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Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

The potential of antifungal peptide Sesquin as natural food preservative

Sesquin is a wide spectrum antimicrobial peptide displaying a remarkable activity on fungi. Contrarily to most antimicrobial peptides, it presents an overall negative charge. In the present study, we elucidate the molecular basis of its mode of action towards biomimetic membranes by NMR and MD experiments. While a specific recognition of phosphatidylethanolamine (PE) might explain its activity in a variety of different organisms (including bacteria), a further interaction with ergosterol accounts for its strong antifungal activity. NMR data reveal a charge gradient along its amide protons allowing the peptide to reach the membrane phosphate groups despite its negative charge. Subsequently, the peptide gets structured inside the bilayer, reducing its order. MD simulations predict that its activity is retained in conditions commonly used for food preservation: low temperatures, high pressure, or the presence of electric field pulses, making Sesquin a good candidate as food preservative.

Molecular dynamics simulations to study the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides

The hydrophobic amino acid biphenylalanine (B) plays a key role in the antibacterial activity of ultrashort peptides. In this study, the interactions of tetrapeptide BRBR-NH₂ (BRBR) and pentapeptide BRBRB-NH₂ (BRBRB) with dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) mixed model membrane were studied by molecular dynamics simulation to assess the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. At low peptide concentrations, both peptides presented amphiphilic conformations; residues B of the pentapeptide approached the membrane faster than those of the tetrapeptide and made more contacts with the membrane; BRBRB exhibited stronger membrane affinity than BRBR. However, due to the low peptide concentrations, the effects of these two peptides on the membrane were not significantly different. At high peptide concentrations, the strong affinity of BRBRB made it have more interaction with membrane than BRBR and most residues B of BRBRB inserted into the membrane; BRBRB was more prone to aggregation and caused the membrane more disordered and thinner than BRBR. Hydrophobic residues often act as anchors in the antibacterial activity of ultrashort antimicrobial peptides. Adding a hydrophobic residue B to the C-terminal of BRBR could improve the ability of the peptide to "grasp" the membrane. At high peptide concentrations, the addition of residue B might enhance the antibacterial activity of the peptide. Thus, our results will be helpful in designing efficient antibacterial drugs.

Bombyx mori Cecropin D could trigger cancer cell apoptosis by interacting with mitochondrial cardiolipin

Cecropin D is an antimicrobial peptide from Bombyx mori displaying anticancer and pro-apoptotic activities and, together with Cecropin XJ and Cecropin A, one of the very few peptides targeting esophageal cancer. Cecropin D displays poor similarity to other cecropins but a remarkable similarity in the structure and activity spectrum with Cecropin A and Cecropin XJ, offering the possibility to highlight key motifs at the base of the biological activity. In this work we show by NMR and MD simulations that Cecropin D is partially structured in solution and stabilizes its two-helix folding upon interaction with biomimetic membranes. Simulations show that Cecropin D strongly interacts with the surface of cancer cell biomimetic bilayers where it recognises the phosphatidylserine headgroup often exposed in the outer leaflet of cancerous cells by means of specific salt bridges. Cecropin D is also able to penetrate deeply in bilayers containing cardiolipin, a phospholipid found in mitochondria, causing significant destabilization in the lipid packing which might account for its pro-apoptotic activity. In bacterial membranes, phosphatidylglycerol and phosphatidylethanolamine act synergically by electrostatically attracting cecropin D and providing access to the membrane core, respectively.

The Influence of Short Motifs on the Anticancer Activity of HB43 Peptide

Despite the remarkable similarity in amino acid composition, many anticancer peptides (ACPs) display significant differences in terms of activity. This strongly suggests that particular relative dispositions of amino acids (motifs) play a role in the interaction with their biological target, which is often the cell membrane. To better verify this hypothesis, we intentionally modify HB43, an ACP active against a wide variety of cancers. Sequence alignment of related ACPs by ADAPTABLE web server highlighted the conserved motifs that could be at the origin of the activity. In this study, we show that changing the order of amino acids in such motifs results in a significant loss of activity against colon and breast cancer cell lines. On the contrary, amino acid substitution in key motifs may reinforce or weaken the activity, even when the alteration does not perturb the amphipathicity of the helix formed by HB43 on liposomes mimicking their surface. NMR and MD simulations with different membrane models (micelles, bicelles, and vesicles) indicate that the activity reflects the insertion capability in cancer-mimicking serine-exposing membranes, supported by the insertion of N-terminal phenylalanine in the FAK motif and the anchoring to the carboxylate of phosphatidylserine by means of arginine side chains.

Amyloidogenic Peptides: New Class of Antimicrobial Peptides with the Novel Mechanism of Activity

Antibiotic-resistant bacteria are recognized as one of the leading causes of death in the world. We proposed and successfully tested peptides with a new mechanism of antimicrobial action “protein silencing” based on directed co-aggregation. The amyloidogenic antimicrobial peptide (AAMP) interacts with the target protein of model or pathogenic bacteria and forms aggregates, thereby knocking out the protein from its working condition. In this review, we consider antimicrobial effects of the designed peptides on two model organisms, E. coli and T. thermophilus, and two pathogenic organisms, P. aeruginosa and S. aureus. We compare the amino acid composition of proteomes and especially S1 ribosomal proteins. Since this protein is inherent only in bacterial cells, it is a good target for studying the process of co-aggregation. This review presents a bioinformatics analysis of these proteins. We sum up all the peptides predicted as amyloidogenic by several programs and synthesized by us. For the four organisms we studied, we show how amyloidogenicity correlates with antibacterial properties. Let us especially dwell on peptides that have demonstrated themselves as AMPs for two pathogenic organisms that cause dangerous hospital infections, and in which the minimal inhibitory concentration (MIC) turned out to be comparable to the MIC of gentamicin sulfate. All this makes our study encouraging for the further development of AAMP. The hybrid peptides may thus provide a starting point for the antibacterial application of amyloidogenic peptides.

Recent advances in the design of antimicrobial peptide conjugates

Antimicrobial peptides (AMPs) are ubiquitous host defense peptides characterized by antibiotic activity and lower propensity for developing resistance compared to classic antibiotics. While several AMPs have shown activity against antibiotic-sensitive...

Fighting Antibiotic-Resistant Bacteria : Promising Strategies Orchestrated by Molecularly Imprinted Polymers

Infections caused by antibiotic-resistant bacteria are difficult and sometimes impossible to treat, making them one of the major public health problems of our time. We highlight how one unique material , molecularly imprinted polymers (MIPs), can orchestrate several strategies to fight this major societal issue. MIPs are tailor-made biomimetic supramolecular receptors that recognize and bind target molecules with a high affinity and selectivity, comparable to those of antibodies. While research on MIPs for combatting cancer has been constantly flourishing, comprehensive work on their involvement in combatting resistant superbugs has been rather scarce. This review aims at filling this gap. We will describe what are the causes of bacterial resistance and at which level MIPs can deploy their weapons. MIPs' targets can be biofilm constituents, quorum sensing messengers, bacterial surface proteins and antibiotic-deactivating enzymes, among others. We will conclude on the current challenges and future developments in this field.

Discovery, Optimization, and Clinical Application of Natural Antimicrobial Peptides

Antimicrobial peptides (AMPs) are widespread in multicellular organisms. These structurally diverse molecules are produced as the first line of defense against pathogens such as bacteria, viruses, fungi, and parasites. Also known as host defense peptides in higher eukaryotic organisms, AMPs display immunomodulatory and anticancer activities. During the last 30 years, technological advances have boosted the research on antimicrobial peptides, which have also attracted great interest as an alternative to tackling the antimicrobial resistance scenario mainly provoked by some bacterial and fungal pathogens. However, the introduction of natural AMPs in clinical trials faces challenges such as proteolytic digestion, short half-lives, and cytotoxicity upon systemic and oral application. Therefore, some strategies have been implemented to improve the properties of AMPs aiming to be used as effective therapeutic agents. In the present review, we summarize the discovery path of AMPs, focusing on preclinical development, recent advances in chemical optimization and peptide delivery systems, and their introduction into the market.

Antimicrobial Peptides: A Potent Alternative to Antibiotics

Antimicrobial peptides constitute one of the most promising alternatives to antibiotics since they could be used to treat bacterial infections, especially those caused by multidrug-resistant pathogens. Many antimicrobial peptides, with various activity spectra and mechanisms of actions, have been described. This review focuses on their use against ESKAPE bacteria, especially in biofilm treatments, their synergistic activity, and their application as prophylactic agents. Limitations and challenges restricting therapeutic applications are highlighted, and solutions for each challenge are evaluated to analyze whether antimicrobial peptides could replace antibiotics in the near future.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Cell-penetrating peptides in the intracellular delivery of viral nanoparticles

Cell-penetrating peptides (CPPs) are a promising tool for the intracellular delivery of cargo. Due to their ability to cross membranes while also cotransporting various cargoes, they offer great potential for biomedical applications. Several CPPs have been derived from viral proteins with natural roles in the viral replication cycle that require them to breach or fuse to cellular membranes. Additionally, the ability of viruses to cross membranes makes viruses and virus-based particles a convenient model for research on nanoparticle delivery and nanoparticle-mediated gene therapy. In this chapter, we aim to characterize CPPs derived from both structural and nonstructural viral proteins. Their function as enhancers of viral infection and transduction by viral nanoparticles as well as the main features of viral CPPs employed in intracellular cargo delivery are summarized to emphasize their potential use in nanomedicine.

Computational Methods and Tools in Antimicrobial Peptide Research

The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Antimicrobial Bombinin-like Peptide 3 Selectively Recognizes and Inserts into Bacterial Biomimetic Bilayers in Multiple Steps

Bombinins are a wide family of antimicrobial peptides from Xenopus skin. By sequence clustering, we highlighted at least three families named A, B, and H, which might exert antibacterial activity by different modes of action. In this work, we study bombinin-like peptide 3 (BLP-3) as a nonhemolytic representative of the quite unexplored class A due to its appealing activity toward WHO-priority-list bacteria such as Neisseria, Pseudomonas aeruginosa, and Staphylococcus aureus. A marked preference for cardiolipin and phosphatidylglycerol head groups, typically found in bacteria, is proven with biomimetic membranes studied by liquid and solid NMR and MD simulations. BLP-3 gets structured upon interaction and penetrates deeply into the bilayer in two steps involving a superficial insertion of key side chains and subsequent internalization. All along the pathway, a fundamental role is played by lysine residues in the conserved region 11-19, which act in synergy with other key residues.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

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

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

Molecular Basis of the Anticancer and Antibacterial Properties of CecropinXJ Peptide: An In Silico Study

Esophageal cancer is an aggressive lethal malignancy causing thousands of deaths every year. While current treatments have poor outcomes, cecropinXJ (CXJ) is one of the very few peptides with demonstrated in vivo activity. The great interest in CXJ stems from its low toxicity and additional activity against most ESKAPE bacteria and fungi. Here, we present the first study of its mechanism of action based on molecular dynamics (MD) simulations and sequence-property alignment. Although unstructured in solution, predictions highlight the presence of two helices separated by a flexible hinge containing P24 and stabilized by the interaction of W2 with target biomembranes: an amphipathic helix-I and a poorly structured helix-II. Both MD and sequence-property alignment point to the important role of helix I in both the activity and the interaction with biomembranes. MD reveals that CXJ interacts mainly with phosphatidylserine (PS) but also with phosphatidylethanolamine (PE) headgroups, both found in the outer leaflet of cancer cells, while salt bridges with phosphate moieties are prevalent in bacterial biomimetic membranes composed of PE, phosphatidylglycerol (PG) and cardiolipin (CL). The antibacterial activity of CXJ might also explain its interaction with mitochondria, whose phospholipid composition recalls that of bacteria and its capability to induce apoptosis in cancer cells.

Antimicrobial Peptide K11 Selectively Recognizes Bacterial Biomimetic Membranes and Acts by Twisting Their Bilayers

K11 is a synthetic peptide originating from the introduction of a lysine residue in position 11 within the sequence of a rationally designed antibacterial scaffold. Despite its remarkable antibacterial properties towards many ESKAPE bacteria and its optimal therapeutic index (320), a detailed description of its mechanism of action is missing. As most antimicrobial peptides act by destabilizing the membranes of the target organisms, we investigated the interaction of K11 with biomimetic membranes of various phospholipid compositions by liquid and solid-state NMR. Our data show that K11 can selectively destabilize bacterial biomimetic membranes and torque the surface of their bilayers. The same is observed for membranes containing other negatively charged phospholipids which might suggest additional biological activities. Molecular dynamic simulations reveal that K11 can penetrate the membrane in four steps: after binding to phosphate groups by means of the lysine residue at the N-terminus (anchoring), three couples of lysine residues act subsequently to exert a torque in the membrane (twisting) which allows the insertion of aromatic side chains at both termini (insertion) eventually leading to the flip of the amphipathic helix inside the bilayer core (helix flip and internalization).

Monitoring the Site-Specific Solid-State NMR Data in Oligopeptides

Reliable values of the solid-state NMR (SSNMR) parameters together with precise structural data specific for a given amino acid site in an oligopeptide are needed for the proper interpretation of measurements aiming at an understanding of oligopeptides' function. The periodic density functional theory (DFT)-based computations of geometries and SSNMR chemical shielding tensors (CSTs) of solids are shown to be accurate enough to support the SSNMR investigations of suitably chosen models of oriented samples of oligopeptides. This finding is based on a thorough comparison between the DFT and experimental data for a set of tripeptides with both ¹³C_α and ¹⁵N_amid CSTs available from the single-crystal SSNMR measurements and covering the three most common secondary structural elements of polypeptides. Thus, the ground is laid for a quantitative description of local spectral parameters of crystalline oligopeptides, as demonstrated for the backbone ¹⁵N_amid nuclei of samarosporin I, which is a pentadecapeptide (composed of five classical and ten nonproteinogenic amino acids) featuring a strong antimicrobial activity.