Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains

An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance.

Antibiotics-free compounds for managing carbapenem-resistant bacteria; a narrative review

Carbapenem-resistant (CR) Gram-negative bacteria have become a significant public health problem in the last decade. In recent years, the prevalence of CR bacteria has increased. The resistance to carbapenems could result from different mechanisms such as loss of porin, penicillin-binding protein alteration, carbapenemase, efflux pump, and biofilm community. Additionally, genetic variations like insertion, deletion, mutation, and post-transcriptional modification of corresponding coding genes could decrease the susceptibility of bacteria to carbapenems. In this regard, scientists are looking for new approaches to inhibit CR bacteria. Using bacteriophages, natural products, nanoparticles, disulfiram, N-acetylcysteine, and antimicrobial peptides showed promising inhibitory effects against CR bacteria. Additionally, the mentioned compounds could destroy the biofilm community of CR bacteria. Using them in combination with conventional antibiotics increases the efficacy of antibiotics, decreases their dosage and toxicity, and resensitizes CR bacteria to antibiotics. Therefore, in the present review article, we have discussed different aspects of non-antibiotic approaches for managing and inhibiting the CR bacteria and various methods and procedures used as an alternative for carbapenems against these bacteria.

Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

Antimicrobial Peptides towards Clinical Application-A Long History to Be Concluded

Antimicrobial peptides (AMPs) are molecules with an amphipathic structure that enables them to interact with bacterial membranes. This interaction can lead to membrane crossing and disruption with pore formation, culminating in cell death. They are produced naturally in various organisms, including humans, animals, plants and microorganisms. In higher animals, they are part of the innate immune system, where they counteract infection by bacteria, fungi, viruses and parasites. AMPs can also be designed de novo by bioinformatic approaches or selected from combinatorial libraries, and then produced by chemical or recombinant procedures. Since their discovery, AMPs have aroused interest as potential antibiotics, although few have reached the market due to stability limits or toxicity. Here, we describe the development phase and a number of clinical trials of antimicrobial peptides. We also provide an update on AMPs in the pharmaceutical industry and an overall view of their therapeutic market. Modifications to peptide structures to improve stability in vivo and bioavailability are also described.

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.

Antimicrobial peptides: An alternative to traditional antibiotics

As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.