Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies

Computational Approaches for Antimicrobial Peptide Delivery

Peptides constitute alternative molecules for the treatment of infections caused by bacteria, viruses, fungi, and protozoa. However, their therapeutic effectiveness is often limited by enzymatic degradation, chemical and physical instability, and toxicity toward healthy human cells. To improve their pharmacokinetic (PK) and pharmacodynamic (PD) profiles, novel routes of administration are being explored. Among these, nanoparticles have shown promise as potential carriers for peptides, although the design of delivery vehicles remains a slow and painstaking process, heavily reliant on trial and error. Recently, computational approaches have been introduced to accelerate the development of effective drug delivery systems for peptides. Here we present an overview of some of these computational strategies and discuss their potential to optimize drug development and delivery.

Functional RNA mining using random high-throughput screening

Functional RNA participates in various life processes in cells. However, there is currently a lack of effective methods to screen for functional RNA. Here, we developed a technology named random high-throughput screening (rHTS). rHTS uses a random library of ∼250-nt synthesized RNA fragments, with high uniformity and abundance. These fragments are circularized into circular RNA by an auto-cyclizing ribozyme to improve their stability. Using rHTS, we successfully screened and identified three RNA fragments contributing significantly to the growth of Escherichia coli, one of which possesses coding potential. Moreover, we found that two noncoding RNAs (ncRNAs) effectively inhibited the growth of E. coli, in vivo rather than in vitro. Subsequently, we applied the rHTS to a coenzyme-dependent screening platform. In this context, two ncRNAs were identified that could effectively promote the conversion from NADPH to NADP+. Exogenous expression of these two ncRNAs was able to increase the conversion rate of glycerol dehydrogenase from glycerol to 1,3-dihydroxyacetone from 18.3% to 21.8% and 23.2%, respectively. These results suggest that rHTS is a powerful technology for functional RNA mining.

Cell-Free Systems: Ideal Platforms for Accelerating the Discovery and Production of Peptide-Based Antibiotics

Peptide-based antibiotics (PBAs), including antimicrobial peptides (AMPs) and their synthetic mimics, have received significant interest due to their diverse and unique bioactivities. The integration of high-throughput sequencing and bioinformatics tools has dramatically enhanced the discovery of enzymes, allowing researchers to identify specific genes and metabolic pathways responsible for producing novel PBAs more precisely. Cell-free systems (CFSs) that allow precise control over transcription and translation in vitro are being adapted, which accelerate the identification, characterization, selection, and production of novel PBAs. Furthermore, these platforms offer an ideal solution for overcoming the limitations of small-molecule antibiotics, which often lack efficacy against a broad spectrum of pathogens and contribute to the development of antibiotic resistance. In this review, we highlight recent examples of how CFSs streamline these processes while expanding our ability to access new antimicrobial agents that are effective against antibiotic-resistant infections.