Industrial application of antimicrobial peptides based on their biological activity and structure-activity relationship

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Antibacterial efficacy and mechanism of Cyprinus carpio chemokine-derived L-10 against multidrug-resistant Escherichia coli infections

OBJECTIVES

Antimicrobial resistance has raised concerns regarding untreatable infections and poses a growing threat to public health. Rational design of new AMPs is an ideal solution to this threat.

METHODS

In this study, we designed, modified, and synthesised an excellent AMP, L-10, based on the original sequence of the Cyprinus carpio chemokine. All experimental data were presented as the mean ± standard deviation (SD), and the two-tailed unpaired T-test method was used to analyze all data.

RESULTS

L-10 exhibited excellent antibacterial activity with negligible toxicity and improved the efficacy of a broad class of antibiotics against MDR Gram-negative pathogens, including tetracycline, meropenem, levofloxacin, and rifampin. Mechanistic studies have suggested that L-10 targets the bacterial membrane components, LPS and PG, to disrupt bacterial membrane integrity, thereby exerting antibacterial effects and enhancing the efficacy of antibiotics. Moreover, in animal infection models, L-10 significantly increased the survival rate of infected animals and effectively reduced the tissue bacterial load and inflammatory factor levels. In addition to its direct antibacterial activity, L-10 dramatically reduced pulmonary pathological alterations in a mouse model of endotoxemia and suppressed LPS-induced proinflammatory cytokines in vitro and in vivo. Lastly, L-10 was successfully expressed in Pichia pastoris and maintained antimicrobial activity against MDR Gram-negative pathogens in vivo and in vitro.

CONCLUSION

Collectively, these results reveal the potential of L-10 as an ideal candidate against MDR bacterial infections and provide new insights into the design, development, and clinical application of AMPs.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.

Large scale peptide screening against main protease of SARS CoV-2

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Engineering Approaches for the Development of Antimicrobial Peptide-Based Antibiotics

Antimicrobial peptides (AMPs) have received increasing attention as potential alternatives for future antibiotics because of the rise of multidrug-resistant (MDR) bacteria. AMPs are small cationic peptides with broad-spectrum antibiotic activities and different action mechanisms to those of traditional antibiotics. Despite the desirable advantages of developing peptide-based antimicrobial agents, the clinical applications of AMPs are still limited because of their enzymatic degradation, toxicity, and selectivity. In this review, structural modifications, such as amino acid substitution, stapling, cyclization of peptides, and hybrid AMPs with conventional antibiotics or other peptides, will be presented. Additionally, nanodelivery systems using metals or lipids to deliver AMPs will be discussed based on the structural properties and action mechanisms of AMPs.