derived epitopes in cancer. We manually collected available mass spectrometry (MS)-based HLA immunopeptidomes of 1190 samples across 24 cancer types. Alignment was performed against an in-house constructed theoretical library of human and intratumor microbiome encoded proteins, including 1298 bacterial and 124 viral species. Currently, MicroEpitope contains 51 497 bacteria and 767 virus-derived epitopes, mainly originating from Bacillus subtilis, Buchnera aphidicola and human cytomegalovirus. The common immunogenic features of epitopes were calculated, as well as their biochemical properties and the clinical relevance of corresponding bacteria and viruses across cancers. MicroEpitope also provides five analytical tools, and multiple visualization methods to facilitate understanding of the roles of microbiota-derived epitopes in cancer immunity. In summary, MicroEpitope represents a vital resource for investigating HLA-presented immunopeptidomes derived from cancer microbiomes, and could further enable rich insight in tumor antigen prioritization strategies.

Enhancing Antimicrobial Peptide Activity through Modifications of Charge, Hydrophobicity, and Structure

Antimicrobial peptides (AMPs) are emerging as a promising alternative to traditional antibiotics due to their ability to disturb bacterial membranes and/or their intracellular processes, offering a potential solution to the growing problem of antimicrobial resistance. AMP effectiveness is governed by factors such as net charge, hydrophobicity, and the ability to form amphipathic secondary structures. When properly balanced, these characteristics enable AMPs to selectively target bacterial membranes while sparing eukaryotic cells. This review focuses on the roles of positive charge, hydrophobicity, and structure in influencing AMP activity and toxicity, and explores strategies to optimize them for enhanced therapeutic potential. We highlight the delicate balance between these properties and how various modifications, including amino acid substitutions, peptide tagging, or lipid conjugation, can either enhance or impair AMP performance. Notably, an increase in these parameters does not always yield the best results; sometimes, a slight reduction in charge, hydrophobicity, or structural stability improves the overall AMP therapeutic potential. Understanding these complex interactions is key to developing AMPs with greater antimicrobial activity and reduced toxicity, making them viable candidates in the fight against antibiotic-resistant bacteria.

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.

Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.

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.

HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides

Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the hemolytic activity of therapeutic peptides is essential for the development of peptide therapies. In this study, a multi-feature cross-fusion model, HemoFuse, for hemolytic peptide identification is proposed. The feature vectors of peptide sequences are transformed by word embedding technique and four hand-crafted feature extraction methods. We apply multi-head cross-attention mechanism to hemolytic peptide identification for the first time. It captures the interaction between word embedding features and hand-crafted features by calculating the attention of all positions in them, so that multiple features can be deeply fused. Moreover, we visualize the features obtained by this module to enhance its interpretability. On the comprehensive integrated dataset, HemoFuse achieves ideal results, with ACC, SP, SN, MCC, F1, AUC, and AP of 0.7575, 0.8814, 0.5793, 0.4909, 0.6620, 0.8387, and 0.7118, respectively. Compared with HemoDL proposed by Yang et al., it is 3.32%, 3.89%, 5.93%, 10.6%, 8.17%, 5.88%, and 2.72% higher. Other ablation experiments also prove that our model is reasonable and efficient. The codes and datasets are accessible at https://github.com/z11code/Hemo .

An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry

Although bioactive peptides have traditionally been studied for their health-promoting qualities in the context of nutrition and medicine, the past twenty years have seen a steady increase in their application to cell culture media optimization. Complex natural sources of bioactive peptides, such as hydrolysates, offer a sustainable and cost-effective means of promoting cellular growth, making them an essential component of scaling-up cultivated meat production. However, the sheer diversity of hydrolysates makes product selection difficult, highlighting the need for functional characterization. Traditional wet-lab techniques for isolating and estimating peptide bioactivity cannot keep pace with peptide identification using high-throughput tools such as mass spectrometry, requiring the development and use of machine learning-based classifiers. This review provides a comprehensive list of available software tools to evaluate peptide bioactivity, classified and compared based on the algorithm, training set, functionality, and limitations of the underlying models. We curated independent test sets to compare the predictive performance of different models based on specific bioactivity classification relevant to promoting cell culture growth: antioxidant and anti-inflammatory. A comprehensive screening of all bioactivity classifiers revealed that while there are approximately fifty tools to elucidate antimicrobial activity and sixteen that predict anti-inflammatory activity, fewer tools are available for other functionalities related to cell growth - five that predict antioxidant activity and two for growth factor and/or cell signaling prediction. A thorough evaluation of the available tools revealed significant issues with sensitivity, specificity, and overall accuracy. Despite the overall interest in estimating peptide bioactivity, our work highlights key gaps in the broader adoption of existing software for the specific application of cell culture media optimization in the context of cultivated meat and beyond.

The Roadmap of Plant Antimicrobial Peptides Under Environmental Stress: From Farm to Bedside

Antimicrobial peptides (AMPs) are the most favorable alternatives in overcoming multidrug resistance, alone or synergistically with conventional antibiotics. Plant-derived AMPs, as cysteine-rich peptides, widely compensate the pharmacokinetic drawbacks of peptide therapeutics. Compared to the putative genes encrypted in the genome, AMPs that are produced under stress are active forms with the ability to combat resistant microbial species. Within this study, plant-derived AMPs, namely, defensins, nodule-specific cysteine-rich peptides, snakins, lipid transfer proteins, hevein-like proteins, α-hairpinins, and aracins, expressed under biotic and abiotic stresses, are classified. We could observe that while α-hairpinins and snakins display a helix-turn-helix structure, conserved motif patterns such as β1αβ2β3 and β1β2β3 exist in plant defensins and hevein-like proteins, respectively. According to the co-expression data, several plant AMPs are expressed together to trigger synergistic effects with membrane disruption mechanisms such as toroidal pore, barrel-stave, and carpet models. The application of AMPs as an eco-friendly strategy in maintaining agricultural productivity through the development of transgenes and bio-pesticides is discussed. These AMPs can be consumed in packaging material, wound-dressing products, coating catheters, implants, and allergology. AMPs with cell-penetrating properties are verified for the clearance of intracellular pathogens. Finally, the dominant pharmacological activities of bioactive peptides derived from the gastrointestinal digestion of plant AMPs, namely, inhibitors of renin and angiotensin-converting enzymes, dipeptidyl peptidase IV and α-glucosidase inhibitors, antioxidants, anti-inflammatory, immunomodulating, and hypolipidemic peptides, are analyzed. Conclusively, as phytopathogens and human pathogens can be affected by plant-derived AMPs, they provide a bright perspective in agriculture, breeding, food, cosmetics, and pharmaceutical industries, translated as farm to bedside.

Discovery of Antimicrobial Peptides in Urinary Tract Infections

Antimicrobial peptides (AMPs) play a pivotal role in the innate immune system as a frontline defense against microbial threats. AMPs can serve as biomarkers and alternative antibiotics, overcoming mortality related to multidrug-resistant pathogens in urinary tract infections (UTIs). While the relevance of AMPs in UTIs has been validated and AMP drugs approved by the US Food and Drug Administration are in clinical use, information about their modification status, regulation, and mechanism of action remains sparse. Only a small fraction of sequences with potential AMP activity, predicted on the basis of known AMP characteristics, have been validated. Elucidation of the global profile of AMPs in the bladder, kidney, and urine under UTI conditions would facilitate an in-depth, disease-specific understanding of the innate immune system and the development of tailored AMP biomarkers and antibiotics. This mini-review focuses on a comprehensive strategy for global profiling and validation of AMPs in UTIs that incorporates AMP data repositories, prediction algorithms, and proteomics for healthy individuals and UTI patients. PATIENT SUMMARY: Short protein molecules called peptides that have antimicrobial activity show promise for the treatment of urinary tract infections. More research and testing of naturally occurring and synthetic peptides with this activity are needed to fully understand how they can help in patient care.

A comprehensive guide on screening and selection of a suitable AMP against biofilm-forming bacteria

Lately, antimicrobial resistance (AMR) is increasing at an exponential rate making it important to search alternatives to antibiotics in order to combat multi-drug resistant (MDR) bacterial infections. Out of the several antibacterial and antibiofilm strategies being tested, antimicrobial peptides (AMPs) have shown to give better hopes in terms of a long-lasting solution to the problem. To select a desired AMP, it is important to make right use of available tools and databases that aid in identification, classification, and analysis of the physiochemical properties of AMPs. To identify the targets of these AMPs, it becomes crucial to understand their mode-of-action. AMPs can also be used in combination with other antibacterial and antibiofilm agents so as to achieve enhanced efficacy against bacteria and their biofilms. Due to concerns regarding toxicity, stability, and bioavailability, strategizing drug formulation at an early-stage becomes crucial. Although there are few concerns regarding development of bacterial resistance to AMPs, the evolution of resistance to AMPs occurs extremely slowly. This comprehensive review gives a deep insight into the selection of the right AMP, deciding the right target and combination strategy along with the type of formulation needed, and the possible resistance that bacteria can develop to these AMPs.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Background

Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens.

Objective

The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets.

Methods

A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models.

Results

XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset.

Conclusion

The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides

Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.

Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

A Critical Review of Short Antimicrobial Peptides from Scorpion Venoms, Their Physicochemical Attributes, and Potential for the Development of New Drugs

Scorpion venoms have proven to be excellent sources of antimicrobial agents. However, although many of them have been functionally characterized, they remain underutilized as pharmacological agents, despite their evident therapeutic potential. In this review, we discuss the physicochemical properties of short scorpion venom antimicrobial peptides (ssAMPs). Being generally short (13-25 aa) and amidated, their proven antimicrobial activity is generally explained by parameters such as their net charge, the hydrophobic moment, or the degree of helicity. However, for a complete understanding of their biological activities, also considering the properties of the target membranes is of great relevance. Here, with an extensive analysis of the physicochemical, structural, and thermodynamic parameters associated with these biomolecules, we propose a theoretical framework for the rational design of new antimicrobial drugs. Through a comparison of these physicochemical properties with the bioactivity of ssAMPs in pathogenic bacteria such as Staphylococcus aureus or Acinetobacter baumannii, it is evident that in addition to the net charge, the hydrophobic moment, electrostatic energy, or intrinsic flexibility are determining parameters to understand their performance. Although the correlation between these parameters is very complex, the consensus of our analysis suggests that there is a delicate balance between them and that modifying one affects the rest. Understanding the contribution of lipid composition to their bioactivities is also underestimated, which suggests that for each peptide, there is a physiological context to consider for the rational design of new drugs.

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

Background

Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development.

Methods

In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps.

Results

In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors.

Conclusion

deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

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.

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Deep-learning-enabled antibiotic discovery through molecular de-extinction

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information

Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.

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.

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.

Small Natural Cyclic Peptides from DBAASP Database

Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks-low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations. As a result, macrocyclic peptides represent attractive drug candidates. Moreover, many drugs are macrocycles that originated from natural product scaffolds, suggesting that nature offers solutions to the challenges faced by AMPs. In this review, we explore natural cyclic peptides from the DBAASP database. DBAASP is a comprehensive repository of data on antimicrobial/cytotoxic activities and structures of peptides. We analyze the data on small (≤25 AA) ribosomal and non-ribosomal cyclic peptides from DBAASP according to their amino acid composition, bonds used for cyclization, targets they act on, and mechanisms of action. This analysis will enhance our understanding of the small cyclic peptides that nature has provided to defend living organisms.

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides

Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for a range of applications from nanoreactors to therapeutics.

Antimicrobial Peptides (AMPs) and the Microbiome in Preterm Infants: Consequences and Opportunities for Future Therapeutics

Antimicrobial peptides (AMPs) are crucial components of the innate immune system in various organisms, including humans. Beyond their direct antimicrobial effects, AMPs play essential roles in various physiological processes. They induce angiogenesis, promote wound healing, modulate immune responses, and serve as chemoattractants for immune cells. AMPs regulate the microbiome and combat microbial infections on the skin, lungs, and gastrointestinal tract. Produced in response to microbial signals, AMPs help maintain a balanced microbial community and provide a first line of defense against infection. In preterm infants, alterations in microbiome composition have been linked to various health outcomes, including sepsis, necrotizing enterocolitis, atopic dermatitis, and respiratory infections. Dysbiosis, or an imbalance in the microbiome, can alter AMP profiles and potentially lead to inflammation-mediated diseases such as chronic lung disease and obesity. In the following review, we summarize what is known about the vital role of AMPs as multifunctional peptides in protecting newborn infants against infections and modulating the microbiome and immune response. Understanding their roles in preterm infants and high-risk populations offers the potential for innovative approaches to disease prevention and treatment.

Recent advances in the development of antimicrobial peptides against ESKAPE pathogens

Multi-drug resistant ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are a global health threat. The severity of the problem lies in its impact on mortality, therapeutic limitations, the threat to public health, and the costs associated with managing infections caused by these resistant strains. Effectively addressing this challenge requires innovative approaches to research, the development of new antimicrobials, and more responsible antibiotic use practices globally. Antimicrobial peptides (AMPs) are a part of the innate immune system of all higher organisms. They are short, cationic and amphipathic molecules with broad-spectrum activity. AMPs interact with the negatively charged bacterial membrane. In recent years, AMPs have attracted considerable interest as potential antibiotics. However, AMPs have low bioavailability and short half-lives, which may be circumvented by chemical modification. This review presents recent in vitro and in silico strategies for the modification of AMPs to improve their stability and application in preclinical experiments.

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.

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/ .

AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Development of a defibrinated human blood hemolysis assay for rapid testing of hemolytic activity compared to computational prediction

Cytotoxicity studies determining hemolytic properties of antimicrobial peptides or other drugs are an important step in the development of novel therapeutics for clinical use. Hemolysis is an affordable, accessible, and rapid method for initial assessment of cellular toxicity for all drugs under development. However, variability in species of red blood cells and protocols used may result in significant differences in results. AMPs generally possess higher selectivity for bacterial cells but can have toxicity against host cells at high concentrations. Knowing the hemolytic activity of the peptides we are developing contributes to our understanding of their potential toxicity. Computational approaches for predicting hemolytic activity of AMPs exist and were tested head-to-head with our experimental results.

RESULTS

Starting with an observation of high hemolytic activity of LL-37 peptide against human red blood cells that were collected in EDTA, we explored alternative approaches to develop a more robust, accurate and simple hemolysis assay using defibrinated human blood. We found significant differences between the sensitivity of defibrinated red blood cells and EDTA treated red blood cells.

SIGNIFICANCE

Accurately determining the hemolytic activity using human red blood cells will allow for a more robust calculation of the therapeutic index of our potential antimicrobial compounds, a critical measure in their pre-clinical development.

CONCLUSION

We introduce a standardized, more accurate protocol for assessing hemolytic activity using defibrinated human red blood cells. This approach, facilitated by the increased commercial availability of de-identified human blood and defibrination methods, offers a robust tool for evaluating toxicity of emerging drug compounds, especially AMPs.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

DCTPep, the data of cancer therapy peptides

With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.

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.