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Cathelicidin antimicrobial peptides are a diverse family of cationic amphipathic peptides with multiple activities. In humans, cathelicidin LL-37 is one of the main host defense peptides with a remarkable medical and biotechnological potential. Deregulation of LL-37 expression has been associated with inflammatory diseases. However the effects of point mutations driven by single nucleotide polymorphisms (SNPs) on LL-37 are unknown. Here we applied an array of computational tools to investigate the effects of such mutations on LL-37 structure and activity. Due to the fact that, on cathelicidins, the prodomain is more conserved than the mature peptide, the SNP effect predictions were biased and, overall, resulted in neutral effects; and due to the slight changes in physicochemical properties, the antimicrobial predictions indicated the maintenance of such activity. Nonetheless, R07P, R07W, R29Q, R29W mutations reduced the peptide net charge, which in turn could result in less active LL-37 variants. Molecular dynamics data indicated that R07Q and N30Y mutations altered the LL-37 structure, leading to potential deleterious effects. In addition, the helix dipole is altered in G03A, R07P, R07W and L31P mutations, which could also alter the antimicrobial activity. Our results indicated that despite the mutations did not alter the residues from LL-37 active core, they could influence the antimicrobial activity and consequently, could be involved in inflammatory diseases.

Novel antibiotics are urgently needed to combat multidrug-resistant pathogens. Venoms represent previously untapped sources of novel drugs. Here we repurposed mastoparan-L, the toxic active principle derived from the venom of the wasp Vespula lewisii, into synthetic antimicrobials. We engineered within its N terminus a motif conserved among natural peptides with potent immunomodulatory and antimicrobial activities. The resulting peptide, mast-MO, adopted an α-helical structure as determined by NMR, exhibited increased antibacterial properties comparable to standard-of-care antibiotics both in vitro and in vivo, and potentiated the activity of different classes of antibiotics. Mechanism-of-action studies revealed that mast-MO targets bacteria by rapidly permeabilizing their outer membrane. In animal models, the peptide displayed direct antimicrobial activity, led to enhanced ability to attract leukocytes to the infection site, and was able to control inflammation. Permutation studies depleted the remaining toxicity of mast-MO toward human cells, yielding derivatives with antiinfective activity in animals. We demonstrate a rational design strategy for repurposing venoms into promising antimicrobials.

Background Bacterial infections represent a major worldwide health problem the antimicrobial peptides (AMPs) have been considered as potential alternative agents for treating these infections. Here we demonstrated the antimicrobial activity of EcDBS1R6, a peptide derived from a signal peptide sequence of Escherichia coli that we previously turned into an AMP by making changes through the Joker algorithm. Methods Antimicrobial activity was measured by broth microdilution method. Membrane integrity was measured using fluorescent probes and through scanning electron microscopy imaging. A sliding window of truncated peptides was used to determine the EcDBS1R6 active core. Molecular dynamics in TFE/water environment was used to assess the EcDBS1R6 structure. Results Signal peptides are known to naturally interact with membranes; however, the modifications introduced by Joker transformed this peptide into a membrane-active agent capable of killing bacteria. The C-terminus was unable to fold into an α-helix whereas its fragments showed poor or no antimicrobial activity, suggesting that the EcDBS1R6 antibacterial core was located at the helical N-terminus, corresponding to the signal peptide portion of the parent peptide. Conclusion The strategy of transforming signal peptides into AMPs appears to be promising and could be used to produce novel antimicrobial agents. General significance The process of transforming an inactive signal peptide into an antimicrobial peptide could open a new venue for creating new AMPs derived from signal peptides.

BackgroundComputer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present “Sense the Moment”, a prediction system capable of discriminating AMPs and shuffled versions.MethodsThe system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales.ResultsThe model showed good accuracy (>80 %) and excellent sensitivity (>90 %) for AMP prediction, exceeding deep-learning-based methods.ConclusionOur results demonstrate the system’s applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives. General Significance: The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis.AvailabilityThe system was implemented as a web application, available at .

Computer-aided screening of antimicrobial peptides (AMPs) is a promising approach for discovering novel therapies against multidrug-resistant bacterial infections. Here, we functionally and structurally characterized an Escherichia coli-derived AMP (EcDBS1R5) previously designed through pattern identification [α-helical set (KK[ILV](3)[AILV])], followed by sequence optimization. EcDBS1R5 inhibited the growth of Gram-negative and Gram-positive, susceptible and resistant bacterial strains at low doses (2-32 μM), with no cytotoxicity observed against non-cancerous and cancerous cell lines in the concentration range analyzed (

The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations.

Bacterial infections represent a major worldwide health problem, with an special highlight on Gram-negative bacteria, which were assigned by the World Health Organization (WHO) as the most critical priority for development of novel antimicrobial compounds. Antimicrobial peptides (AMPs) have been considered as potential alternative agents for treating these infections. Here we demonstrated the broad-spectrum activity of EcDBS1R6, a peptide derived from a signal peptide sequence ofEscherichia colithat we previously turned into an AMP by making changes predicted through the Joker algorithm. Signal peptides are known to naturally interact with membranes; however, the modifications introduced by Joker made this peptide capable of killing bacteria. Membrane damage of the bacterial cells was observed by measuring membrane integrity using fluorescent probes and through scanning electron microscopy imaging. Structural analysis revealed that the C-terminus was unable to fold into an α-helix, indicating that the EcDBS1R6 antibacterial activity core was located at the N-terminus, corresponding to the signal peptide portion of the parent peptide. Therefore, the strategy of transforming signal peptides into AMPs seems to be promising and could be used for producing novel antimicrobial agents.

Diverse peptides have been evaluated for their activity against pathogenic microorganisms. Here, five mastoparan variants were designed based on mastoparan-L, among which two (R1 and R4) were selected for in-depth analysis. Mastoparan-L (parent/control), R1, and R4 inhibited susceptible/resistant bacteria at concentrations ranging from 2 to 32 μM, whereas only R1 and R4 eradicated Pseudomonas aeruginosa biofilms at 16 μM. Moreover, the toxic effects of mastoparan-L toward mammalian cells were drastically reduced in both variants. In skin infections, R1 at 64 μM was the most effective variant, reducing P. aeruginosa bacterial counts 1000 times on day 4 post-infection. Structurally, all of the peptides showed varying levels of helicity and structural stability in aqueous and membrane-like conditions, which may affect the different bioactivities observed here. By computationally modifying the physicochemical properties of R1 and R4, we reduced the cytotoxicity and optimized the therapeutic potential of these mastoparan-like peptides both in vitro and in vivo.

Bacterial biofilms and associated infections represent one of the biggest challenges in the clinic, and as an alternative to counter bacterial infections, antimicrobial peptides have attracted great attention in the past decade. Here, ten short cationic antimicrobial peptides were generated through a sliding-window strategy on the basis of the 19-amino acid residue peptide, derived from a Pyrobaculum aerophilum ribosomal protein. PaDBS1R6F10 exhibited anti-infective potential as it decreased the bacterial burden in murine Pseudomonas aeruginosa cutaneous infections by more than 1000-fold. Adverse cytotoxic and hemolytic effects were not detected against mammalian cells. The peptide demonstrated structural plasticity in terms of its secondary structure in the different environments tested. PaDBS1R6F10 represents a promising antimicrobial agent against bacteria infections, without harming human cells.

© 2018 Elsevier B.V. All rights reserved., Antimicrobial peptides (AMPs) are promising candidates for the development of future antibiotics. In an attempt to increase the efficacy of therapeutic AMPs, computer-based design methods appear as a reliable strategy. In this study, we evaluated the antimicrobial efficiency and mechanism of action of a novel designed AMP named PaDBS1R1, previously designed by means of the Joker algorithm, using a fragment of the ribosomal protein L39E from the archaeon Pyrobaculum aerophilum as a template. PaDBS1R1 displayed low micromolar broad-spectrum antimicrobial activity against Gram-negative (MIC of 1.5 μM) and Gram-positive (MIC of 3 μM) bacteria, including carbapenem-resistant Klebsiella pneumoniae (MIC of 6.25 μM) and methicillin-resistant Staphylococcus aureus (MIC of 12.5 μM), without cytotoxicity towards HEK-293 cells. In addition, membrane permeabilization and depolarization assays, combined with time-kill studies and FEG-SEM imaging, indicated a fast membrane permeation and further leakage of intracellular content. Biophysical studies with lipid vesicles show a preference of PaDBS1R1 for Gram-negative bacteria-like membranes. We investigated the three-dimensional structure of PaDBS1R1 by CD and NMR analyses. Our results suggest that PaDBS1R1 adopts an amphipathic α-helix upon interacting with hydrophobic environments, after an initial electrostatic interaction with negative charges, suggesting a membrane lytic effect. This study reveals that PaDBS1R1 has potential application in antibiotic therapy., The authors acknowledge Fundação para a Ciência e a Tecnologia – Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES, Portugal) for funding, including fellowship SFRH/BD/100517/2014 to M.R.F., Marie Sklodowska-Curie Research and Innovation Staff Exchange (MSCA-RISE, European Union) project INPACT (call H2020-MSCA-RISE-2014, grant agreement 644167), IMPC (Institut des Matériaux de Paris Centre, FR 2482) and the C'Nano projects of the Region Ile-de-France for SEM-FEG funding. Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq for financial support and researcher fellowship (Processes: 141327/2012-0; 407181/2013-0; 303181/20150), Coordenação de Aperfeiçoamento de Pessoal do Nível Superior – CAPES, for scholarship (Process: BEX 2758/15-6), and Fundação de Amparo à Pesquisa do Estado de Goiás – FAPEG (Process: 201710267000062). This work was also supported by Fundação de Amparo a Pesquisa do Mato Grosso do Sul (FUNDECT) and Fundação de Amapro a Pesquisa do Distrito Federal (FAPDF).

Infections caused by Gram-negative bacteria, Escherichia coli and Pseudomonas aeruginosa foremost among them, constitute a major worldwide health problem. Bioinformatics methodologies are being used to rationally design new antimicrobial peptides, a potential alternative for treating these infections. One of the algorithms used to develop antimicrobial peptides is the Joker, which was used to design the peptide PaDBS1R6. This study evaluates the antibacterial activities of PaDBS1R6 in vitro and in vivo, characterizes the peptide interaction to target membranes, and investigates the PaDBS1R6 structure in contact with mimetic vesicles. Moreover, we demonstrate that PaDBS1R6 exhibits selective antimicrobial activity against Gram-negative bacteria. In the presence of negatively charged and zwitterionic lipids the structural arrangement of PaDBS1R6 transits from random coil to α-helix, as characterized by circular dichroism. The tertiary structure of PaDBS1R6 was determined by NMR in zwitterionic dodecylphosphocholine (DPC) micelles. In conclusion, PaDBS1R6 is a candidate for the treatment of nosocomial infections caused by Gram-negative bacteria, as template for producing other antimicrobial agents.

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., Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.

Antimicrobial peptides (AMPs) are small molecules present in all living beings. Despite their huge sequence variability, AMPs present great structural conservation, mainly in cysteine-stabilized families. Moreover, in non-model plants, it is possible to detect cysteine-stabilized AMPs (cs-AMPs) with different sequences not covered by conventional searches. Here, we described a threading application for cs-AMP identification in the non-model arum lily (Zantedeschia aethiopica) plant, exploring the spathe transcriptome. By using the predicted proteins from the Z. aethiopica transcriptome as our primary source of sequences, we have filtered by using structural alignments of 12 putative cs-AMP sequences. The two unreported sequences were submitted to PCR validation, and ZaLTP7 gene was confirmed. By using the structure alignments, we classified ZaLTP7 as an LTP type 2-like. The successful threading application for cs-AMP identification is an important advance in transcriptomic and proteomic data mining. Besides, the same approach could be applied to the use of NGS public data to discover molecules to combat multidrug-resistant bacteria.

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 athttp://github.com/williamfp7/Jokerand requires a Linux 32-bit machine.

Pharmaceutical industries have renewed interest in screening multifunctional bioactive peptides as a marketable product in health care applications. In this context, several animal and plant peptides with potential bioactivity have been reported. Milk proteins and peptides have received much attention as a source of health-enhancing components to be incorporated into nutraceuticals and functional foods. By using this source, 24 peptides have been fractionated and purified from human milk using RP-HPLC. Multifunctional roles including antimicrobial, antioxidant and growth stimulating activity have been evaluated in all 24 fractions. Nevertheless, only four fractions show multiple combined activities among them. Using a proteomic approach, two of these four peptides have been identified as lactoferrin derived peptide and kappa casein short chain peptide. Lactoferrin derived peptide (f8) is arginine-rich and kappa casein derived (f12) peptide is proline-rich. Both peptides (f8 and f12) showed antimicrobial activities against both Gram-positive and Gram-negative bacteria. Fraction 8 (f8) exhibits growth stimulating activity in 3T3 cell line and f12 shows higher free radical scavenging activity in comparison to other fractions. Finally, both peptides were in silico evaluated and some insights into their mechanism of action were provided. Thus, results indicate that these identified peptides have multiple biological activities which are valuable for the quick development of the neonate and may be considered as potential biotechnological products for nutraceutical industry.

Glycine-rich proteins (GRPs) derived from plants compose a family of proteins and peptides that share a glycine repeat domain and they can perform diverse functions. Two structural conformations have been proposed for GRPs: glycine loops arranged as a Velcro and an anti-parallel β-sheet with several β-strands. The antimicrobial peptide Pg-AMP1 is the only plant GRP with antibacterial activity reported so far and its structure remains unclear. Recently, its recombinant expression was reported, where the recombinant peptide had an additional methionine residue at the N-terminal and a histidine tag at the C-terminal (His6-tag). These changes seem to change the peptide's activity, generating a broader spectrum of antibacterial activity. In this report, through ab initio molecular modelling and molecular dynamics, it was observed that both native and recombinant peptide structures were composed of an N-terminal α-helix and a dynamic loop that represents two-thirds of the protein. In contrast to previous reports, it was observed that there is a tendency to adopt a globular fold instead of an extended one, which could be in both, glycine loops or anti-parallel β-sheet conformation. The recombinant peptide showed a slightly higher solvated potential energy compared to the native form, which could be related to the His6-tag exposition. In fact, the His6-tag could be mainly responsible for the broader spectrum of activity, but it does not seem to cause great structural changes. However, novel studies are needed for a better characterization of its pharmacological properties so that in the future novel drugs may be produced based on this peptide.

Phytopathogens cause economic losses in agribusiness. Plant-derived compounds have been proposed to overcome this problem, including the antimicrobial peptides (AMPs). This paper reports the identification of Ps-AFP1, a novel AMP isolated from the Pisum sativum radicle. Ps-AFP1 was purified and evaluated against phytopathogenic fungi, showing clear effectiveness. In silico analyses were performed, suggesting an unusual fold and disulfide bond pattern. A novel fold and a novel AMP class were here proposed, the αβ-trumpet fold and αβ-trumpet peptides, respectively. The name αβ-trumpet was created due to the peptide's fold, which resembles the musical instrument. The Ps-AFP1 mechanism of action was also proposed. Microscopic analyses revealed that Ps-AFP1 could affect the fungus during the hyphal elongation from spore germination. Furthermore, confocal microscopy performed with Ps-AFP1 labeled with FITC shows that the peptide was localized at high concentration along the fungal cell surface. Due to low cellular disruption rates, it seems that the main target is the fungal cell wall. The binding thermogram and isothermal titration, molecular dynamics and docking analyses were also performed, showing that Ps-AFP1 could bind to chitin producing a stable complex. Data here reported provided novel structural–functional insights into the αβ-trumpet peptide fold.

Among the main classes of cysteine-stabilized antimicrobial peptides, the snakin/GASA family has not yet had any structural characterization. Through the combination of ab initio and comparative modeling with a disulfide bond predictor, the three-dimensional structure prediction of snakin-1 is reported here. The structure was composed of two long α-helices with a disulfide pattern of Cys(I)-Cys(IX), Cys(II)-Cys(VII), Cys(III)-Cys(IV), Cys(V)-Cys(XI), Cys(VI)-Cys(XII) and Cys(VIII)-Cys(X). The overall structure was maintained throughout molecular dynamics simulation. Snakin-1 showed a small degree of structural similarity with thionins and α-helical hairpins. This is the first report of snakin-1 structural characterization, shedding some light on the snakin/GASA family.

Data mining has been recognized by many researchers as a hot topic in different areas. In the post-genomic era, the growing number of sequences deposited in databases has been the reason why these databases have become a resource for novel biological information. In recent years, the identification of antimicrobial peptides (AMPs) in databases has gained attention. The identification of unannotated AMPs has shed some light on the distribution and evolution of AMPs and, in some cases, indicated suitable candidates for developing novel antimicrobial agents. The data mining process has been performed mainly by local alignments and/or regular expressions. Nevertheless, for the identification of distant homologous sequences, other techniques such as antimicrobial activity prediction and molecular modelling are required. In this context, this review addresses the tools and techniques, and also their limitations, for mining AMPs from databases. These methods could be helpful not only for the development of novel AMPs, but also for other kinds of proteins, at a higher level of structural genomics. Moreover, solving the problem of unannotated proteins could bring immeasurable benefits to society, especially in the case of AMPs, which could be helpful for developing novel antimicrobial agents and combating resistant bacteria.

International audience; The emergence of antibiotic-resistant clinical isolates and the decreased rate of development of new antibiotics are a constant threat to human health. In this context, the therapeutic value of mastoparan (MP), a toxin from wasp venom, has been extensively studied. However, since MP shows significant cytotoxic activities, further optimization is needed. Here we evaluated the antimicrobial and cytolytic activities of an MP analog created by Ala-substitution in positions 5 and 8, named [I-5, R-8] mastoparan ([I-5, R-8] MP). We found that [I-5, R-5] MP displayed a broad-spectrum antimicrobial activity against bacteria and fungi (MIC in the range 3-25 mu M), without being hemolytic or cytotoxic toward HEK-293 cells. In addition, [I-5, R-8] MP-amide was highly potent (MIC = 3 mu M) against antibiotic-resistant bacteria. The interaction with microbial membranes was investigated revealing that [I-5, R-8] MP is able to form an active amphipathic ct-helix conformation and to disturb membranes causing lysis and cell death. Based on our findings, we hypothesize that [I-5, R-8] MP follows a mechanism of action similar to that proposed for MP, where the pore-forming activity leads to cell death. Our results indicate that hydrophobic moment modified by amino acid substitution may enhance MP selectivity.

Lectins are proteins with ability to bind reversibly and non-enzymatically to a specific carbohydrate. They are involved in numerous biological processes and show enormous biotechnological potential. Among plant lectins, the hevein domain is extremely common, being observed in several kinds of lectins. Moreover, this domain is also observed in an important class of antimicrobial peptides named hevein-like peptides. Due to higher cysteine residues conservation, hevein-like peptides could be mined among the sequence databases. By using the pattern CX(4,5)CC[GS]X(2)GXCGX[GST]X(2,3)[FWY]C[GS]X[AGS] novel hevein-like peptide precursors were found from three different plants: Oryza sativa, Vitis vinifera and Selaginella moellendorffii. In addition, an hevein-like peptide precursor from the phytopathogenic fungus Phaeosphaeria nodorum was also identified. The molecular models indicate that they have the same scaffold as others, composed of an antiparallel β-sheet and short helices. Nonetheless, the fungal hevein-like peptide probably has a different disulfide bond pattern. Despite this difference, the complexes between peptide and N,N,N-triacetylglucosamine are stable, according to molecular dynamics simulations. This is the first report of an hevein-like peptide from an organism outside the plant kingdom. The exact role of an hevein-like peptide in the fungal biology must be clarified, while in plants they are clearly involved in plant defense. In summary, data here reported clear shows that an in silico strategy could lead to the identification of novel hevein-like peptides that could be used as biotechnological tools in the fields of health and agribusiness.

The antimicrobial peptides (AMPs) are evolutionarily ancient molecules that act as components of the innate immune system. Recently, it was demonstrated that a single AMP can perform various functions; this ability is known as “peptide promiscuity.” However, little is known about promiscuity in plant AMPs without disulfide bonds. This study was carried out to evaluate the promiscuity of Cn-AMP1: a promising disulfide-free plant peptide with reduced size and cationic and hydrophobic properties. Its activity against human pathogenic bacteria and fungal pathogens, as well as its in vitro immunostimulatory activity and effects on cancerous and healthy mammalian cell proliferation were studied here. Cn-AMP1 exerts antimicrobial effects against Gram-positive bacteria, Gram-negative bacteria, and fungi. Moreover, tumor cell viability activity in Caco-2 cells, as well as immunostimulatory activity by evaluating upregulated inflammatory-cytokine secretion by monocytes was also positively observed. Cn-AMP1 does not exhibit a well-defined conformation in aqueous solution and probably undergoes a 310-helix transition in hydrophobic environments. The experimental results support the promiscuous activity of Cn-AMP1, presenting a wide range of activities, including antibacterial, antifungal, and immunostimulatory activity. In the future, Cn-AMP1 should be used in the development of novel biopharmaceuticals, mainly due to its reduced size and broad spectrum of activity. © 2012 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 98: 322–331, 2012.

Plants produce a variety of proteins and peptides which are involved in their defense against pathogens. Serine protease inhibitors are a well-established class of inhibitors correlated with plant defense. Increased levels of protease inhibitors delay cell damage and expand the cell's life-span. Recently, the rapid emergence of antibiotic-resistant microbial pathogens has prompted immense interest in purifying novel antimicrobial proteins or peptides from plant sources. Usually, the purification of protease inhibitors is accomplished by salt-extraction, ultrafiltration and affinity chromatography. Here, we developed a novel approach based on iron oxide nanoparticles conjugated to dextran functionalized with trypsin beads that accelerate the quick screening and purification of antimicrobial peptides with serine protease inhibitor activity. The method described here also works for screening other inhibitors using particular protein kinases, and it is therefore a novel tool for use as the leading method in the development of novel antimicrobial agents with protease inhibitory activity. Finally, and no less important, molecular modelling and dynamics studies of a homologous inhibitor studied here with Escherichia coli trypsin and chymotrypsin are provided in order to shed some light on inhibitor–enzyme interactions.

In recent decades the activity of conventional antibiotics against pathogenic bacteria has decreased due to the development of resistance. This phenomenon has generated the socalled ‘superbugs’, which are multi-resistant bacteria. In this context, antimicrobial peptides (AMP) appear as an alternative to control them. AMPs have been found in several sources, including animals, plants and fungi, constituting the first line of host defence against pathogens. However, the use of AMPs as therapeutic agents has some limitations, such as stability, cytotoxicity and mainly their amino acid length, since amino acids are expensive building blocks. Despite these limitations they have compensatory properties, including secondary activities such as immunomodulation or antitumor ones. Several methods have been applied since the 1990s for rational AMPs design, in order to generate analogues with improved activity, looking to reduce limitations and increase advantages. Computer-aided identification and design of AMPs play a crucial role in this area. The discovery of AMP properties, through the first rational design studies, will allow the development of methods for prediction of AMPs, which in turn, should lead to identification prior to synthesis of novel analogues. Thus, this chapter will be dedicated to describing important techniques in prediction and rational design of AMPs and their applications for drug development.

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine.

The cysteine knot motifs are widely spread in several classes of peptides including those with antimicrobial functions. These motifs offer a major stability to the protein structure. Nevertheless, the antimicrobial activity is modulated by physicochemical properties. In this paper, we create a model of support vector machine to predict antimicrobial activity from sequences with similar motifs, based on physicochemical properties: net charge, ratio between hydrophobic and charged residues, average hydrophobicity and hydrophobic moment. The support vector machine model was trained with 146 antimicrobial peptides with six cysteines from the antimicrobial peptides database and an equal number of random sequences predicted as transmembrane proteins. The polynomial kernel shows the best accuracy (77.4%) on 10-fold cross validation. Testing in a blind dataset, we observe an accuracy of 83.02%. Through this model, proteins of varied size with a cysteine knot motif can be predicted with good reliability.

The search for novel antimicrobial peptides in free databases is a key element to design new antibiotics. Their amino acid physicochemical features impact into the antimicrobial peptides activities. The relationship between the amino acid physicochemical properties and the antimicrobial target might have a fuzzy behavior. This study proposes a sequence similarity and physicochemical search method followed by a fuzzy inference system to find the most appropriated antimicrobial peptides for each domain. The proposed system was tested with NCBI's NR protein data file and the obtained peptide sub dataset will be tested in vitro .

Antimicrobial peptides (AMPs) are compounds that act in a wide range of physiological defensive mechanisms developed to counteract bacteria, fungi, parasites and viruses. These molecules have become increasingly important as a consequence of remarkable microorganism resistance to common antibiotics. This report shows Escherichia coli expressing the recombinant antimicrobial peptide Pg-AMP1 previously isolated from Psidium guajava seeds. The deduced Pg-AMP1 open reading frame consists in a 168 bp long plus methionine also containing a His6 tag, encoding a predicted 62 amino acid residue peptide with related molecular mass calculated to be 6.98 kDa as a monomer and 13.96 kDa at the dimer form. The recombinant Pg-AMP1 peptide showed inhibitory activity against multiple Gram-negative (E. coli, Klebsiella pneumonia and Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus and Staphylococcus epidermides) bacteria. Moreover, theoretical structure analyses were performed in order to understand the functional differences between natural and recombinant Pg-AMP1 forms. Data here reported suggest that Pg-AMP1 is a promising peptide to be used as a biotechnological tool for control of human infectious diseases.