Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship

Translocation of Antimicrobial Peptides across Model Membranes: The Role of Peptide Chain Length

Cushioned lipid bilayers are structures consisting of a lipid bilayer supported on a solid substrate with an intervening layer of soft material. They offer possibilities for studying the behavior and interactions of biological membranes more accurately under physiological conditions. In this work, we continue our studies of cushion formation induced by histatin 5 (24Hst5), focusing on the effect of the length of the peptide chain. 24Hst5 is a short, positively charged, intrinsically disordered saliva peptide, and here, both a shorter (14Hst5) and a longer (48Hst5) peptide variant were evaluated. Experimental surface active techniques were combined with coarse-grained Monte Carlo simulations to obtain information about these peptides. Results show that at 10 mM NaCl, both the shorter and the longer peptide variants behave like 24Hst5 and a cushion below the bilayer is formed. At 150 mM NaCl, however, no interaction is observed for 24Hst5. On the contrary, a cushion is formed both in the case of 14Hst5 and 48Hst5, and in the latter, an additional thick, diffuse, and highly hydrated layer of peptide and lipid molecules is formed, on top of the bilayer. Similar trends were observed from the simulations, which allowed us to hypothesize that positively charged patches of the amino acids lysine and arginine in all three peptides are essential for them to interact with and translocate over the bilayer. We therefore hypothesize that electrostatic interactions are important for the interaction between the solid-supported lipid bilayers and the peptide depending on the linear charge density through the primary sequence and the positively charged patches in the sequence. The understanding of how, why, and when the cushion is formed opens up the possibility for this system to be used in the research and development of new drugs and pharmaceuticals.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Antimicrobial peptide DvAMP combats carbapenem-resistant Acinetobacter baumannii infection

Carbapenem-resistant Acinetobacter baumannii (CRAB), an important opportunistic pathogen, is a major cause of healthcare-associated infections. The polymyxins (colistin and polymyxin B) are the last line of defense in the treatment of CRAB infections, and there is an urgent need to develop novel alternative therapeutic strategies. In this study, we found that the antimicrobial peptide DvAMP exhibited satisfactory antibacterial and antibiofilm activity against CRAB. In addition, DvAMP showed tolerable stability in salt ions and serum and exhibited low toxicity in vivo. Investigation of the underlying mechanism demonstrated that DvAMP disrupts cell membrane structural integrity and specifically binds to exogenous lipopolysaccharides (LPS) and phospholipids (PG/CL), resulting in increased membrane permeability and dissipating proton motive force (PMF), further reducing intracellular ATP levels and inducing ROS accumulation, leading to bacterial death. Furthermore, DvAMP therapy efficiently improved survival rates and decreased the bacterial load in the lungs of mice in a mouse pneumonia model, showing that DvAMP administration reduced CRAB susceptibility to lung infection. These results indicate that the peptide DvAMP is a promising alternative therapeutic agent to combat CRAB infection.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

The antibacterial activity and mechanism of a novel peptide MR-22 against multidrug-resistant Escherichia coli

Introduction

Bacterial infections have become serious threats to human health, and the excessive use of antibiotics has led to the emergence of multidrug-resistant (MDR) bacteria. E. coli is a human bacterial pathogen, which can cause severe infectious. Antimicrobial peptides are considered the most promising alternative to traditional antibiotics.

Materials and methods

The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and hemolytic activity were determined by the microdilution method. The antimicrobial kinetics of MR-22 against E. coli were studied by growth curves and time-killing curves. The cytotoxicity of MR-22 was detected by the CCK-8 assay. The antimicrobial activity of MR-22 in salt, serum, heat and trypsin was determined by the microdilution method. The antimicrobial mechanism of MR-22 against drug-resistant E. coli was studied by Scanning Electron Microscope, laser confocal microscopy, and Flow Cytometry. The in vivo antibacterial activity of MR-22 was evaluated by the mice model of peritonitis.

Results and discussion

In this study, MR-22 is a new antimicrobial peptide with good activity that has demonstrated against MDR E. coli. The antimicrobial activity of MR-22 exhibited stability under conditions of high temperature, 10% FBS, and Ca2+. However, a decline of the activity was observed in the presence of Na+, serum, and trypsin. MR-22 had no significant cytotoxicity or hemolysis in vitro. SEM and fluorescent images revealed that MR-22 could disrupt the integrity of cell membrane. DCFH-DA indicated that MR-22 increased the content of reactive oxygen species, while it decreased the content of intracellular ATP. In mice model of peritonitis, MR-22 exhibited potent antibacterial activity in vivo. These results indicated that MR-22 is a potential drug candidate against drug-resistant E. coli.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.

Novel antimicrobial peptide DvAMP serves as a promising antifungal agent against Cryptococcus neoformans

Cryptococcus neoformans is an important opportunistic human fungal pathogen that causes cryptococcosis in immunocompromised patients. However, the number of drugs for the treatment of cryptococcosis is restricted, and the development of novel antifungal drugs and innovative strategies for the treatment of cryptococcosis is urgently needed. In this study, we validated that DvAMP is a novel antimicrobial peptide with antimicrobial activity and that it was obtained by pre-screening from the UniProt database of more than three million unknown functional sequences based on the quantitative structure-activity relationships (QSARs) protocol (http://www.chemoinfolab.com/antifungal). The peptide exhibited satisfactory biosafety and physicochemical properties, and relatively rapid fungicidal activity against C. neoformans. Meanwhile, DvAMP was able to inhibit the static biofilm of C. neoformans and cause a reduction in the thickness of the capsule. In addition, DvAMP exerts antifungal effects through membrane-mediated mechanisms (membrane permeability and depolarization) and mitochondrial dysfunction, involving a hybrid multi-hit mechanism. Furthermore, by using the C. neoformans-Galleria mellonella infection model, we demonstrated that DvAMP has significant therapeutic effects in vivo and that it significantly reduces the mortality and fungal burden of infected larvae. These results suggest that DvAMP may be a potential antifungal drug candidate for the treatment of cryptococcosis.

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

Specific Focus on Antifungal Peptides against Azole Resistant Aspergillus fumigatus: Current Status, Challenges, and Future Perspectives

The prevalence of fungal infections is increasing worldwide, especially that of aspergillosis, which previously only affected people with immunosuppression. Aspergillus fumigatus can cause allergic bronchopulmonary aspergillosis and endangers public health due to resistance to azole-type antimycotics such as fluconazole. Antifungal peptides are viable alternatives that combat infection by forming pores in membranes through electrostatic interactions with the phospholipids as well as cell death to peptides that inhibit protein synthesis and inhibit cell replication. Engineering antifungal peptides with nanotechnology can enhance the efficacy of these therapeutics at lower doses and reduce immune responses. This manuscript explains how antifungal peptides combat antifungal-resistant aspergillosis and also how rational peptide design with nanotechnology and artificial intelligence can engineer peptides to be a feasible antifungal alternative.

The Antimicrobial Peptide AMP-17 Derived from Musca domestica Inhibits Biofilm Formation and Eradicates Mature Biofilm in Candida albicans

The biofilm formation of C. albicans represents a major virulence factor during candidiasis. Biofilm-mediated drug resistance has necessitated the search for a new antifungal treatment strategy. In our previous study, a novel antimicrobial peptide named AMP-17 derived from Musca domestica was confirmed to have significant antifungal activity and suppress hyphal growth greatly in C. albicans. In the current work, we aimed to investigate the antibiofilm property of AMP-17 in C. albicans and explore the underlying mechanism. An antifungal susceptibility assay showed that AMP-17 exerted a strong inhibitory efficacy on both biofilm formation and preformed biofilms in C. albicans. Furthermore, AMP-17 was found to block the yeast-to-hypha transition and inhibit the adhesion of biofilm cells with a reduction in cellular surface hydrophobicity. A morphological analysis revealed that AMP-17 indeed suppressed typical biofilm formation and damaged the structures of the preformed biofilm. The RNA-seq showed that the MAPK pathway, biosynthesis of antibiotics, and essential components of the cell were mainly enriched in the biofilm-forming stage, while the citrate cycle (TCA cycle), phenylamine metabolism, and propanoate metabolism were enriched after the biofilm matured. Moreover, the co-expressed DEGs in the two pairwise comparisons highlighted the terms of transmembrane transporter activity, regulation of filamentation, and biofilm formation as important roles in the antibiofilm effect of AMP-17. Additionally, qRT-PCR confirmed that the level of the genes involved in cell adhesion, filamentous growth, MAPK, biofilm matrix, and cell dispersal was correspondingly altered after AMP-17 treatment. Overall, our findings reveal the underlying antibiofilm mechanisms of AMPs in C. albicans, providing an interesting perspective for the development of effective antifungal agents with antibiofilm efficacy in Candida spp.

Designed Multifunctional Peptides for Intracellular Targets

Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides.

New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning

Acute ischemic stroke (AIS) is a syndrome characterized by high morbidity, prevalence, mortality, recurrence and disability. The longer the delay before proper treatment of a stroke, the greater the likelihood of brain damage and disability. Computed tomography and nuclear magnetic resonance are the primary choices for fast diagnosis of AIS in the early stage, which can provide certain information about infarction location and degree, and even the vascular distribution of lesions responsible for strokes. However, this is quite difficult to achieve in small clinics or at-home diagnoses. Hematology tests could quickly obtain a large number of pathology-related indicators, and offer an effective method for rapid AIS diagnosis when combined with the machine learning technique. To explore a reliable, predictable method for early clinical etiologic diagnosis of AIS, a retrospective study was deployed on 456 AIS patients at the early stage and 28 reference subjects without the symptoms of AIS, by means of the selected significant traits amongst 64 clinical and blood traits in conjunction with powerful machine learning strategies. Five representative biomarkers were closely related to cardioembolic (CE), 22 to large artery atherosclerosis (LAA), and 15 to small vessel occlusion (SVO) strokes, respectively. With these biomarkers, different etiologic subtypes of stroke patients were determined with high accuracy of >0.73, sensitivity of >0.73, and specificity of >0.70, which was comparable to the accuracy obtained in the emergency department by clinical diagnosis. The proposed method may offer an alternative strategy for the etiologic diagnosis of AIS at the early stage when integrating significant blood traits into machine learning.

A rapid and safe strategy was proposed for clinical etiologic diagnosis of acute ischemic stroke at the early stage using clinical hematology traits and machine learning. Blood biomarkers were effectively identified.