Design Of Antimicrobial Peptides Research Articles

In Silico Studies in Antimicrobial Peptides Design and Development

Antimicrobial peptides (AMPs) are a group of natural-derived molecules exhibited broad spectrum antimicrobial activity. Currently these molecules have been investigated comprehensively due to their interesting features regarding antimicrobial and immunomodulatory mode of actions which placed them as promising therapeutics agents in this post antibiotics era. Numerous strategies have been implemented in order to develop a novel AMP for biotechnology and therapeutics applications, one of which is in silico study.. This approach offers a rapid and cost effective manner in AMPs design and development. In silico studies provide additional and substantial information for in vitro techniques. In this paper, we deliver an overview of the applicable in silico approaches that have been used in designing and developing AMPs.

Rational Design of Short Peptide Variants by Using Kunitzin-RE, an Amphibian-Derived Bioactivity Peptide, for Acquired Potent Broad-Spectrum Antimicrobial and Improved Therapeutic Potential of Commensalism Coinfection of Pathogens

Commensalism coinfection of pathogens has seriously jeopardized human health. Currently, Kunitzin-RE, as an amphibian-derived bioactivity peptide, is regarded as a potential antimicrobial candidate. However, its antimicrobial properties were unsatisfactory. In this study, a set of shortened variants of Kunitzin-RE was developed by the interception of a peptide fragment and single-site mutation to investigate the effect of chain length, positive charge, hydrophobicity, amphipathicity, and secondary structure on antimicrobial properties. Among them, W8 (AARIILRWRFR) significantly broadened the antimicrobial spectrum and showed the highest antimicrobial activity (GMall = 2.48 μM) against all the fungi and bacteria tested. Additionally, W8 showed high cell selectivity and salt tolerance in vitro, whereas it showed high effectiveness against mice keratitis cause by infection by C. albicans 2.2086. Additionally, it also had obviously lipopolysaccharide-binding ability and a potent membrane-disruptive mechanism. Overall, these findings contributed to the design of short antimicrobial peptides and to combat the serious threat of commensalism coinfection of pathogens.

Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia

The Bac2A (RLARIVVIRVAR−NH2) is a linearized counterpart of cationic cyclic peptide Bactenecin—one of the smallest naturally occurring antimicrobial peptides (AMPs), which, however, generally exhibits a low or moderate antibacterial potency against gram-positive bacteria. Here, it is found that the Bac2A and its linear derivates cannot spontaneously fold into a well-defined helical conformation in solution, thus impairing the peptide amphipathicity and antibacterial activity. Hydrocarbon stapling is rationally designed to constrain the helical conformation of these linear peptides. Atomistic dynamics simulations reveal that the membrane-penetrating course of linear and stapled peptides include four distinct phases, during which the stapled peptides can maintain in an ordered helical conformation, while linear peptides are structured from intrinsic disorder in water solution to partially helical state in membrane interior, indicating that lipid environment can help the linear peptide refolding into amphipathic helix, although the refolding process would incur a large configurational entropy penalty. The antibacterial activities of the most potent stapled peptide are determined as MIC = 7.6 and 16 µg/ml against two gram-positive Staphylococcus aureus clinical strains isolated from pyemia. The activity values are improved by 7.1-fold and 5-fold as compared to that of native Bac2A peptide with MIC = 54 and 80 µg/ml, respectively.

AMAP: Hierarchical multi-label prediction of biologically active and antimicrobial peptides

Due to increase in antibiotic resistance in recent years, development of efficient and accurate techniques for discovery and design of biologically active peptides such as antimicrobial peptides (AMPs) has become essential. The screening of natural and synthetic AMPs in the wet lab is a challenge due to time and cost involved in such experiments. Bioinformatics methods can be used to speed up discovery and design of antimicrobial peptides by limiting the wet-lab search to promising peptide sequences. However, most such tools are typically limited to the prediction of whether a peptide exhibits antimicrobial activity or not and they do not identify the exact type of the biological activities of these peptides. In this work, we have designed a machine learning based model called AMAP for predicting biological activity of peptides with a specialized focus on antimicrobial activity prediction. AMAP used multi-label classification to predict 14 different types of biological functions of a given peptide sequence with improved accuracy in comparison to existing state of the art techniques. We have performed stringent performance analyses of the proposed method. In addition to cross-validation and performance comparison with existing AMP predictors, AMAP has also been benchmarked on recently published experimentally verified peptides that were not a part of our training set. We have also analyzed features used in this work and our analysis shows that the proposed predictor can generalize well in predicting biological activity of novel peptide sequences. A webserver of the proposed method is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#AMAP.

Topological effects on the designability and bactericidal potency of antimicrobial peptides

New ideas and methods are being developed to generate highly designable small functional protein folds beyond the confines of natural structures, from secondary to quaternary level. Highly designable folds can have multiple sequence solutions, which are thermodynamically and kinetically stable. We have previously described how short syndiotactic helices can be exceptionally stable energetically, and how they can be used as a template for designing antibacterial agents. In this work, we have designed four syndiotactic, single turn, amphipathic; cationic 7-mer peptides which are the sequence and structural subset of earlier published 12-mer sequences. We examined the stability of the designed structures and its effects on the biological activity of such short peptide sequences. This was achieved by making objective comparisons between 12-mer and 7-mer sequences in terms of their antibacterial activity. Further, we investigated the mechanistic origins of clearly different bactericidal potency of single (7-mer) and double (12-mer) turn syndiotactic helices using molecular dynamics simulations. Our results suggest that conformationally constrained stable short double turn peptide scaffolds are highly designable, whereas single turn structures are more likely to be disordered. The stability of the designed peptide structure provides a platform for inclusion of multiple sequence variables and defined electrostatic fingerprints. Therefore, a stable peptide scaffold along with pre-defined electrostatic signatures can together be utilized for the design of novel antimicrobial peptides.

A Uniform In Vitro Efficacy Dataset to Guide Antimicrobial Peptide Design

Antimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data are now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms of Gram-negative, Gram-positive, mycobacterial, and fungal origin. We also present circular dichroism spectra for all antimicrobial peptides. We draw simple inferences from this data, and we discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.

Rational Design of Polyleucine-Based Antimicrobial Peptides as Promising Agents Against Cancer Cells

Rational Design of Polyleucine-Based Antimicrobial Peptides as Promising Agents Against Cancer Cells

Design and activity study of a melittin\u2013thanatin hybrid peptide

The unique antimicrobial mechanism of antimicrobials make them a promising substitute for antibiotics for fighting drug-resistant bacteria. Both melittin and thanatin have antimicrobial bioactivity. However, thanatin does not inhibit the growth of Staphylococcus aureus. Melittin can inhibit S. aureus and has strong hemolytic activity. In the present study, the mutant fragments of melittin and thanatin were combined by flexible peptides to form a novel hybrid peptide, which was synthesized based on the secondary and tertiary structure prediction. The hybrid peptide inhibited S. aureus with a hemolytic concentration of above 45 μmol/L and inhibition rate in SMMC-7721 cells of 19.14%. The hybrid antimicrobial peptide, which was designed by the combination of α-helix and β-lamellar antimicrobial peptides, showed that both types of peptides did not interact with each either on spatial structure or biological activities, thereby providing a novel idea for the design of artificial antimicrobial peptides.

Rational design of novel amphipathic antimicrobial peptides focused on the distribution of cationic amino acid residues.

Antimicrobial peptides (AMPs) have garnered much attention as novel therapeutic agents against infectious diseases. They exhibit antimicrobial activity through microbial membrane disruption based on their amphipathic properties. In this study, we rationally designed and synthesized a series of novel AMPs Block, Stripe, and Random, and revealed that Stripe exhibits potent antimicrobial activity against Gram-positive and Gram-negative microbes. Moreover, we also demonstrated that Stripe disrupts both Gram-positive and Gram-negative mimetic bacterial membranes. Finally, we investigated the hemolytic activity and cytotoxicity in human blood cells and human cell lines, and found that Stripe exhibited neither. These data indicated that Stripe is a promising antimicrobial reagent that does not display significant cytotoxicity.

Linking sequence patterns and functionality of alpha-helical antimicrobial peptides.

The rational design of antimicrobial peptides (AMPs) with increased therapeutic potential requires deep understanding of the determinants of their activities. Inspired by the computational linguistic approach, we hypothesized that sequence patterns may encode the functional features of AMPs. We found that α-helical and β-sheet peptides have non-intersecting pattern sets and therefore constructed new sequence templates using only helical patterns. Designed peptides adopted an α-helical conformation upon binding to lipids, confirming that the method captures structural and biophysical properties. In the antimicrobial assay, 5 of 7 designed peptides exhibited activity against Gram(+) and Gram(-) bacteria, with most potent candidate comparable to best natural peptides. We thus conclude that sequence patterns comprise the structural and functional features of α-helical AMPs and guide their efficient design. Supplementary data are available at Bioinformatics online.

Design of antimicrobial peptides from a cuttlefish database.

No antimicrobial peptide has been identified in cephalopods to date. Annotation of transcriptomes or genomes using basic local alignment Search Tool failed to yield any from sequence identities. Therefore, we searched for antimicrobial sequences in the cuttlefish (Sepia officinalis) database by in silico analysis of a transcriptomic database. Using an original approach based on the analysis of cysteine-free antimicrobial peptides selected from our Antimicrobial Peptide Database (APD3), the online prediction tool of the Collection of Anti-Microbial Peptides (CAMPR3), and a homemade software program, we identified potential antibacterial sequences. Nine peptides less than 25 amino acids long were synthesized. The hydrophobic content of all nine of them ranged from 30 to 70%, and they could form alpha-helices. Three peptides possessed similarities with piscidins, one with BMAP-27, and five were totally new. Their antibacterial activity was evaluated on eight bacteria including the aquatic pathogens Vibrio alginolyticus, Aeromonas salmonicida, or human pathogens such as Salmonella typhimurium, Listeria monocytogenes, or Staphylococcus aureus. Despite the prediction of an antimicrobial potential for eight of the peptides, only two-GR21 and KT19-inhibited more than one bacterial strain with minimal inhibitory concentrations below 25µM. Some sequences like VA20 and FK19 were hemolytic, while GR21 induced less than 10% of hemolysis on human blood cells at a concentration of 200µM. GR21 was the only peptide derived from a precursor with a signal peptide, suggesting a real role in cuttlefish immune defense.

Novel 3D Structure Based Model for Activity Prediction and Design of Antimicrobial Peptides

The emergence and worldwide spread of multi-drug resistant bacteria makes an urgent challenge for the development of novel antibacterial agents. A perspective weapon to fight against severe infections caused by drug-resistant microorganisms is antimicrobial peptides (AMPs). AMPs are a diverse class of naturally occurring molecules that are produced as a first line of defense by all multi-cellular organisms. Limited by the number of experimental determinate 3D structure, most of the prediction or classification methods of AMPs were based on 2D descriptors, including sequence, amino acid composition, peptide net charge, hydrophobicity, amphiphilic, etc. Due to the rapid development of structural simulation methods, predicted models of proteins (or peptides) have been successfully applied in structure based drug design, for example as targets of virtual ligand screening. Here, we establish the activity prediction model based on the predicted 3D structure of AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments.

Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions.

Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria). We show how datasets can be harvested to reveal features which inform new design concepts. We introduce new analysis and visualization tools: a graphical representation of the k-mer spectrum as a fundamental property encoded in antimicrobial peptide databases and a data-driven representation to illustrate membrane binding and permeation of helical peptides.

Joker: An algorithm to insert patterns into sequences for designing antimicrobial peptides

Innovative alternatives to control bacterial infections are need due to bacterial resistance rise. Antimicrobial peptides (AMPs) have been considered as the new generation of antimicrobial agents. Based on the fact that AMPs are sequence-dependent, a linguistic model for designing AMPs was previously developed, considering AMPs as a formal language with a grammar (patterns or motifs) and a vocabulary (amino acids). Albeit promising, that model has been poorly exploited mainly because thousands of sequences need to be generated, and the outcome has high similarity to already known AMPs. Here we present Joker, an innovative algorithm that improves the application of the linguistic model for rational design of antimicrobial peptides. We modelled the AMPs as a card game, where Joker combines the cards in the hand (patterns) with the cards in the table (sequence templates), generating a few variants. Our algorithm is capable of improving existing AMPs or even creating new AMPs from inactive peptides. A standalone version of Joker is available for download at <http://github.com/williamfp7/Joker> and requires a Linux 32-bit machine.

The Modification and Design of Antimicrobial Peptide.

The antimicrobial peptides (AMPs) are a group of unique naturally occurring anti-microbial compounds with around 50 amino acids. It represents promising therapeutic agents to the infectious disease without concerning about drug resistance. However, commercial development of these peptides for even the simplest application has been hindered by the limitations of sources, instability, toxicity and bioavailability. To improve the properties of the artificial synthesized AMPs, the modification and design are the hotspots of the AMPs research. In fact, more than half of the known AMPs are naturally modified. In this review, two types of modification strategies, biochemical modification and chemical modification were summarized. Although, the chemical modification is versatile and direct, the manufacturing cost is greatly increased compared to the antibiotics. With the recent progress of the protein modification enzyme, the biochemical modification of the antimicrobial peptide followed by heterologous expression has great application prospects.

In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics.

Effects of linker amino acids on the potency and selectivity of dimeric antimicrobial peptides

Dimerization is an effective strategy for designing antimicrobial peptides that combine the advantages of different native peptides. In this study, we explored the effects of different linker amino acids, including leucine, proline and aminocaproic acid, on the anticancer, antimicrobial and hemolytic activities of the heteromeric antimicrobial peptides AM-1, AM-2, and AM-3. Proline and aminocaproic acid are ideal linkers for increasing the potency and selectivity of heteromeric antimicrobial peptides. The results of MD simulations provided a rationalization for this observation. Both AM-2, which had a proline linker, and AM-3, which had an aminocaproic acid linker, adopted a compact conformation in water and a bent conformation in membranes. This change in the flexible structures of AM-2 and AM-3 could have resulted in decreased binding of these peptides to zwitterionic lipid bilayers and increased damage to mixed lipid bilayers containing acidic phospholipids. In short, these findings obtained via assessing the effects of linker amino acids will contribute to the design of ideal heteromeric antimicrobial peptides with high selectivity and potency.

Molecular design of antimicrobial peptides based on hemagglutinin fusion domain to combat antibiotic resistance in bacterial infection.

Antimicrobial peptides are derived from the viral fusion domain of influenza virus hemagglutinin based on rational analysis of the intermolecular interaction between peptides and bacterial outer membrane. It is revealed that the isolated viral fusion domain is a negatively charged peptide HAfp1-23 that cannot effectively interact with the anionic membrane. Conversion of the native HAfp1-23 to a positively charged peptide HAfp1-23 _KK by E11K/D19K mutation can promote the peptide-membrane interaction substantially; this confers to the peptide a moderate antibacterial potency against antibiotic-resistant bacterial strains. Cyclization of the linear peptide HAfp1-23 _KK results in a cyclic peptide cHAfp1-23 _KK, which can largely minimize entropy penalty upon the peptide-membrane binding by pre-stabilizing peptide hairpin configuration in solvent, where the linear peptide would incur in a considerable conformational change/folding from intrinsic disorder (in water) to the structured hairpin conformation (in lipid). As might be expected, the cyclization considerably improves peptide antibacterial activity with minimum inhibitory concentration of 67 and 34μg/mL against multidrug-resistant Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus, respectively.

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria

There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum β-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.

Design and NMR Structural Studies of New Antimicrobial Peptides with Higher Activity

The availability of antibiotics has allowed for the successful treatment of many bacterial infections as well as the ability to perform invasive medical procedures including surgery and chemotherapy. However, their wide use has led to pathogens' increased drug resistance and the need to find novel classes of antimicrobial peptides as alternatives to antibiotics. Lactophoricin (LPcin), a cationic amphipathic peptide consists of 23-mer peptide, corresponds to the carboxy terminal 113-135 region of component-3 of proteose-peptone and is a good candidate as a peptide antibiotic because it has an antibacterial activity but no hemolytic activity. Three different analogs of LPcin, LPcin-yk2 which has mutant amino acids, LPcin-yk1 and LPcin-yk3 that has shorter mutant amino acids are recently developed by using peptide engineering techniques in our laboratory and show better antibioitic activities than wild-type LPcin and no toxicity at all. Recently, we designed and selected more antimicrobial peptides LPcin-YK5, LPcub-YK8 and LPcin-YK11 based on LPcin-YK3. Designed all analogues were expressed and purified using several biophysical techniques and characterized using antimicrobial activity tests and various spectroscopic methods like MALDI-TOF MS and CD spectrometry, as well as 1D/2D solution NMR and solid-state NMR techniques in membrane environments. In here, we will present the optimizing processes with high-yield expression and purification of new LPcin analogs and solid- state NMR structural studies to figure out antibacterial killing mechanisms.