Short linear cationic antimicrobial peptides: screening, optimizing, and prediction

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

Identification of Bacterial Membrane Selectivity of Romo1-Derived Antimicrobial Peptide AMPR-22 via Molecular Dynamics

The abuse or misuse of antibiotics has caused the emergence of extensively drug-resistant (XDR) bacteria, rendering most antibiotics ineffective and increasing the mortality rate of patients with bacteremia or sepsis. Antimicrobial peptides (AMPs) are proposed to overcome this problem; however, many AMPs have attenuated antimicrobial activities with hemolytic toxicity in blood. Recently, AMPR-11 and its optimized derivative, AMPR-22, were reported to be potential candidates for the treatment of sepsis with a broad spectrum of antimicrobial activity and low hemolytic toxicity. Here, we performed molecular dynamics (MD) simulations to clarify the mechanism of lower hemolytic toxicity and higher efficacy of AMPR-22 at an atomic level. We found four polar residues in AMPR-11 bound to a model mimicking the bacterial inner/outer membranes preferentially over eukaryotic plasma membrane. AMPR-22 whose polar residues were replaced by lysine showed a 2-fold enhanced binding affinity to the bacterial membrane by interacting with bacterial specific lipids (lipid A or cardiolipin) via hydrogen bonds. The MD simulations were confirmed experimentally in models that partially mimic bacteremia conditions in vitro and ex vivo. The present study demonstrates why AMPR-22 showed low hemolytic toxicity and this approach using an MD simulation would be helpful in the development of AMPs.

The multifaceted nature of antimicrobial peptides: current synthetic chemistry approaches and future directions

Bacterial infections caused by 'superbugs' are increasing globally, and conventional antibiotics are becoming less effective against these bacteria, such that we risk entering a post-antibiotic era. In recent years, antimicrobial peptides (AMPs) have gained significant attention for their clinical potential as a new class of antibiotics to combat antimicrobial resistance. In this review, we discuss several facets of AMPs including their diversity, physicochemical properties, mechanisms of action, and effects of environmental factors on these features. This review outlines various chemical synthetic strategies that have been applied to develop novel AMPs, including chemical modifications of existing peptides, semi-synthesis, and computer-aided design. We will also highlight novel AMP structures, including hybrids, antimicrobial dendrimers and polypeptides, peptidomimetics, and AMP-drug conjugates and consider recent developments in their chemical synthesis.

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance of AMP optimization. In this context, the computer-aided design of AMPs has put together crucial information on chemical parameters and bioactivities in AMP sequences, thus providing modes of prediction to evaluate the antibacterial potential of a candidate sequence before synthesis. Quantitative structure-activity relationship (QSAR) computational models, for instance, have greatly contributed to AMP sequence optimization aimed at improved biological activities. In addition to machine-learning methods, the de novo design, linguistic model, pattern insertion methods, and genetic algorithms, have shown the potential to boost the automated design of AMPs. However, how successful have these approaches been in generating effective antibacterial drug candidates? Bearing this in mind, this review will focus on the main computational strategies that have generated AMPs with promising activities against pathogenic bacteria, as well as anti-infective potential in different animal models, including sepsis and cutaneous infections. Moreover, we will point out recent studies on the computer-aided design of antibiofilm peptides. As expected from automated design strategies, diverse candidate sequences with different structural arrangements have been generated and deposited in databases. We will, therefore, also discuss the structural diversity that has been engendered.

Machine learning antimicrobial peptide sequences: Some surprising variations on the theme of amphiphilic assembly

Antimicrobial peptides (AMPs) collectively constitute a key component of the host innate immune system. They span a diverse space of sequences and can be α-helical, β-sheet, or unfolded in structure. Despite a wealth of knowledge about them from decades of experiments, it remains difficult to articulate general principles governing such peptides. How are they different from other molecules that are also cationic and amphiphilic? What other functions, in immunity and otherwise, are enabled by these simple sequences? In this short review, we present some recent work that engages these questions using methods not usually applied to AMP studies, such as machine learning. We find that not only do AMP-like sequences confer membrane remodeling activity to an unexpectedly broad range of protein classes, their cationic and amphiphilic signature also allows them to act as meta-antigens and self-assemble with immune ligands into nanocrystalline complexes for multivalent presentation to Toll-like receptors.

What Can Pleiotropic Proteins in Innate Immunity Teach Us about Bioconjugation and Molecular Design?

A common bioengineering strategy to add function to a given molecule is by conjugation of a new moiety onto that molecule. Adding multiple functions in this way becomes increasingly challenging and leads to composite molecules with larger molecular weights. In this review, we attempt to gain a new perspective by looking at this problem in reverse, by examining nature's strategies of multiplexing different functions into the same pleiotropic molecule using emerging analysis techniques such as machine learning. We concentrate on examples from the innate immune system, which employs a finite repertoire of molecules for a broad range of tasks. An improved understanding of how diverse functions are multiplexed into a single molecule can inspire new approaches for the deterministic design of multifunctional molecules.

What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.

Mapping membrane activity in undiscovered peptide sequence space using machine learning

There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance σ from the SVM hyperplane (thus maximize its "antimicrobialness") and its ⍺-helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric σ correlates not with a peptide's minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

QSAR studies may help to better understand structural requirements for peptide bioactivity and therefore to develop potent BAPs.

Predicting the Minimal Inhibitory Concentration for Antimicrobial Peptides with Rana-Box Domain

The global spreading of multidrug resistance has motivated the search for new antibiotic classes including different types of antimicrobial peptides (AMPs). Computational methods for predicting activity in terms of the minimal inhibitory concentration (MIC) of AMPs can facilitate "in silico" design and reduce the cost of synthesis and testing. We have used an original method for separating training and test data sets, both of which contain the sequences and measured MIC values of non-homologous anuran peptides having the Rana-box disulfide motif at their C-terminus. Using a more flexible profiling methodology (sideways asymmetry moment, SAM) than the standard hydrophobic moment, we have developed a two-descriptor model to predict the bacteriostatic activity of Rana-box peptides against Gram-negative bacteria--the first multilinear quantitative structure-activity relationship model capable of predicting MIC values for AMPs of widely different lengths and low identity using such a small number of descriptors. Maximal values for SAMs, as defined and calculated in our method, furthermore offer new structural insight into how different segments of a peptide contribute to its bacteriostatic activity, and this work lays the foundations for the design of active artificial AMPs with this type of disulfide bridge.

Rational design of artificial β-strand-forming antimicrobial peptides with biocompatible properties

Because the intensive use of antibiotics has led to a large variety of resistant bacterial strains, therapeutic measures have become increasingly challenging. In order to ensure reliable treatment of diseases, alternative antimicrobial agents need to be explored. In this context, antimicrobial peptides have been discussed as novel bioactive molecules, which, however, may be limited in their applicability due to their high manufacturing costs and poor pharmacokinetic properties. Consequently, the design of artificial antimicrobial peptides featuring two flanking cationic regions and a hydrophobic center is presented. These sequences led to distinct antimicrobial activity on the same order of magnitude as that of naturally occurring reference peptides but with less cytotoxic or cytostatic drawbacks. Furthermore, a deletion and substitution library revealed the minimal sequence requirements. By analysis of the computed 3D structures of these peptides, a single characteristic β-strand was identified. This structural motif was pivotal for antimicrobial activity. Consequently, an optimized peptide sequence with antimicrobial and biocompatible properties was derived, and its application was demonstrated in a mixed culture experiment. Thus, it was shown that the optimized artificial antimicrobial peptide is suitable as a therapeutic agent and may be used as template for the development of new antimicrobial peptides with unique secondary structures.

New milk protein-derived peptides with potential antimicrobial activity: an approach based on bioinformatic studies

New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs) from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM), random forest (RF), artificial neural networks (ANN) and discriminant analysis (DA) available in the Collection of Anti-Microbial Peptides (CAMP database). Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins.

New trends in peptide-based anti-biofilm strategies: a review of recent achievements and bioinformatic approaches

Antimicrobial peptides (AMPs) have a broad spectrum of activity and unspecific mechanisms of action. Therefore, they are seen as valid alternatives to overcome clinically relevant biofilms and reduce the chance of acquired resistance. This paper reviews AMPs and anti-biofilm AMP-based strategies and discusses ongoing and future work. Recent studies report successful AMP-based prophylactic and therapeutic strategies, several databases catalogue AMP information and analysis tools, and novel bioinformatics tools are supporting AMP discovery and design. However, most AMP studies are performed with planktonic cultures, and most studies on sessile cells test AMPs on growing rather than mature biofilms. Promising preliminary synergistic studies have to be consubstantiated and the study of functionalized coatings with AMPs must be further explored. Standardized operating protocols, to enforce the repeatability and reproducibility of AMP anti-biofilm tests, and automated means of screening and processing the ever-expanding literature are still missing.

Exploring the pharmacological potential of promiscuous host-defense peptides: from natural screenings to biotechnological applications

In the last few years, the number of bacteria with enhanced resistance to conventional antibiotics has dramatically increased. Most of such bacteria belong to regular microbial flora, becoming a real challenge, especially for immune-depressed patients. Since the treatment is sometimes extremely expensive, and in some circumstances completely inefficient for the most severe cases, researchers are still determined to discover novel compounds. Among them, host-defense peptides (HDPs) have been found as the first natural barrier against microorganisms in nearly all living groups. This molecular class has been gaining attention every day for multiple reasons. For decades, it was believed that these defense peptides had been involved only with the permeation of the lipid bilayer in pathogen membranes, their main target. Currently, it is known that these peptides can bind to numerous targets, as well as lipids including proteins and carbohydrates, from the surface to deep within the cell. Moreover, by using in vivo models, it was shown that HDPs could act both in pathogens and cognate hosts, improving immunological functions as well as acting through multiple pathways to control infections. This review focuses on structural and functional properties of HDP peptides and the additional strategies used to select them. Furthermore, strategies to avoid problems in large-scale manufacture by using molecular and biochemical techniques will also be explored. In summary, this review intends to construct a bridge between academic research and pharmaceutical industry, providing novel insights into the utilization of HDPs against resistant bacterial strains that cause infections in humans.

Antimicrobial peptides: key components of the innate immune system

Life-threatening infectious diseases are on their way to cause a worldwide crisis, as treating them effectively is becoming increasingly difficult due to the emergence of antibiotic resistant strains. Antimicrobial peptides (AMPs) form an ancient type of innate immunity found universally in all living organisms, providing a principal first-line of defense against the invading pathogens. The unique diverse function and architecture of AMPs has attracted considerable attention by scientists, both in terms of understanding the basic biology of the innate immune system, and as a tool in the design of molecular templates for new anti-infective drugs. AMPs are gene-encoded short (<100 amino acids), amphipathic molecules with hydrophobic and cationic amino acids arranged spatially, which exhibit broad spectrum antimicrobial activity. AMPs have been the subject of natural evolution, as have the microbes, for hundreds of millions of years. Despite this long history of co-evolution, AMPs have not lost their ability to kill or inhibit the microbes totally, nor have the microbes learnt to avoid the lethal punch of AMPs. AMPs therefore have potential to provide an important breakthrough and form the basis for a new class of antibiotics. In this review, we would like to give an overview of cationic antimicrobial peptides, origin, structure, functions, and mode of action of AMPs, which are highly expressed and found in humans, as well as a brief discussion about widely abundant, well characterized AMPs in mammals, in addition to pharmaceutical aspects and the additional functions of AMPs.

Insect natural products and processes: new treatments for human disease

In this overview, some of the more significant recent developments in bioengineering natural products from insects with use or potential use in modern medicine are described, as well as in utilisation of insects as models for studying essential mammalian processes such as immune responses to pathogens. To date, insects have been relatively neglected as sources of modern drugs although they have provided valuable natural products, including honey and silk, for at least 4-7000 years, and have featured in folklore medicine for thousands of years. Particular examples of Insect Folk Medicines will briefly be described which have subsequently led through the application of molecular and bioengineering techniques to the development of bioactive compounds with great potential as pharmaceuticals in modern medicine. Insect products reviewed have been derived from honey, venom, silk, cantharidin, whole insect extracts, maggots, and blood-sucking arthropods. Drug activities detected include powerful antimicrobials against antibiotic-resistant bacteria and HIV, as well as anti-cancer, anti-angiogenesis and anti-coagulant factors and wound healing agents. Finally, the many problems in developing these insect products as human therapeutic drugs are considered and the possible solutions emerging to these problems are described.

The sulfolobicin genes of Sulfolobus acidocaldarius encode novel antimicrobial proteins

Crenarchaea, such as Sulfolobus acidocaldarius and Sulfolobus tokodaii, produce antimicrobial proteins called sulfolobicins. These antimicrobial proteins inhibit the growth of closely related species. Here we report the identification of the sulfolobicin-encoding genes in S. acidocaldarius. The active sulfolobicin comprises two proteins that are equipped with a classical signal sequence. These proteins are secreted by the cells and found to be membrane vesicle associated. Gene inactivation studies demonstrate that both proteins are required for the bacteriostatic antimicrobial activity. Sulfolobicins constitute a novel class of antimicrobial proteins without detectable homology to any other protein.

An FPGA implementation to detect selective cationic antibacterial peptides

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.

Deciphering the mode of action of the synthetic antimicrobial peptide Bac8c

Bac8c (RIWVIWRR-NH(2)) is an 8-amino-acid peptide derived from Bac2A (RLARIVVIRVAR-NH(2)), a C3A/C11A variant of the naturally occurring bovine peptide, bactenecin (also known as bovine dodecapeptide), the smallest peptide with activity against a range of pathogenic Gram-positive and Gram-negative bacteria, as well as yeast. The effects of Bac8c on Escherichia coli were examined by studying its bacteriostatic and bactericidal properties, demonstrating its effects on proton motive force generation, and visually analyzing (via transmission electron microscopy) its effects on cells at different concentrations, in order to probe the complexities of the mechanism of action of Bac8c. Results were consistent with a two-stage model for the Bac8c mode of action. At sublethal concentrations (3 μg/ml), Bac8c addition resulted in transient membrane destabilization and metabolic imbalances, which appeared to be linked to inhibition of respiratory function. Although sublethal concentrations resulted in deleterious downstream events, such as methylglyoxal formation and free radical generation, native E. coli defense systems were sufficient for full recovery within 2 h. In contrast, at the minimal bactericidal concentration (6 μg/ml), Bac8c substantially but incompletely depolarized the cytoplasmic membrane within 5 min and disrupted electron transport, which in turn resulted in partial membrane permeabilization and cell death.