Design Of Antimicrobial Peptides Research Articles

Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms

Abstract The occurrence and spread of antimicrobial resistance (AMR) pose a looming threat to human health around the world. Novel antibiotics are urgently needed to address the AMR crisis. In recent years, antimicrobial peptides (AMPs) have gained increasing attention as potential alternatives to conventional antibiotics due to their abundant sources, structural diversity, broad-spectrum antimicrobial activity, and ease of production. Given its significance, there has been a tremendous advancement in the research and development of AMPs. Numerous AMPs have been identified from various natural sources (e.g., plant, animal, human, microorganism) based on either well-established isolation or bioinformatic pipelines. Moreover, computer-assisted strategies (e.g., machine learning (ML) and deep learning (DL)) have emerged as a powerful and promising technology for the accurate prediction and design of new AMPs. It may overcome some of the shortcomings of traditional antibiotic discovery and contribute to the rapid development and translation of AMPs. In these cases, this review aims to appraise the latest advances in identifying and designing AMPs and their significant antimicrobial activities against a wide range of bacterial pathogens. The review also highlights the critical challenges in discovering and applying AMPs.

In silico Analysis of the Stability of Ribosomal Protein S1 from Various Strains of the Pathogenic Microorganism Staphylococcus aureus.

The pathogenic microorganism Staphylococcus aureus, one of the representatives of the staphylococcus family, is capable of developing resistance to antibiotics and antiseptics. The largest ribosomal protein, S1, from S. aureus contains four structural S1 domains, and short peptides synthesized based on its sequence have amyloidogenic and antimicrobial properties (antimicrobial peptides). In this work, in silico analysis of all available ribosomal protein S1 sequences from various S. aureus strains allowed us to identify residues that are characteristic of specific strains. Based on data from the I-Mutant service, changes in the stability of ribosomal protein S1 from various S. aureus strains were predicted. The results obtained will be used in the future for targeted mutations in the design of new antimicrobial peptides based on ribosomal protein S1.

Designing Analogs of SAAP-148 with Enhanced Antimicrobial and Anti-LPS Activities.

SAAP-148, a derivative of LL-37, exhibits a well-defined amphipathic structure and enhanced antimicrobial activity; however, it also displays significant cytotoxicity towards human cells. In this study, we employed Lys-scan to produce a series of amphiphilic SAAP-148 analogs derived from the SAAP-148 sequence to investigate the impact of the distribution of positively charged residues on the biological viability of the antimicrobial peptides (AMPs). The physical properties and biological activity of the designed peptides were subsequently compared. The substitution of lysine resulted in an increase in the overall charge of SAAP-148 and a decrease in its overall hydrophobicity and hyd. moment, except for SAAP-10 where an analogue substitution occurred at the 18th residue. The replacement of lysine led to a reduction in hemolytic activity compared to SAAP-148, with slightly higher haemolysis rates observed in SAAP-11 and SAAP-13. The cytotoxicity of peptides towards human normal lung epithelial cells (BEAS-2B) was closely linked to their haemolytic activity, indicating that substituting lysine may mitigate the cytotoxic effects of SAAP-148. Additionally, the arrangement of positively charged residues in the peptides significantly influenced its antimicrobial activity. Our findings suggest that the positioning of a positively charged residue has a significant impact on the biophysical properties of the peptide. Additionally, the substitution of lysine at different positions demonstrates an influence on the anti-lipopolysaccharide (anti-LPS) activity of SAAP-148. These discoveries provide valuable insights for the design and optimization of antimicrobial peptides, which will be advantageous for the future development of antimicrobial agents.

De Novo Design and Characterization of Novel Antimicrobial Peptides, Lk3 and Lk4, Containing Repeating Unit

De Novo Design and Characterization of Novel Antimicrobial Peptides, Lk3 and Lk4, Containing Repeating Unit

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.

De novo synthetic antimicrobial peptide design with a recurrent neural network.

Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.

Quantitative structure-activity relationships modelling in antimicrobial peptides design

Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problems such as biological activity, cytotoxicity and bacterial resistance, which is responsible for future developments in new generation of AMPs and optimization in QSAR modeling.

Identification and Design of Novel Potential Antimicrobial Peptides Targeting Mycobacterial Protein Kinase PknB

Antimicrobial peptides have gradually gained advantages over small molecule inhibitors for their multifunctional effects, synthesising accessibility and target specificity. The current study aims to determine an antimicrobial peptide to inhibit PknB, a serine/threonine protein kinase (STPK), by binding efficiently at the helically oriented hinge region. A library of 5626 antimicrobial peptides from publicly available repositories has been prepared and categorised based on the length. Molecular docking using ADCP helped to find the multiple conformations of the subjected peptides. For each peptide served as input the tool outputs 100 poses of the subjected peptide. To maintain an efficient binding for relatively a longer duration, only those peptides were chosen which were seen to bind constantly to the active site of the receptor protein over all the poses observed. Each peptide had different number of constituent amino acid residues; the peptides were classified based on the length into five groups. In each group the peptide length incremented upto four residues from the initial length form. Five peptides were selected for Molecular Dynamic simulation in Gromacs based on higher binding affinity. Post-dynamic analysis and the frame comparison inferred that neither the shorter nor the longer peptide but an intermediate length of 15 mer peptide bound well to the receptor. Residual substitution to the selected peptides was performed to enhance the targeted interaction. The new complexes considered were further analysed using the Elastic Network Model (ENM) for the functional site’s intrinsic dynamic movement to estimate the new peptide’s role. The study sheds light on prospects that besides the length of peptides, the combination of constituent residues equally plays a pivotal role in peptide-based inhibitor generation. The study envisages the challenges of fine-tuned peptide recovery and the scope of Machine Learning (ML) and Deep Learning (DL) algorithm development. As the study was primarily meant for generation of therapeutics for Tuberculosis (TB), the peptide proposed by this study demands meticulous invitro analysis prior to clinical applications.Graphical

Optimizing Antimicrobial Peptide Design: Integration of Cell-Penetrating Peptides, Amyloidogenic Fragments, and Amino Acid Residue Modifications.

The escalating threat of multidrug-resistant pathogens necessitates innovative approaches to combat infectious diseases. In this study, we examined peptides R23FS*, V31KS*, and R44KS*, which were engineered to include an amyloidogenic fragment sourced from the S1 protein of S. aureus, along with one or two cell-penetrating peptide (CPP) components. We assessed the antimicrobial efficacy of these peptides in a liquid medium against various strains of both Gram-positive bacteria, including S. aureus (209P and 129B strains), MRSA (SA 180 and ATCC 43300 strains), and B. cereus (strain IP 5832), and Gram-negative bacteria such as P. aeruginosa (ATCC 28753 and 2943 strains) and E. coli (MG1655 and K12 strains). Peptides R23FS*, V31KS*, and R44KS* exhibited antimicrobial activity comparable to gentamicin and meropenem against all tested bacteria at concentrations ranging from 24 to 48 μM. The peptides showed a stronger antimicrobial effect against B. cereus. Notably, peptide R44KS* displayed high efficacy compared to peptides R23FS* and V31KS*, particularly evident at lower concentrations, resulting in significant inhibition of bacterial growth. Furthermore, modified peptides V31KS* and R44KS* demonstrated enhanced inhibitory effects on bacterial growth across different strains compared to their unmodified counterparts V31KS and R44KS. These results highlight the potential of integrating cell-penetrating peptides, amyloidogenic fragments, and amino acid residue modifications to advance the innovation in the field of antimicrobial peptides, thereby increasing their effectiveness against a broad spectrum of pathogens.

De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks.

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.

A novel family of defensin-like peptides from Hermetia illucens with antibacterial properties.

The world faces a major infectious disease challenge. Interest in the discovery, design, or development of antimicrobial peptides (AMPs) as an alternative approach for the treatment of bacterial infections has increased. Insects are a good source of AMPs which are the main effector molecules of their innate immune system. Black Soldier Fly Larvae (BSFL) are being developed for large-scale rearing for food sustainability, waste reduction and as sustainable animal and fish feed. Bioinformatic studies have suggested that BSFL have the largest number of AMPs identified in insects. However, most AMPs identified in BSF have not yet undergone antimicrobial evaluation but are promising leads to treat critical infections. Jg7197.t1, Jg7902.t1 and Jg7904.t1 were expressed into the haemolymph of larvae following infection with Salmonella enterica serovar Typhimurium and were predicted to be AMPs using the computational tool ampir. The genes encoding these proteins were within 2 distinct clusters in chromosome 1 of the BSF genome. Following removal of signal peptides, predicted structures of the mature proteins were superimposed, highlighting a high degree of structural conservation. The 3 AMPs share primary sequences with proteins that contain a Kunitz-binding domain; characterised for inhibitory action against proteases, and antimicrobial activities. An in vitro antimicrobial screen indicated that heterologously expressed SUMO-Jg7197.t1 and SUMO-Jg7902.t1 did not show activity against 12 bacterial strains. While recombinant SUMO-Jg7904.t1 had antimicrobial activity against a range of Gram-negative and Gram-positive bacteria, including the serious pathogen Pseudomonas aeruginosa. We have cloned and purified putative AMPs from BSFL and performed initial in vitro experiments to evaluate their antimicrobial activity. In doing so, we have identified a putative novel defensin-like AMP, Jg7904.t1, encoded in a paralogous gene cluster, with antimicrobial activity against P. aeruginosa.

A Systematic Review of the Design and Applications of Antimicrobial Peptides in Wound Healing.

The sources of antimicrobial peptides (AMPs), also known as peptide-based antibiotics, are diverse, such as plants, animals, microorganisms including human leukocytes, saliva, human defense peptides, and human sweat. These natural sources provide a rich variety of AMPs with unique characteristics and potential therapeutic applications, including wound-healing and antimicrobial properties. AMPs derived from these sources have shown promise in combating a wide range of pathogens, making them valuable targets for further research and potential clinical applications. The design of AMPs for wound healing involves a meticulous process of structurally optimizing peptides to possess a unique combination of antibacterial and wound-healing characteristics. This systematic review was produced to show the design and applications of AMPs in wound healing. The terms "antimicrobial peptides AND wound healing" were used to search for articles published between September 2023 and January 2010. In the search, we found a total of 12958 articles, of which 12898 were excluded, and the remaining 60 articles were chosen for further study. This systematic review underscores the potential of AMPs as valuable tools in infection control and wound healing, showcasing their versatility and effectiveness in combating a wide range of pathogens. Overall, AMPs in wound healing display a diverse mechanism of action, influencing the inflammatory response, encouraging tissue regeneration, and aiding tissue remodeling, along with strong antibacterial activity. Furthermore, this systematic review addresses AMP toxicity studies, which include rigorous in vitro and in vivo examinations to determine potential cytotoxic effects, systemic toxicity, and any adverse responses connected with its usage in wound-healing applications.

Novel design of simplified β-hairpin antimicrobial peptide as a potential food preservative based on Trp-pocket backbone

Food contamination from microbial deterioration requires the development of potent antimicrobial peptides (AMPs). The deployment of approved AMPs as dietary preservatives is limited due to barriers such as instability, toxicity, and high synthetic costs. This exploration utilizes the primary structural elements of the Trp-pocket backbone to engineer a series of β-hairpin AMPs (XWRWRPGXKXXR-NH2, X representing I, V, F, and/or L). Peptides WpLF, with Phe as X and Leu arranged at the 11th position, demonstrated exceptional selectivity index (SI = 123.08) and sterilization effects both in vitro and in vivo. WpLF consistently exhibited stable bacteriostasis, regardless of physiological salts, serum, and extreme pH. Mechanistic analysis indicated that the peptide penetrates microbial cell membranes, inducing membrane disruption, thereby impeding drug resistance evolution. Conclusively, AMPs engineered by the Trp-pocket skeleton hold substantial potential as innovative biological preservatives in food preservation, providing valuable insights for sustainable and safe peptide-based food preservatives.

Abstract 2231 Revisiting the Translation Initiation in Bacterial Protein Biosynthesis for Rational Design of Antimicrobial Peptides

Abstract 2231 Revisiting the Translation Initiation in Bacterial Protein Biosynthesis for Rational Design of Antimicrobial Peptides

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

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

Rational Design of Amphipathic Antimicrobial Peptides with Alternating L-/D-Amino Acids That Form Helical Structures.

Antimicrobial peptides (AMPs) are promising therapeutic agents against bacteria. We have previously reported an amphipathic AMP Stripe composed of cationic L-Lys and hydrophobic L-Leu/L-Ala residues, and Stripe exhibited potent antimicrobial activity against Gram-positive and Gram-negative bacteria. Gramicidin A (GA), composed of repeating sequences of L- and D-amino acids, has a unique β6.3-helix structure and exhibits broad antimicrobial activity. Inspired by the structural properties and antimicrobial activities of LD-alternating peptides such as GA, in this study, we designed Stripe derivatives with LD-alternating sequences. We found that simply alternating L- and D-amino acids in the Stripe sequence to give StripeLD caused a reduction in antimicrobial activity. In contrast, AltStripeLD, with cationic and hydrophobic amino acids rearranged to yield an amphipathic distribution when the peptide adopts a β6.3-helix, displayed higher antimicrobial activity than AltStripe. These results suggest that alternating L-/D-cationic and L-/D-hydrophobic amino acids in accordance with the helical structure of an AMP may be a useful way to improve antimicrobial activity and develop new AMP drugs.

Design, biological characteristics, and antibacterial mechanism of high therapeutic index antimicrobial peptides with PRRP as central axis.

As the important components of biological innate immunity, antimicrobial peptides (AMPs) were found in a variety of organisms including insects, plants, animals, bacteria, fungi, etc. However, high hemolytic activity, high toxicity, and poor stability of natural AMPs hinder serious their application as therapeutic agents. To overcome these problems, in this study we use PRRP as a central axis, and peptides were designed based on the sequence template XRRXXRXPRRPXRXXRRX-NH2, where X represents a hydrophobic amino acid like Phe (F), Ile (I), Val (V), and Leu (L). The designed peptides LR18, FR18, and IR18 showed effective antimicrobial activity against some Gram-positive bacteria and Gram-negative bacteria, low cytotoxicity to mammalian cells, and had a tendency to form α-helical structures in membrane-mimetic environments. Among them, peptide LR18 (X: L) showed the highest geometric mean average treatment index (GMTI = 42.7) against Gram-negative bacteria, and FR18 (X: L) showed the highest GMTI (22.86) against Gram-positive bacteria. LR18 and FR18 also showed better salt, temperature, pH, and trypsin stability. LR18 and FR18 exert their antimicrobial effects mainly through destroying bacteria cell membrane. Briefly, peptide LR18 and FR18 have the potential to serve as a therapeutic agent to reduce antibiotic resistance owing to its high therapeutic index and great stability.

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

An in silico approach to de novo design of anti-microbial peptide from inspirited Komodo dragon's original VK6 peptide

An in silico approach to de novo design of anti-microbial peptide from inspirited Komodo dragon's original VK6 peptide

Rational design of antimicrobial peptides: an optimization approach

Discovery of new antimicrobial peptide sequences using a computer-aided inverse design framework.