Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Harnessing advanced computational approaches to design novel antimicrobial peptides against intracellular bacterial infections

Intracellular bacterial infections pose a significant challenge to current therapeutic strategies due to the limited penetration of antibiotics through host cell membranes. This study presents a novel computational framework for efficiently screening candidate peptides against these infections. The proposed strategy comprehensively evaluates the essential properties for the clinical application of candidate peptides, including antimicrobial activity, permeation efficiency, and biocompatibility, while also taking into account the speed and reliability of the screening process. A combination of multiple AI-based activity prediction models allows for a thorough assessment of sequences in the cell-penetrating peptides (CPPs) database and quickly identifies candidate peptides with target properties. On this basis, the CPP microscopic dynamics research system was constructed. Exploration of the mechanism of action at the atomic level provides strong support for the discovery of promising candidate peptides. Promising candidates are subsequently validated through in vitro and in vivo experiments. Finally, Crot-1 was rapidly identified from the CPPsite 2.0 database. Crot-1 effectively eradicated intracellular MRSA, demonstrating significantly greater efficacy than vancomycin. Moreover, it exhibited no apparent cytotoxicity to host cells, highlighting its potential for clinical application. This work offers a promising new avenue for developing novel antimicrobial materials to combat intracellular bacterial infections.

Advances in antimicrobial peptides: From mechanistic insights to chemical modifications

This review provides a comprehensive analysis of antimicrobial peptides (AMPs), exploring their diverse sources, secondary structures, and unique characteristics. The review explores into the mechanisms underlying the antibacterial, immunomodulatory effects, antiviral, antiparasitic and antitumour of AMPs. Furthermore, it discusses the three principal synthesis pathways for AMPs and assesses their current clinical applications and preclinical research status. The paper also addresses the limitations of AMPs, including issues related to stability, resistance, and toxicity, while offering insights into strategies for their enhancement. Recent advancements in AMP research, such as chemical modifications (including amino acid sequence optimisation, terminal and side-chain modifications, PEGylation, conjugation with small molecules, conjugation with photosensitisers, metal ligands, polymerisation, cyclisation and specifically targeted antimicrobial peptides) are highlighted. The goal is to provide a foundation for the future design and optimisation of AMPs.

Unraveling the potential of bioengineered microbiome-based strategies to enhance cancer immunotherapy

The human microbiome plays a pivotal role in the field of cancer immunotherapy. The microbial communities that inhabit the gastrointestinal tract, as well as the bacterial populations within tumors, have been identified as key modulators of therapeutic outcomes, affecting immune responses and reprogramming the tumor microenvironment. Advances in synthetic biology have made it possible to reprogram and engineer these microorganisms to improve antitumor activity, enhance T-cell function, and enable targeted delivery of therapies to neoplasms. This review discusses the role of the microbiome in modulating both innate and adaptive immune mechanisms-ranging from the initiation of cytokine production and antigen presentation to the regulation of immune checkpoints-and discusses how these mechanisms improve the efficacy of immune checkpoint inhibitors. We highlight significant advances with bioengineered strains like Escherichia coli Nissle 1917, Lactococcus lactis, Bifidobacterium, and Bacteroides, which have shown promising antitumor efficacy in preclinical models. These engineered microorganisms not only efficiently colonize tumor tissues but also help overcome resistance to standard therapies by reprogramming the local immune environment. Nevertheless, several challenges remain, such as the requirement for genetic stability, effective tumor colonization, and the control of potential safety issues. In the future, the ongoing development of genetic engineering tools and the optimization of bacterial delivery systems are crucial for the translation of microbiome-based therapies into the clinic. This review highlights the potential of bioengineered microbiota as an innovative, personalized approach in cancer immunotherapy, bringing hope for more effective and personalized treatment options for patients with advanced malignancies.

Developing an E. coli-Based Cell-Free Protein Synthesis System for Artificial Spidroin Production and Characterization

Spider silk spidroins, nature's advanced polymers, have long hampered efficient in vitro production due to their considerable size, repetitive sequences, and aggregation-prone nature. This study harnesses the power of a cell-free protein synthesis (CFPS) system, presenting the first successful in vitro production and detailed characterization of recombinant spider silk major ampullate spidroins (MaSps) utilizing a reformulated and optimizedEscherichia coli based CFPS system. Through systematic optimization, including cell strain engineering via knockout generation, energy sources, crowding agents, and amino acid supplementation, we effectively addressed the specific challenges associated with recombinant spidroin biosynthesis, resulting in high yields of 0.61 mg/mL for MaSp1 (69 kDa) and 0.52 mg/mL for MaSp2 (73 kDa). The synthesized spidroins self-assembled into micelles, facilitating efficient purification compared to in vivo methods, and were further processed into prototype silk fiber products. The functional characterization demonstrated that the purified spidroins maintain essential natural properties, such as phase separation and fiber formation triggered by pH and ions. This tailored CFPS platform also facilitates versatile cosynthesis and serves as an accessible platform for studying the supramolecular coassembly and dynamic interactions among spidroins. This CFPS platform offers a viable alternative to conventional in vivo methods, facilitating innovative approaches for silk protein engineering and biomaterial development in a high-throughput, efficient manner.

Novel Anti-MRSA Peptide from Mangrove-Derived Virgibacillus chiguensis FN33 Supported by Genomics and Molecular Dynamics

Antimicrobial resistance (AMR) is a global health threat, with methicillin-resistant Staphylococcus aureus (MRSA) being one of the major resistant pathogens. This study reports the isolation of a novel mangrove-derived bacterium, Virgibacillus chiguensis FN33, as identified through genome analysis and the discovery of a new anionic antimicrobial peptide (AMP) exhibiting anti-MRSA activity. The AMP was composed of 23 amino acids, which were elucidated as NH3-Glu-Gly-Gly-Cys-Gly-Val-Asp-Thr-Trp-Gly-Cys-Leu-Thr-Pro-Cys-His-Cys-Asp-Leu-Phe-Cys-Thr-Thr-COOH. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for MRSA were 8 µg/mL and 16 µg/mL, respectively. FN33 AMP induced cell membrane permeabilization, suggesting a membrane-disrupting mechanism. The AMP remained stable at 30-40 °C but lost activity at higher temperatures and following exposure to proteases, surfactants, and extreme pH. All-atom molecular dynamics simulations showed that the AMP adopts a β-sheet structure upon membrane interaction. These findings suggest that Virgibacillus chiguensis FN33 is a promising source of novel antibacterial agents against MRSA, supporting alternative strategies for drug-resistant infections.

Revolutionizing agroindustry: Towards the industrial application of antimicrobial peptides against pathogens and pests

Antibiotics are essential chemicals for medicine and agritech. However, all antibiotics are small molecules that pathogens evolve antimicrobial resistance (AMR). Alternatively, antimicrobial peptides (AMPs) offer potential to overcome or evade AMR. AMPs provide broad-spectrum activity, favourable biosafety profiles, and a rapid and efficient mechanism of action with low resistance incidence. These properties have driven innovative applications, positioning AMPs as promising contributors to advancements in various industrial sectors. This review evaluates the multifaceted nature of AMPs and their biotechnological applications in underexplored sectors. In the food industry, the application of AMPs helps to suppress the growth of microorganisms, thereby decreasing foodborne illnesses, minimizing food waste, and prolonging the shelf life of products. In animal husbandry and aquaculture, incorporating AMPs into the diet reduces the load of pathogenic microorganisms and enhances growth performance and survival rates. In agriculture, AMPs provide an alternative to decrease the use of chemical pesticides and antibiotics. We also review current methods for obtaining AMPs, including chemical synthesis, recombinant DNA technology, cell-free protein synthesis, and molecular farming, are also reviewed. Finally, we look to the peptide market to assess its status, progress, and transition from the discovery stage to benefits for society and high-quality products. Overall, our review exemplifies the other side of the coin of AMPs and how these molecules provide similar benefits to conventional antibiotics and pesticides in the agritech sector.

AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

INTRODUCTION

Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids.

OBJECTIVES

This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids.

METHODS

This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models.

RESULTS

A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the -log(MIC) values on the benchmark dataset.

CONCLUSION

Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is essential to integrate atomic-level dynamic information that reflects their underlying mechanisms of action.

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries.

HMAMP: Designing Highly Potent Antimicrobial Peptides Using a Hypervolume-Driven Multiobjective Deep Generative Model

Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrug-resistant bacteria, prompting the proposal of many excellent generative models. However, the multiobjective nature of AMP discovery is often overlooked, contributing to the high attrition rate of drug candidates. Here, we propose a novel approach termed hypervolume-driven multiobjective AMP design (HMAMP), which prioritizes the simultaneous optimization of multiattribute AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively biases generative processes and mitigates the pattern collapse issue. Comparative experiments show that HMAMP significantly outperforms state-of-the-art methods in effectiveness and diversity. A knee-based decision strategy is then employed to fast screen candidates with favorable physicochemical properties, aligning with the enhanced antimicrobial activity and reduced side effects. Molecular visualization further elucidates structural and functional properties of the AMPs. Overall, HMAMP is an effective approach to traverse large and complex exploration spaces to search for idealism-realism trade-off AMPs.

Synthesis and Evaluation of Aquatic Antimicrobial Peptides Derived from Marine Metagenomes Using a High-Throughput Screening Approach

Bacterial diseases cause high mortality and considerable losses in aquaculture. The rapid expansion of intensive aquaculture has further increased the risk of large-scale outbreaks. However, the emergence of drug-resistant bacteria, food safety concerns, and environmental regulations have severely limited the availability of antimicrobial. Compared to traditional antibiotics, antimicrobial peptides (AMPs) offer broad spectrum activity, physicochemical stability, and lower resistance development. However, their low natural yield and high extraction costs along with the time-consuming and expensive nature of traditional drug discovery, pose a challenge. In this study, we applied a machine-learning macro-model to predict AMPs from three macrogenomes in the water column of South American white shrimp aquaculture ponds. The AMP content per megabase in the traditional earthen pond (TC1) was 1.8 times higher than in the biofloc pond (ZA1) and 63% higher than in the elevated pond (ZP11). A total of 1033 potential AMPs were predicted, including 6 anionic linear peptides, 616 cationic linear peptides, and 411 cationic cysteine-containing peptides. After screening based on structural, and physio-chemical properties, we selected 10 candidate peptides. Using a rapid high-throughput cell-free protein expression system, we identified nine peptides with antimicrobial activity against aquatic pathogens. Three were further validated through chemical synthesis. The three antimicrobial peptides (K-5, K-58, K-61) showed some inhibitory effects on all four pathogenic bacteria. The MIC of K-5 against Vibrio alginolyticus was 25 μM, the cell viability of the three peptides was higher than 70% at low concentrations (≤12.5 μM), and the hemolysis rate of K-5 and K-58 was lower than 5% at 200 μM. This study highlights the benefits of machine learning in AMP discovery, demonstrates the potential of cell-free protein synthesis systems for peptide screening, and provides an efficient method for high-throughput AMP identification for aquatic applications.

De novo-designed amphiphilic α-helical peptide Z2 exhibits broad-spectrum antimicrobial, anti-biofilm, and anti-inflammatory efficacy in acute Pseudomonas aeruginosa pneumonia

Antimicrobial peptides (AMPs) show considerable promise in combating bacterial infections due to their broad-spectrum efficacy, unique mechanisms of action, and resistance capabilities. In this study, we de novo designed a series of α-helical AMPs (Z1-Z6) with enhanced antimicrobial activity, anti-biofilm, and anti-inflammatory effects. The design incorporated isoleucine with long alkyl side chains and carefully balanced the positive charge and hydrophobicity. Among the designed peptides, Z2 demonstrated remarkable properties. In vitro assays revealed a high therapeutic index, with effective inhibition of 10 pathogenic and drug-resistant bacterial strains by disrupting cell membranes and interacting with bacterial genomes. Z2 also significantly suppressed biofilm formation and reduced reactive oxygen species production in RAW264.7 cells, leading to a decrease in inflammatory cytokine expression, thus showing anti-inflammatory activity. In a mouse model of acute Pseudomonas aeruginosa pneumonia, Z2 significantly improved survival rates, efficiently cleared bacteria from the lungs, and alleviated lung damage. Overall, Z2's unique design endows it with excellent antimicrobial, anti-biofilm, and anti-inflammatory activities, suggesting its great potential as a novel antimicrobial agent for further development. Future research will focus on the studying the drug formulations, elucidating the mechanisms underlying Z2's anti-inflammatory effects and exploring its therapeutic potential in other infection models.

Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms

The occurrence and spread of antimicrobial resistance (AMR) pose a looming threat to human health around the world. Novel antibiotics are urgently needed to address the AMR crisis. In recent years, antimicrobial peptides (AMPs) have gained increasing attention as potential alternatives to conventional antibiotics due to their abundant sources, structural diversity, broad-spectrum antimicrobial activity, and ease of production. Given its significance, there has been a tremendous advancement in the research and development of AMPs. Numerous AMPs have been identified from various natural sources (e.g., plant, animal, human, microorganism) based on either well-established isolation or bioinformatic pipelines. Moreover, computer-assisted strategies (e.g., machine learning (ML) and deep learning (DL)) have emerged as a powerful and promising technology for the accurate prediction and design of new AMPs. It may overcome some of the shortcomings of traditional antibiotic discovery and contribute to the rapid development and translation of AMPs. In these cases, this review aims to appraise the latest advances in identifying and designing AMPs and their significant antimicrobial activities against a wide range of bacterial pathogens. The review also highlights the critical challenges in discovering and applying AMPs.

Exploration of Novel Antimicrobial Agents against Foodborne Pathogens via a Deep Learning Approach

The emergence of antibiotic-resistant bacteria poses a severe threat to food safety and human health, necessitating an urgent search for novel antimicrobial agents that can be applied in the food industry. This study utilizes a deep learning approach to establish the optimal models for antibacterial activity against foodborne pathogens, particularly Escherichia coli and Staphylococcus aureus, as well as for predicting carcinogenicity. These optimal models are applied to screen natural products from the COCONUT database, resulting in the identification of 130 compounds with both antibacterial activity and noncarcinogenic properties. Two natural products, bis(hexamethylene)triamine and N-phenethylbiguanide, are selected for experimental validation of their antibacterial activity. The confirmation of antimicrobial properties validates the reliability of the models developed in this study. By providing an innovative approach for identifying antimicrobial agents for foodborne pathogens, this research offers new insights for discovering effective antimicrobials in an efficient manner.

Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction

Bioactive peptides, as small protein fragments, are essential mediators of diverse physiological activities, such as antimicrobial, anti-inflammatory, anticancer, antioxidant, and immunomodulatory functions. Despite their substantial potential in pharmaceuticals and the food industry, conventional methods for peptide classification and activity prediction are limited by high costs, time-intensive procedures, and extensive data processing requirements. Here, we present BioPepPred-DLEmb, a novel computational model integrating Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs), augmented with natural language processing to encode amino acids into information-dense vectors. Evaluated across nine bioactive peptide datasets, BioPepPred-DLEmb demonstrates superior predictive accuracy (0.909) and sensitivity (0.911) compared to traditional methods. Through UMAP visualization and Kplogo analysis, the model effectively differentiates peptide activity states and identifies key biomarkers. The predicted antimicrobial peptides (Pred-AMPs) exhibit potent efficacy in vitro, achieving low micromolar inhibitory concentrations (2-16 μmol/L) against pathogens such as Escherichia coli and Acinetobacter baumannii. These findings establish a robust foundation for bioactive peptide development, with implications for advancements in precision medicine, personalized therapies, and functional food innovations.

Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning

Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden 'grammars' of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.

Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach, for swiftly designing antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 micrograms per milliliter against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.

Cell-free systems: A synthetic biology tool for rapid prototyping in metabolic engineering

Microbial cell factories provide sustainable alternatives to petroleum-based chemical production using cost-effective substrates. A deep understanding of their metabolism is essential to harness their potential along with continuous efforts to improve productivity and yield. However, the construction and evaluation of numerous genetic variants are time-consuming and labor-intensive. Cell-free systems (CFSs) serve as powerful platforms for rapid prototyping of genetic circuits, metabolic pathways, and enzyme functionality. They offer numerous advantages, including minimizing unwanted metabolic interference, precise control of reaction conditions, reduced labor, and shorter Design-Build-Test-Learn cycles. Additionally, the introduction of in vitro compartmentalization strategies in CFSs enables ultra-high-throughput screening in physically separated spaces, which significantly enhances prototyping efficiency. This review highlights the latest examples of using CFS to overcome prototyping limitations in living cells with a focus on rapid prototyping, particularly regarding gene regulation, enzymes, and multienzymatic reactions in bacteria. Finally, this review evaluates CFSs as a versatile prototyping platform and discusses its future applications, emphasizing its potential for producing high-value chemicals through microbial biosynthesis.

Cell-free protein synthesis platforms for accelerating drug discovery

Cell-free protein synthesis is a platform for streamlined production of macromolecules. Recently, several proteins with pharmaceutical relevance were synthesised and characterised. Off-the-shelf reagents and parallelised experimentation have enabled the exploration of many different conditions for in vitro protein synthesis and engineering. Herein is described how machine learning algorithms were applied for protein yield maximisation as well as for protein engineering and de novo design. Cell-free protein synthesis provides the biotechnological platform to unlock the power and benefit of AI/ML for drug discovery and improve human health.

ConoGPT: Fine-Tuning a Protein Language Model by Incorporating Disulfide Bond Information for Conotoxin Sequence Generation

Conotoxins are a class of peptide toxins secreted by marine mollusks of the Conus genus, characterized by their unique mechanism of action and significant biological activity, making them highly valuable for drug development. However, traditional methods of acquiring conotoxins, such as in vivo extraction or chemical synthesis, face challenges of high costs, long cycles, and limited exploration of sequence diversity. To address these issues, we propose the ConoGPT model, a conotoxin sequence generation model that fine-tunes the ProtGPT2 model by incorporating disulfide bond information. Experimental results demonstrate that sequences generated by ConoGPT exhibit high consistency with authentic conotoxins in physicochemical properties and show considerable potential for generating novel conotoxins. Furthermore, compared to models without disulfide bond information, ConoGPT outperforms in terms of generating sequences with ordered structures. The majority of the filtered sequences were shown to possess significant binding affinities to nicotinic acetylcholine receptor (nAChR) targets based on molecular docking. Molecular dynamics simulations of the selected sequences further confirmed the dynamic stability of the generated sequences in complex with their respective targets. This study not only provides a new technological approach for conotoxin design but also offers a novel strategy for generating functional peptides.

A conditional denoising VAE-based framework for antimicrobial peptides generation with preserving desirable properties

MOTIVATION

The widespread use of antibiotics has led to the emergence of resistant pathogens. Antimicrobial peptides (AMPs) combat bacterial infections by disrupting the integrity of cell membranes, making it challenging for bacteria to develop resistance. Consequently, AMPs offer a promising solution to addressing antibiotic resistance. However, the limited availability of natural AMPs cannot meet the growing demand. While deep learning technologies have advanced AMP generation, conventional models often lack stability and may introduce unforeseen side effects.

RESULTS

This study presents a novel denoising VAE-based model guided by desirable physicochemical properties for AMP generation. The model integrates key features (e.g. molecular weight, isoelectric point, hydrophobicity, etc.), and employs position encoding along with a Transformer architecture to enhance generation accuracy. A customized loss function, combining reconstruction loss, KL divergence, and property preserving loss ensure effective model training. Additionally, the model incorporates a denoising mechanism, enabling it to learn from perturbed inputs, thus maintaining performance under limited training data. Experimental results demonstrate that the proposed model can generate AMPs with desirable functional properties, offering a viable approach for AMP design and analysis, which ultimately contributes to the fight against antibiotic resistance.

AVAILABILITY AND IMPLEMENTATION

The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).

Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens

Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs. Of these, the 6 most effective were synthesized and tested against multidrug-resistant pathogens and demonstrated activity against carbapenem-resistant species Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii, and vancomycin-resistant Enterococcus faecium. The most potent AMP, pep-19-mod, was validated in vivo, achieving over 95% reduction in bacterial loads in mouse models of thigh infection through both systemic and local administration. Our approach advances the automatic identification and optimization of AMPs.

Bioengineering lipid-based synthetic cells for therapeutic protein delivery

Synthetic cells (SCs) offer a promising approach for therapeutic protein delivery, combining principles from synthetic biology and drug delivery. Engineered to mimic natural cells, SCs provide biocompatibility and versatility, with precise control over their architecture and composition. Protein production is essential in living cells, and SCs aim to replicate this process using compartmentalized cell-free protein synthesis systems within lipid bilayers. Lipid bilayers serve as favored membranes in SC design due to their similarity to the biological cell membrane. Moreover, engineering lipidic membranes enable tissue-specific targeting and immune evasion, while stimulus-responsive SCs allow for triggered protein production and release. This Review explores lipid-based SCs as platforms for therapeutic protein delivery, discussing their design principles, functional attributes, and translational challenges and potential.

Cell-free protein synthesis for biocatalysis

Cell-free protein synthesis (CFPS) serves as an innovation booster in the field of biocatalysis. By integrating CFPS into the design and development of biocatalysts, the discovery, synthesis, and screening of previously untapped enzymes and their engineered variants can be improved. The high-throughput capability of CFPS accelerates the identification of optimal synthesis conditions, including expression hosts, chaperone sets, temperature, and codon optimization. Moreover, the availability of various CFPS systems facilitates the incorporation of non-canonical amino acids and enables native post-translational modifications. Using CFPS in combination with enzymatic activity assays also helps to determine the best conditions for biocatalytic reactions, such as temperature, pH, substrate, and choice of cofactor. The compatibility of CFPS with robotic and microfluidic systems, along with artificial intelligence, further enhances its high-throughput capabilities. However, challenges remain regarding scalability, the low concentration of the target protein, and the applicability of a generalized CFPS system for the synthesis of any protein. While these challenges impede the incorporation of CFPS in industrial scale biocatalytic processes, its applicability as screening tool is validated to improve biocatalytic reactions. This knowledge can then be transferred to in vivo synthesis systems to improve the overall production outcomes.

High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry

Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining machine learning and quantum chemistry to accelerate AOP identification and analyze structure-activity relationships. A Bi-LSTM-based model, AOPP, achieved superior performance with accuracies of 0.9043 and 0.9267, precisions of 0.9767 and 0.9848, and Matthews correlation coefficients (MCCs) of 0.818 and 0.859 on two data sets, outperforming existing methods. Compared with XGBoost and LightGBM, AOPP demonstrated a 4.67% improvement in accuracy. Feature fusion significantly enhanced classification, as validated by UMAP visualization. Experimental validation of ten peptides confirmed the antioxidant activity, with LLA exhibiting the highest DPPH and ABTS scavenging rates (0.108 and 0.437 mmol/g, respectively). Quantum chemical calculations identified LLA's lowest HOMO-LUMO gap (ΔE = 0.26 eV) and C3-H26 as the key active site contributing to its superior antioxidant potential. This study highlights the synergy of machine learning and quantum chemistry, offering an efficient framework for AOP discovery with broad applications in therapeutics and functional foods.

Functional antimicrobial peptide-loaded 3D scaffolds for infected bone defect treatment with AI and multidimensional printing

Infection is the most prevalent complication of fractures, particularly in open fractures, and often leads to severe consequences. The emergence of bacterial resistance has significantly exacerbated the burden of infection in clinical practice, making infection control a significant treatment challenge for infectious bone defects. The implantation of a structural stent is necessary to treat large bone defects despite the increased risk of infection. Therefore, there is a need for the development of novel antibacterial therapies. The advancement in antibacterial biomaterials and new antimicrobial drugs offers fresh perspectives on antibacterial treatment. Although antimicrobial 3D scaffolds are currently under intense research focus, relying solely on material properties or antibiotic action remains insufficient. Antimicrobial peptides (AMPs) are one of the most promising new antibacterial therapy approaches. This review discusses the underlying mechanisms behind infectious bone defects and presents research findings on antimicrobial peptides, specifically emphasizing their mechanisms and optimization strategies. We also explore the potential prospects of utilizing antimicrobial peptides in treating infectious bone defects. Furthermore, we propose that artificial intelligence (AI) algorithms can be utilized for predicting the pharmacokinetic properties of AMPs, including absorption, distribution, metabolism, and excretion, and by combining information from genomics, proteomics, metabolomics, and clinical studies with computational models driven by machine learning algorithms, scientists can gain a comprehensive understanding of AMPs' mechanisms of action, therapeutic potential, and optimizing treatment strategies tailored to individual patients, and through interdisciplinary collaborations between computer scientists, biologists, and clinicians, the full potential of AI in accelerating the discovery and development of novel AMPs will be realized. Besides, with the continuous advancements in 3D/4D/5D/6D technology and its integration into bone scaffold materials, we anticipate remarkable progress in the field of regenerative medicine. This review summarizes relevant research on the optimal future for the treatment of infectious bone defects, provides guidance for future novel treatment strategies combining multi-dimensional printing with new antimicrobial agents, and provides a novel and effective solution to the current challenges in the field of bone regeneration.

A review on the screening methods for the discovery of natural antimicrobial peptides

Natural antimicrobial peptides (AMPs) are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens. They have found extensive applications in the fields of medicine, food, and agriculture. However, efficiently screening AMPs from natural sources poses several challenges, including low efficiency and high antibiotic resistance. This review focuses on the action mechanisms of AMPs, both through membrane and non-membrane routes. We thoroughly examine various highly efficient AMP screening methods, including whole-bacterial adsorption binding, cell membrane chromatography (CMC), phospholipid membrane chromatography binding, membrane-mediated capillary electrophoresis (CE), colorimetric assays, thin layer chromatography (TLC), fluorescence-based screening, genetic sequencing-based analysis, computational mining of AMP databases, and virtual screening methods. Additionally, we discuss potential developmental applications for enhancing the efficiency of AMP discovery. This review provides a comprehensive framework for identifying AMPs within complex natural product systems.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Terrabacteria: redefining bacterial envelope diversity, biogenesis and evolution

The bacterial envelope is one of the oldest and most essential cellular components and has been traditionally divided into Gram-positive (monoderm) and Gram-negative (diderm). Recent landmark studies have challenged a major paradigm in microbiology by inferring that the last bacterial common ancestor had a diderm envelope and that the outer membrane (OM) was lost repeatedly in evolution to give rise to monoderms. Intriguingly, OM losses appear to have occurred exclusively in the Terrabacteria, one of the two major clades of bacteria. In this Review, we present current knowledge about the Terrabacteria. We describe their diversity and phylogeny and then highlight the vast phenotypic diversity of the Terrabacteria cell envelopes, which display large deviations from the textbook examples of diderms and monoderms, challenging the classical Gram-positive-Gram-negative divide. We highlight the striking differences in the systems involved in OM biogenesis in Terrabacteria with respect to the classical diderm experimental models and how they provide novel insights into the diversity and biogenesis of the bacterial cell envelope. We also discuss the potential evolutionary steps that might have led to the multiple losses of the OM and speculate on how the very first OM might have emerged before the last bacterial common ancestor.

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

Screening and identification of antimicrobial peptides from the gut microbiome of cockroach Blattella germanica

BACKGROUND

The overuse of antibiotics has led to lethal multi-antibiotic-resistant microorganisms around the globe, with restricted availability of novel antibiotics. Compared to conventional antibiotics, evolutionarily originated antimicrobial peptides (AMPs) are promising alternatives to address these issues. The gut microbiome of Blattella germanica represents a previously untapped resource of naturally evolving AMPs for developing antimicrobial agents.

RESULTS

Using the in-house designed tool "AMPidentifier," AMP candidates were mined from the gut microbiome of B. germanica, and their activities were validated both in vitro and in vivo. Among filtered candidates, AMP1, derived from the symbiotic microorganism Blattabacterium cuenoti, demonstrated broad-spectrum antibacterial activity, low cytotoxicity towards mammalian cells, and a lack of hemolytic effects. Mechanistic studies revealed that AMP1 rapidly permeates the bacterial cell and accumulates intracellularly, resulting in a gradual and mild depolarization of the cell membrane during the initial incubation period, suggesting minimal direct impact on membrane integrity. Furthermore, observations from fluorescence microscopy and scanning electron microscopy indicated abnormalities in bacterial binary fission and compromised cell structure. These findings led to the hypothesis that AMP1 may inhibit bacterial cell wall synthesis. Furthermore, AMP1 showed potent antibacterial and wound healing effects in mice, with comparable performances of vancomycin.

CONCLUSIONS

This study exemplifies an interdisciplinary approach to screening safe and effective AMPs from natural biological tissues, and our identified AMP 1 holds promising potential for clinical application.

New Insights into the Mechanism of Action of L-681,217, a Medicinally Promising Polyketide Inhibitor of Bacterial Protein Translation

An attractive strategy for combating antibacterial resistance involves the development of new antibiotics whose mechanisms differ from those of existing ones in the clinic. Elfamycin antibiotics, whose prototypes include kirromycin and aurodox, are illustrative examples based on their ability to target EF-Tu, an essential component for protein translation in bacteria. Our efforts to revisit this antibiotic class were enabled by two developments. First, we produced L-681,217, an understudied member of this polyketide family harboring a terminal carboxylic acid in place of a hydroxypyridone ring, and synthesized a biotinylated derivative with comparable activity to the natural product. Second, we established a sensitive cell-free protein synthesis (CFPS) assay in which superfolder green fluorescent protein (sfGFP) production was inhibited by L-681,217. Biotinyl-L-681,217 was used to drain the CFPS system of endogenous EF-Tu, allowing replenishment with orthologs to interrogate pathogen selectivity and propensity toward resistance. Comparative in vitro analysis of kirromycin and L-681,217 showed that, while both antibiotics are equipotent in CFPS assays, they interact distinctly with purified EF-Tu, a feature that presumably correlates with prior observations that kirromycin enhances GTP hydrolysis by EF-Tu whereas L-681,217 does not. Analysis of L-681,217 and kirromycin accumulation in selected mutant E. coli strains also revealed that antibiotic import and efflux contributed to resistance. The promise of L-681,217 as a medicinal lead was underscored by the observation that, unlike aurodox, this polyketide does not inhibit adenylosuccinate synthase.

Embleporicin: A Novel Class I Lanthipeptide from the Actinobacteria Embleya sp. NF3

Genome mining has emerged as a revolutionary tool for discovering new ribosomally synthesized and post-translationally modified peptides (RiPPs) in various genomes. Recently, these approaches have been used to detect and explore unique environments as sources of RiPP-producing microorganisms, particularly focusing on endophytic microorganisms found in medicinal plants. Some endophytic actinobacteria, especially strains of Streptomyces, are notable examples of peptide producers, as specific biosynthetic clusters encode them. To uncover the genetic potential of these organisms, we analyzed the genome of the endophytic actinobacterium Embleya sp. NF3 using genome mining and bioinformatics tools. Our analysis led to the identification of a putative class I lanthipeptide. We cloned the core biosynthetic genes of this putative lanthipeptide, named embleporicin, and expressed them in vitro using a cell-free protein system (CFPS). The resulting product demonstrated antimicrobial activity against Micrococcus luteus ATCC 9341. This represents the first RiPP reported in the genus Embleya and the first actinobacterial lanthipeptide produced through cell-free technology.

Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.

Genetic Code Expansion History and Modern Innovations

The genetic code is the foundation for all life. With few exceptions, the translation of nucleic acid messages into proteins follows conserved rules, which are defined by codons that specify each of the 20 proteinogenic amino acids. For decades, leading research groups have developed a catalogue of innovative approaches to extend nature's amino acid repertoire to include one or more noncanonical building blocks in a single protein. In this review, we summarize advances in the history of in vitro and in vivo genetic code expansion, and highlight recent innovations that increase the scope of biochemically accessible monomers and codons. We further summarize state-of-the-art knowledge in engineered cellular translation, as well as alterations to regulatory mechanisms that improve overall genetic code expansion. Finally, we distill existing limitations of these technologies into must-have improvements for the next generation of technologies, and speculate on future strategies that may be capable of overcoming current gaps in knowledge.

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Integrated computational approaches for advancing antimicrobial peptide development

The increasing prevalence of antimicrobial resistance has intensified the need for novel antimicrobial drugs. Antimicrobial peptides (AMPs) are promising alternative antibiotics due to their broad-spectrum activity and slower resistance development. However, the time-consuming, costly development and challenge of systematic optimization limit their translation into the clinic. Recently, integrating computational methods have led to breakthroughs in the precise design and optimization of AMPs, reduced resource consumption, and accelerated AMP development process. We highlight the application of these integrated approaches in AMP molecule discovery, optimization, and delivery and demonstrate the synergy of these strategies to fuel AMP development.

Split Reporters Facilitate Monitoring of Gene Expression and Peptide Production in Linear Cell-Free Transcription-Translation Systems

Cell-free transcription-translation (TXTL) systems expressing genes from linear dsDNA enable the rapid prototyping of genetic devices while avoiding cloning steps. However, repetitive inclusion of a reporter gene is an incompressible cost and sometimes accounts for most of the synthesized DNA length. Here we present reporter systems based on split-GFP systems that reassemble into functional fluorescent proteins and can be used to monitor gene expression in E. coli TXTL. The 135 bp GFP10-11 fragment produces a fluorescent signal comparable to its full-length GFP counterpart when reassembling with its complementary protein synthesized from the 535 bp fragment expressed in TXTL. We show that split reporters can be used to characterize promoter libraries, with data qualitatively comparable to full-length GFP and matching in vivo expression measurements. We also use split reporters as small fusion tags to measure the TXTL protein and peptide production yield. Finally, we generalize our concept by providing a luminescent split reporter based on split-nanoluciferase. The ∼80% gene sequence length reduction afforded by split reporters lowers synthesis costs and liberates space for testing larger devices while producing a reliable output. In the peptide production context, the small size of split reporters compared with full-length GFP is less likely to bias peptide solubility assays. We anticipate that split reporters will facilitate rapid and cost-efficient genetic device prototyping, protein production, and interaction assays.

Application of machine learning based genome sequence analysis in pathogen identification

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Synthesis of secretory leukocyte protease inhibitor using cell-free protein synthesis system

Secretory leukocyte protease inhibitor (SLPI) functions as a protease inhibitor that modulates excessive proteolysis in the body, exhibits broad-spectrum antimicrobial activity, regulates inflammatory responses, and plays an important role in the innate immunity. The purpose of the study was to artificially synthesize a SLPI, an antimicrobial peptide, and investigate its effect on antimicrobial activity against Porphyromonas gingivalis and interleukin-6 (IL-6) production. SLPI protein with a molecular weight of approximately 13 kDa was artificially synthesized using a cell-free protein synthesis (CFPS) system and investigated by western blotting and enzyme-linked immunosorbent assay (ELISA). Disulfide bond isomerase in the protein synthesis mixture increased the amount of SLPI synthesized. The synthesized SLPI (sSLPI) protein was purified and its antimicrobial activity was investigated based on the growth of Porphyromonas gingivalis and bacterial adhesion to oral epithelial cells. The effect of sSLPI on IL-6 production in human periodontal ligament fibroblasts (HPLFs) was examined by ELISA. Our results showed that sSLPI significantly inhibited the growth of Porphyromonas gingivalis and bacterial adhesion to oral epithelial cells and further inhibited IL-6 production by HPLFs. These results suggested that SLPI artificially synthesized using the CFPS system may play a role in the prevention of periodontal diseases through its antimicrobial and anti-inflammatory effects.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

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.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Molecular farming for sustainable production of clinical-grade antimicrobial peptides

Antimicrobial peptides (AMPs) are emerging as next-generation therapeutics due to their broad-spectrum activity against drug-resistant bacterial strains and their ability to eradicate biofilms, modulate immune responses, exert anti-inflammatory effects and improve disease management. They are produced through solid-phase peptide synthesis or in bacterial or yeast cells. Molecular farming, i.e. the production of biologics in plants, offers a low-cost, non-toxic, scalable and simple alternative platform to produce AMPs at a sustainable cost. In this review, we discuss the advantages of molecular farming for producing clinical-grade AMPs, advances in expression and purification systems and the cost advantage for industrial-scale production. We further review how 'green' production is filling the sustainability gap, streamlining patent and regulatory approvals and enabling successful clinical translations that demonstrate the future potential of AMPs produced by molecular farming. Finally, we discuss the regulatory challenges that need to be addressed to fully realize the potential of molecular farming-based AMP production for therapeutics.

Cell-Free Translation Quantification via a Fluorescent Minihelix

Cell-free gene expression systems are used in numerous applications, including medicine making, diagnostics, and educational kits. Accurate quantification of nonfluorescent proteins in these systems remains a challenge. To address this challenge, we report the adaptation and use of an optimized tetra-cysteine minihelix both as a fusion protein and as a standalone reporter with the FlAsH dye. The fluorescent reporter helix is short enough to be encoded on a primer pair to tag any protein of interest via PCR. Both the tagged protein and the standalone reporter can be detected quantitatively in real time or at the end of cell-free expression reactions with standard 96/384-well plate readers, an RT-qPCR system, or gel electrophoresis without the need for staining. The fluorescent signal is stable and correlates linearly with the protein concentration, enabling product quantification. We modified the reporter to study cell-free expression dynamics and engineered ribosome activity. We anticipate that the fluorescent minihelix reporter will facilitate efforts in engineering in vitro transcription and translation systems.

The Development and Opportunities of Predictive Biotechnology

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Mechanism-based and data-driven modeling in cell-free synthetic biology

Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

First vertebrate BRICHOS antimicrobial peptides: β-hairpin host defense peptides in limbless amphibia lung resemble those of marine worms

Innate immunity of invertebrates offers potent antimicrobial peptides (AMPs) against drug-resistant infections. To identify new worm β-hairpin AMPs, we explored the sequence diversity of proteins with a BRICHOS domain, which comprises worm AMP precursors. Strikingly, we discovered new BRICHOS AMPs not in worms, but in caecilians, the least studied clade of vertebrates. Two precursor proteins from Microcaecilia unicolor and Rhinatrema bivittatum resemble SP-C lung surfactants and bear worm AMP-like peptides at C-termini. The analysis of M. unicolor tissue transcriptomes shows that the AMP precursor is highly expressed in the lung along with regular SP-C, suggesting a different, protective function. The peptides form right-twisted β-hairpins, change conformation upon lipid binding, and rapidly disrupt bacterial membranes. Both peptides exhibit broad-spectrum activity against multidrug-resistant ESKAPE pathogens with 1-4 μM MICs and remarkably low toxicity, giving 40-70-fold selectivity towards bacteria. These BRICHOS AMPs, previously unseen in vertebrates, reveal a novel lung innate immunity mechanism and offer a promising antibiotics template.

Cell-free biosynthesis and engineering of ribosomally synthesized lanthipeptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a major class of natural products with diverse chemical structures and potent biological activities. A vast majority of RiPP gene clusters remain unexplored in microbial genomes, which is partially due to the lack of rapid and efficient heterologous expression systems for RiPP characterization and biosynthesis. Here, we report a unified biocatalysis (UniBioCat) system based on cell-free gene expression for rapid biosynthesis and engineering of RiPPs. We demonstrate UniBioCat by reconstituting a full biosynthetic pathway for de novo biosynthesis of salivaricin B, a lanthipeptide RiPP. Next, we delete several protease/peptidase genes from the source strain to enhance the performance of UniBioCat, which then can synthesize and screen salivaricin B variants with enhanced antimicrobial activity. Finally, we show that UniBioCat is generalizable by synthesizing and evaluating the bioactivity of ten uncharacterized lanthipeptides. We expect UniBioCat to accelerate the discovery, characterization, and synthesis of RiPPs.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.