Design Of Peptides Research Articles

Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides

Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifungal peptides. Here, we develop an alternative pipeline grounded in a diffusion model and molecular dynamics for the de novo design of AMPs. The peptides generated by our pipeline have lower similarity and identity than those of other reported methodologies. Among the 40 peptides synthesized for an experimental validation, 25 exhibit either antibacterial or antifungal activity. AMP-29 shows selective antifungal activity against Candida glabrata and in vivo antifungal efficacy in a murine skin infection model. AMP-24 exhibits potent in vitro activity against Gram-negative bacteria and in vivo efficacy against both skin and lung Acinetobacter baumannii infection models. The proposed approach offers a pipeline for designing diverse AMPs to counteract the threat of antibiotic resistance.

Detection and Treatment with Peptide Power: A New Weapon Against Bacterial Biofilms.

Bacterial biofilms, complex microbial communities encased in a protective extracellular matrix, pose a significant threat to public health due to their inherent antibiotic resistance. This review explores the potential of peptides, particularly antimicrobial peptides (AMPs), as innovative tools to combat biofilm-related infections. AMPs, characterized by their potent antimicrobial activity and tissue permeability, offer a promising approach to overcome the challenges posed by biofilms. By disrupting biofilm architecture, inhibiting bacterial growth, and enhancing biofilm detection through nuclear-based, fluorescence-based, and nanobased techniques, AMPs provide a multifaceted strategy. This review highlights recent advancements, approaches, and strategies in peptide research, examining their potential as both diagnostic and therapeutic agents. It also addresses key challenges and outlines future directions for optimizing peptide-based detection and therapies. By overcoming these challenges and refining peptide design, we can unlock the full potential of AMPs in combating bacterial biofilm infections, paving the way for the development of innovative solutions to tackle biofilm-related diseases and improve global health.

Gold Nanoparticles as a Platform for Delivery of Immunogenic Peptides to THP-1 Derived Macrophages: Insights into Nanotoxicity

Background: Peptide-based nanovaccines have emerged as a promising strategy for combating infectious diseases, as they overcome the low immunogenicity that is inherent to short epitope-containing synthetic peptides. Gold nanoparticles (AuNPs) present several advantages as peptide nanocarriers, but a deeper understanding of the design criteria is paramount to accelerate the development of peptide-AuNPs nanoconjugates (p-AuNPs). Methods: Herein, we synthesized and characterized p-AuNPs of 23 nm (p-Au23) and 68 nm (p-Au68) with varying levels of peptide surface coverage and different peptide designs, investigating their effect on the cell viability (cell death and mitochondrial activity), cellular uptake, and cathepsin B activity in THP-1 macrophages. Results: p-Au23 proved no negative effect in the cell viability and high levels of nanoconjugate uptake, but p-Au68 induced strong toxicity to the cell line. The peptide sequences were successfully designed with spacer regions and a cell-penetrating peptide (pTAT) that enhanced cellular uptake and cathepsin B activity for p-Au23, while pTAT induced severe effects in the THP-1 viability (~40–60% cell death). Conclusions: These findings provide valuable insight into the design criteria of AuNPs and immunogenic peptides, along with nanotoxicity effects associated with AuNP size and surface charge in human monocyte-derived macrophages.

Perturbation-Theory Machine Learning for Multi-Objective Antibacterial Discovery: Current Status and Future Perspectives

Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and/or multi-strain antibacterial activity.

Rational design of dual-agonist peptides targeting GLP-1 and NPY2 receptors for regulating glucose homeostasis and body weight with minimal nausea and emesis.

There is an urgent need for effective treatments targeting comorbidities of type 2 diabetes (T2DM) and obesity. Developing dual agonists of glucagon-like peptide 1 receptor (GLP-1R) and neuropeptide Y receptor type 2 (NPY2R) with combined PYY3-36 and GLP-1 bioactivity is promising. However, designing such dual agonists that effectively control glycemia and reduce weight while minimizing gastrointestinal side effects is challenging. In this study, we systematically evaluated the side effects induced by co-administering various GLP-1R agonists and PYY3-36 analogue. Our findings revealed that different GLP-1R agonist-PYY analogue combinations elicited gastrointestinal side effects of varying intensities. Among these, the co-administration of bullfrog GLP-1 analogue (bGLP-1) with PYY3-36 analogue resulted in lower gastrointestinal side effects. Thus, bGLP-1 was selected as the preferred candidate for designing dual GLP-1R/NPY2R agonists. Through stepwise structural design, optimization of linker arms, and durability enhancements, coupled with in vitro receptor screening, the novel peptide bGLP/PYY-19 emerged as the lead candidate. Notably, experimental results in mice and rats showed a significant reduction in emesis with bGLP/PYY-19 compared to semaglutide and bGLP-1 long-acting analogue (LAbGLP-1). Furthermore, bGLP/PYY-19 significantly outperformed semaglutide and LAbGLP-1 in reducing body weight in diet-induced obese (DIO) mice, without inducing nausea-associated behavior. These findings underscore the potential of dual-targeting single peptide conjugates as a promising strategy for developing glucoregulatory treatments that offer superior weight loss benefits and are better tolerated compared to treatments targeting GLP-1R alone.

Specific Rosetta-based protein-peptide prediction protocol allows the design of novel cholinesterase inhibitor peptides.

The search for novel cholinesterase inhibitors is essential for advancing treatments for neurodegenerative disorders such as Alzheimer's disease (AD). In this study, we employed the Rosetta pepspec module, originally developed for designing peptides targeting protein-protein interactions, to design de novo peptides targeting the peripheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of nine peptides were designed for human AChE (hAChE), T. californica AChE (TcAChE), and human BChE (hBChE). These peptides were synthesized using Fmoc-SPPS and tested in vitro using Ellman's reaction to evaluate their inhibitory potency. Peptide 11tA, designed for TcAChE, exhibited potent inhibition of hAChE (IC50=1.21±0.25µM) and demonstrated strong antioxidant activity against DPPH radicals and lipid peroxidation, making it a promising multitherapeutic candidate for AD. Peptide 11hB, designed for hBChE, showed the highest inhibitory activity against hBChE, with a Ki of 12.69±1.27µM, making it the most potent natural amino acid peptide reported against hBChE. The computational protocol effectively distinguished the specific characteristics of each enzyme target. Toxicity assessments, including hemolysis tests and A. salina lethality assays, revealed no toxic effects at low concentrations, further supporting the potential of these peptides for peptide-based drug development in AD. This study underscores the growing potential of peptides as alternatives to small-molecule drugs. It demonstrates that computational protocols for protein-protein interactions can be successfully adapted to design high-affinity peptide inhibitors.

Tying the Knot: In Silico Design of Foldable Lasso Peptides.

Lasso peptides are a unique class of ribosomally synthesized and post-translationally modified natural products with diverse biological activities and potential for therapeutic applications. Although direct synthesis would facilitate therapeutic design, it has not yet been possible to fold these short sequences to their threaded architecture without the help of biosynthetic enzyme stabilization. Our work explores strategies to enhance the stability of the pre-lasso structure, the essential precursor to de novo lasso peptide formation. We find that sequence design, incorporating non-canonical amino acid residues, and design-guided cross-linking can augment stability to increase the likelihood of lasso motif accessibility. This work presents several strategies for the continued design of foldable lasso peptides.

Artificial intelligence in peptide-based drug design.

Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promising modulators of PPIs, because they can bind to protein surfaces with high affinity and specificity. Nonetheless, computational peptide design remains difficult, hindered by the intrinsic flexibility of peptides and the substantial computational resources required. Recent advances in artificial intelligence (AI) are paving new paths for peptide-based drug design. In this review, we explore the advanced deep generative models for designing target-specific peptide binders, highlight key challenges, and offer insights into the future direction of this rapidly evolving field.

Design of Fluorinated Peptides as Biotransformed Urinalysis Biomarkers for Non-Invasive Diagnosis and Treatment of Liver Injury through Enzyme Directed Kinetics.

Urinalysis, as a non-invasive and efficient diagnostic method, is very important but faces great challenges due to the complex compositions of urine and limited naturally occurring biomarkers for diseases. Herein, by leveraging the intrinsic absence of endogenous fluorinated interference, a strategy with the enzymatically activated assembly of synthetic fluorinated peptide for cholestatic liver injury (CLI) diagnosis and treatment through 19F nuclear magnetic resonance (NMR) urinalysis and efficient drug retention is developed. Specifically, alkaline phosphatase (ALP), overexpressed in the liver of CLI mice, triggers the assembly of fluorinated peptide, thus, directingthe traffic and dynamic distribution of the synthetic biomarkers after administration, whereas CLI mice display much slower clearance of peptides through urine as compared with healthy counterparts. As such, it enables to transform pathophysiological information into exogenous signals via noninvasive urinary monitoring. Moreover, as a proof-of-concept, by grafting different functional groups to peptides, the theranostic platforms can be established to providea new paradigm for the design of multifunctional peptides.

CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and cis-peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temperature molecular dynamics (high-T MD) simulations on 250 CPs with random sequences and applied the point-adaptive k-nearest neighbors (PAk) method to estimate the free energies of millions of sampled conformations. Using this data set, we trained a SchNet-based deep learning model, termed CPconf_score, to predict the conformational free energies of CPs. We tested CPconf_score to identify near-native conformations from MD-sampled conformations of 50 CPs from the Cambridge Structural Database. Our method achieved accurate predictions for 41 out of 50 CPs with a backbone RMSD of less than 1.0 Å compared to crystal structures. In comparison, other advanced CP structure prediction tools, such as HighFold and Rosetta, successfully predicted 12 and 19 CPs, respectively.

Self-Assembled Peptide Hydrogels PPI45 and PPI47: Novel Drug Candidates for Staphylococcus aureus Infection Treatment.

Staphylococcus aureus, a prevalent zoonotic pathogen, poses a significant threat to skin wound infections. This study evaluates the bactericidal efficacy of self-assembled peptide hydrogels, PPI45 and PPI47, derived from the defensin-derived peptide PPI42, against S. aureus ATCC43300. The high-level preparation of PPI45 and PPI47 was achieved with yields of 1.82 g/L and 2.13 g/L, which are 2.19 and 2.60 times the yield of PPI42. Additionally, the critical micelle concentrations (CMCs) of the peptides at pH 7.4 for PPI42, PPI45, and PPI47 were determined to be 245 µg/mL, 973 µg/mL, and 1016 µg/mL, respectively. At a concentration of 3 mg/mL, the viscosities of the gels were 52,500 mPa·s, 33,700 mPa·s, and 3480 mPa·s for PPI42, PPI45, and PPI47. Transmission electron microscopy (TEM) revealed that all peptides exhibited long, pearl necklace-like protofibrils. These peptides demonstrated potent bactericidal activity, with a minimal inhibitory concentration (MIC) of 4-16 µg/mL against S. aureus, and a sustained effect post-drug clearance. Flow cytometry analysis after 2×MIC peptides treatment for 2 h revealed a 20-38% membrane disruption rate in bacteria, corroborated by scanning electron microscopy (SEM) observations of membrane damage and bacterial collapse. The peptide treatment also led to reduced hyperpolarized membrane potential. In vitro safety assessments indicated minimal hemolytic activity on murine red blood cells and low cytotoxicity on human immortalized epidermal cells (HaCaT). In summary, this work lays a valuable cornerstone for the future design and characterization of self-assembling antimicrobial peptides hydrogels to combat S. aureus infection.

Comparative Structural and Biophysical Investigation of Lycosa erythrognatha Toxin I (LyeTx I) and Its Analog LyeTx I-b.

Background/Objectives: This study investigates the structural and biophysical properties of the wild-type antimicrobial peptide LyeTx I, isolated from the venom of the spider Lycosa erythrognatha, and its analog LyeTx I-b, designed to enhance antibacterial activity, selectivity, and membrane interactions by the acetylation and increased amphipathicty. Methods: To understand the mechanisms behind these enhanced properties, comparative analyses of the structural, topological, biophysical, and thermodynamic aspects of the interactions between each peptide and phospholipid bilayers were evaluated. Both peptides were isotopically labeled with 2H3-Ala and 15N-Leu to facilitate structural studies via NMR spectroscopy. Results: Circular dichroism and solid-state NMR analyses revealed that, while both peptides adopt α-helical conformations in membrane mimetic environments, LyeTx I-b exhibits a more amphipathic and extended helical structure, which correlates with its enhanced membrane interaction. The thermodynamic properties of the peptide-membrane interactions were quantitatively evaluated in the presence of phospholipid bilayers using ITC and DSC, highlighting a greater propensity of LyeTx I-b to disrupt lipid vesicles. Calcein release studies reveal that both peptides cause vesicle disruption, although DLS measurements and TEM imaging indicate distinct effects on phospholipid vesicle organization. While LyeTx I-b permeabilizes anionic membrane retaining the vesicle integrity, LyeTx I promotes significant vesicle agglutination. Furthermore, DSC and calcein release assays indicate that LyeTx I-b exhibits significantly lower cytotoxicity toward eukaryotic membranes compared to LyeTx I, suggesting greater selectivity for bacterial membranes. Conclusions: Our findings provide insights into the structural and functional modifications that enhance the antimicrobial and therapeutic potential of LyeTx I-b, offering valuable guidance for the design of novel peptides targeting resistant bacterial infections and cancer.

Hybridization Design and High-Throughput Screening of Peptides with Immunomodulatory and Antioxidant Activities.

With the increasing recognition of the role of immunomodulation and oxidative stress in various diseases, designing peptides with both immunomodulatory and antioxidant activities has emerged as a promising therapeutic strategy. In this study, a hybridization design was applied as a powerful method to obtain multifunctional peptides. A total of 40 peptides with potential immunomodulatory and antioxidant activities were designed and screened. First, molecular docking was employed to screen peptides with a high binding affinity to MD2, a key receptor protein in the NFκB immune pathway. For the in vitro high-throughput screening, we constructed a reporter gene-based stable cell line, IPEC-J2-Lucia ARE cells, which was subsequently used to screen peptides with antioxidant activity. Furthermore, the biocompatibility, immunomodulatory, and antioxidant activities of these peptides were assessed. Among the candidates, the hybrid peptide VA exhibited the strongest immune-enhancing activity through the activation of the NF-κB pathway and significant antioxidant activity via the Nrf2-ARE pathway. Additionally, VA demonstrated protective effects against H2O2-induced oxidative damage in HepG2 cells. This study not only demonstrates the potential of peptide hybridization, but also develops a screening platform for multifunctional peptides. It provides a new tool for the treatment of autoimmune diseases and oxidative stress-related diseases.

Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease.

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide-receptor complexes, along with Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin's interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides.

De novo design of ribosomally synthesized and post-translationally modified peptides.

In nature, peptides are enzymatically modified to constrain their structure and introduce functional moieties. De novo peptide structures could be built by combining enzymes from different pathways, but determining the rules of their use is difficult. We present a biophysical model to combine enzymes sourced from bacterial ribosomally synthesized and post-translationally modified peptide (RiPP) gene clusters. Using a pipeline to evaluate more than 1,000 peptides, the model was parameterized under uniform conditions in Escherichia coli for enzymes from different classes (graspetide, spliceotide, pantocin, cyanobactin, glycocin, lasso peptide and lanthipeptide). Synthetic leader peptides with recognition sequences for up to three enzymes were designed to modify core sequences sharing no identity to natural RiPPs. Empirically, RiPPs with the desired modifications constituted 7-67% of the total peptides produced, and 6 of our 8 peptide designs were successfully modified. This work is an example of the design of enzyme-modified peptides and libraries, using a framework that can be expanded to include new enzymes and chemical moieties.

Peptide design to control protein-protein interactions.

Targeting of protein-protein interactions has become of huge interest in every aspect of medicinal and biological sciences. The control of protein interactions selectively offers the opportunity to control biological processes while limiting off target effects. This interest has massively increased with the development of cryo-EM and protein structure prediction with tools such as RosettaFold and AlphaFold. When designing molecules to control protein interactions, either inhibition or stabilisation, a starting point is commonly peptide design. This tutorial review describes that process, highlighting the selection of an initial sequence with and without structural information. Subsequently, methods for how the sequence can be analysed for key residues and how this information can be used to optimise the ligand efficiency are highlighted. Finally a discussion on how peptides can be further modified to increase their affinity and cell permeability, improving their drug-like properties, is presented.

Biophysics-guided uncertainty-aware deep learning uncovers high-affinity plastic-binding peptides.

Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a potential solution through targeted adsorption and subsequent MP detection or removal from the environment. A challenge in discovering plastic-binding peptides is the vast combinatorial space of possible peptides (i.e., over 1015 for 12-mer peptides), which far exceeds the sample sizes typically reachable by experiments or biophysics-based computational methods. One step towards addressing this issue is to train deep learning models on experimental or biophysical datasets, permitting faster and cheaper evaluations of peptides. However, deep learning predictions are not always accurate, which could waste time and money due to synthesizing and evaluating false positives. Here, we resolve this issue by combining biophysical modeling data from Peptide Binder Design (PepBD) algorithm, the predictive power and uncertainty quantification of evidential deep learning, and metaheuristic search methods to identify high-affinity PBPs for several common plastics. Molecular dynamics simulations show that the discovered PBPs have greater median adsorption free energies for polyethylene (5%), polypropylene (18%), and polystyrene (34%) relative to PBPs previously designed by PepBD. The impact of including uncertainty quantification in peptide design is demonstrated by the increasing improvement in the median adsorption free energy with decreasing uncertainty. This robust framework accelerates peptide discovery, paving the way for effective, bio-inspired solutions to MP remediation.

Peptide Design for Enhanced Anti-Melanogenesis: Optimizing Molecular Weight, Polarity, and Cyclization.

Melanogenesis is a biochemical process that regulates skin pigmentation, which is crucial role in protecting against ultraviolet radiation. It is also associated with hyperpigmentation conditions such as melasma and age spots, which negatively impact aesthetics and self-confidence. Tyrosinase (TYR), a key enzyme in the melanogenesis pathway, catalyzes the biosynthesis of melanin in the skin. Inhibition of tyrosinase particularly by blocking its active site and preventing the binding of natural substrates such as tyrosine, can reduce melanin production, making it a promising therapeutic target for treating hyperpigmentation. Peptides have emerged as promising therapeutics to regulate melanogenesis by minimizing the side effects associated with conventional skin whitening therapeutics. This review is designed to offer a comprehensive analysis of current strategies in peptide design aimed at optimizing anti-melanogenic activity, by focusing on the role of molecular weight, polarity, and cyclization strategies in enhancing peptide efficacy and stability. It was found that optimal peptide size was within the range of 400-600 Da. The balance between hydrophilic and hydrophobic properties in peptides is crucial for effective TYR inhibition, as higher hydrophilicity enhances affinity for the TYR active site and stronger catalytic inhibition, while hydrophobicity can contribute through alternative mechanisms. Cyclization of peptides enhances their structural stability, serum resistance, and binding affinity while reducing toxicity. This process increases resistance to enzymatic degradation and improves target specificity by limiting conformational flexibility. Additionally, the rigidity and internal hydrogen bonding of cyclic peptides can aid in membrane permeability, making them more effective for therapeutic use. Peptide optimizations through size modification, polarity change, and cyclization strategies have been shown to be promising as reliable and safe agents for melanin inhibition. Future studies exploring specific amino acid in peptide chains are required to improve efficacy and potential clinical applications of these anti-melanogenic peptides as a hyperpigmentation treatment.

Programmable short peptides for modulating stem cell fate in tissue engineering and regenerative medicine.

Recent advancements in tissue engineering and regenerative medicine have introduced promising strategies to address tissue and organ deficiencies. This review highlights the critical role of short peptides, particularly their ability to self-assemble into matrices that mimic the extracellular matrix (ECM). These low molecular weight peptides exhibit target-specific activities, modulate gene expression, and influence cell differentiation pathways. They are stable, programmable, non-cytotoxic, biocompatible, biodegradable, capable of crossing the cell membrane and easy to synthesize. This review underscores the importance of peptide structure and concentration in directing stem cell differentiation and explores their diverse biomedical applications. Peptides such as Aβ1-40, Aβ1-42, RADA16, A13 and KEDW are discussed for their roles in modulating stem cell differentiation into neuronal, glial, myocardial, osteogenic, hepatocyte and pancreatic lineages. Furthermore, this review delves into the underlying signaling mechanisms, the chemistry and design of short peptides and their potential for engineering biocompatible materials that mimic stem cell microenvironments. Short peptide-based biomaterials and scaffolds represent a promising avenue in stem cell therapy, tissue engineering, and regenerative medicine.

How Useful are Antimicrobial Peptide Properties for Predicting Activity, Selectivity, and Potency?

Antimicrobial peptides (AMPs) are recognized for their potential application as new generation antibiotics, however, up to date, they have not been widely commercialized as expected. Although current bioinformatic tools can predict antimicrobial activity based on only amino acid sequences with astounding accuracy, peptide selectivity and potency are not foreseeable. This, in turn, creates a bottleneck not only in the discovery and isolation of promising candidates but, most importantly, in the design and development of novel synthetic peptides. In this paper, we discuss the challenges faced when trying to predict peptide selectivity and potency, based on peptide sequence, structure and relevant biophysical properties such as length, net charge and hydrophobicity. Here, pore-forming alpha-helical antimicrobial peptides family isolated from anurans was used as the case study. Our findings revealed no congruent relationship between the predicted peptide properties and reported microbial assay data, such as minimum inhibitory concentrations against microorganisms and hemolysis. In many instances, the peptides with the best physicochemical properties performed poorly against microbial strains. In some cases, the predicted properties were so similar that differences in activity amongst peptides of the same family could not be projected. Our general conclusion is that antimicrobial peptides of interest must be carefully examined since there is no universal strategy for accurately predicting their behavior.