Core Insights - The article discusses the advancements in monoclonal antibody development through the use of artificial intelligence, particularly the introduction of the Monoclonal Antibody Generator (MAGE) which can generate antigen-specific antibodies without the need for initial templates [4][6][10]. Group 1: AI and Antibody Development - The demand for computational tools to accelerate antibody discovery has increased due to the expanding therapeutic market for monoclonal antibodies [3]. - Recent breakthroughs in AI, especially with large language models (LLMs) and diffusion models, have significantly advanced computational methods for antibody design tasks [3][8]. Group 2: MAGE Development - MAGE is a first-in-class model that can design human antibodies targeting multiple antigens without requiring an initial antibody template [6][10]. - The development of MAGE is based on fine-tuning the Progen2 model, which is a self-regressive decoder language model pre-trained on general protein sequences [8]. Group 3: Experimental Validation - MAGE has successfully generated diverse antibody sequences targeting SARS-CoV-2, H5N1 avian influenza virus, and respiratory syncytial virus A (RSV-A), with experimental validation confirming binding specificity [5][11]. - Out of 20 MAGE-generated antibodies tested against the SARS-CoV-2 receptor-binding domain, 9 (45%) confirmed binding specificity, with one showing neutralization efficacy superior to 10 ng/mL [9][10]. Group 4: Unique Features of MAGE - MAGE demonstrates zero-shot learning capabilities, successfully generating antibodies for new antigens not present in the training data, as evidenced by its performance against the H5N1 virus [10]. - The antibodies generated by MAGE exhibit diverse binding modes and can introduce critical amino acid residues that affect functionality [10][11].

Core Insights - The article discusses the development of a novel protein language model (PLM) called Saprot, which integrates one-dimensional amino acid sequences with three-dimensional structural information to enhance protein structure and function prediction [2][9][19] - The launch of the open-source platform SaprotHub aims to democratize access to advanced PLMs for researchers in the life sciences, bridging the gap between AI developers and biologists [3][8][19] Group 1: Challenges in Protein Research - Protein research faces significant challenges due to the technical expertise required for training and deploying advanced AI models, which creates a barrier for biologists engaged in experimental research [5][19] - The complexity of programming environments, data preprocessing, and model training limits the widespread adoption of AI technologies in fields like medicine and biotechnology [5] Group 2: SaprotHub and Its Components - SaprotHub is a comprehensive ecosystem that combines cutting-edge AI model technology, open-source tools, and a global community to facilitate collaboration in protein research [8][19] - The core engine, Saprot, has been trained using millions of protein structures predicted by AlphaFold2, utilizing 64 NVIDIA A100 GPUs, and has demonstrated superior performance in various protein function prediction tasks [9][19] Group 3: Open-Source Tools and Global Collaboration - The ColabSaprot platform simplifies the training of protein language models, allowing researchers without programming backgrounds to easily engage with advanced AI tools [10][19] - The Open Protein Modeling Consortium (OPMC) is a collaborative initiative that includes top research institutions worldwide, aiming to foster the development of the protein field through shared resources and knowledge [11][19] Group 4: Validation and Real-World Applications - The effectiveness of SaprotHub has been validated through user studies and various biological experiments, showing that non-AI researchers can achieve results comparable to AI experts [12][19] - Successful applications include enhancing the activity of an industrial enzyme by 2.55 times, optimizing gene editing tools for doubled efficiency, and designing a new fluorescent protein with over eight times the brightness of the original [18][19]

Core Viewpoint - The rapid development of antibiotic resistance outpaces the discovery of new antibiotics, highlighting the potential of antimicrobial peptides (AMPs) as promising alternatives due to their broad-spectrum antimicrobial activity and unique mechanisms of action [2][6]. Group 1: Antimicrobial Peptides (AMPs) - AMPs are small molecules (10-50 amino acids) that play a crucial role in the host immune defense system, targeting bacteria, fungi, viruses, and parasites [2]. - The mechanisms of AMPs differ from traditional antibiotics, primarily disrupting pathogen cell membranes or interfering with metabolic processes [2][6]. - Despite their potential, the discovery of AMPs remains challenging, necessitating advanced tools like machine learning and deep learning to accelerate research [6][8]. Group 2: Generative Artificial Intelligence in AMP Design - Generative artificial intelligence, particularly through models like AMP-Diffusion, offers a powerful approach for designing AMPs by exploring sequence space systematically [3][7]. - AMP-Diffusion utilizes a pre-trained latent diffusion model to generate potent AMP sequences, ensuring integration with established protein language models like ESM-2 [7][9]. - The model has successfully generated 50,000 candidate AMP sequences, with 76% demonstrating low toxicity and effective bacterial killing capabilities [8][9]. Group 3: Research Findings and Implications - The research team synthesized and validated 46 top-ranking AMP candidates, which exhibited broad-spectrum antimicrobial activity, including against multidrug-resistant strains, with low cytotoxicity [8][9]. - In preclinical mouse models, lead AMPs significantly reduced bacterial load, showing efficacy comparable to polymyxin B and levofloxacin without adverse effects [8][9]. - AMP-Diffusion represents a robust platform for antibiotic design, addressing the urgent need for new antimicrobial agents in the face of rising antibiotic resistance [8][9].

Core Viewpoint - The article discusses the development of a peptide-encoded organ-selective targeting (POST) method that enhances the delivery of mRNA to extrahepatic organs using lipid nanoparticles (LNP) [4][11]. Group 1: mRNA Delivery and LNP Technology - mRNA-based gene and protein replacement technologies present significant opportunities for vaccine, cancer treatment, and regenerative therapy development [2]. - LNPs have been widely adopted as delivery vehicles for mRNA COVID-19 vaccines, demonstrating their safety and efficacy [2]. - Achieving organ-selective delivery of LNPs containing mRNA remains challenging, particularly for extrahepatic organs [2][4]. Group 2: Advances in Organ-Selective Delivery - Recent studies have made progress in organ-selective delivery through simple binary charge modulation and lipid chemical modifications, but these strategies are limited by the rational design of the LNP-environment interface [2][4]. - The POST method utilizes specific amino acid sequences to engineer the surface of LNPs, allowing for efficient mRNA delivery to extrahepatic organs after systemic administration [4][7]. Group 3: Mechanism and Applications - The targeting mechanism of the POST system is based on the optimization of the mechanical affinity between peptide sequences and plasma proteins, forming a specific protein corona around the LNPs [4][9]. - The POST code does not rely on the charge of LNPs for organ selectivity, but rather on the unique protein corona formed, which is influenced by the amino acid sequence [9]. - The POST code is applicable to various LNP formulations and can facilitate the selective delivery of mRNA to organs such as the placenta, bone marrow, adipose tissue, and testes [9][11]. Group 4: AI and Computational Design - The research team developed an AI-based framework using a Transformer-based protein language model to generate peptide sequences with high mechanical affinity for specific proteins, demonstrating the potential of computational design in guiding LNP organ targeting [9][11]. - The peptide sequence RRRYRR was shown to enable selective delivery of mRNA to the lungs, supporting the feasibility of using computer-aided rational design for POST-LNP organ-selective delivery [9][11].

Core Viewpoint - The article discusses the significant environmental challenges posed by plastic waste, particularly PET, and introduces a novel enzyme discovery model, VenusMine, which utilizes protein language models for efficient identification of highly effective PET hydrolases [2][6][13]. Group 1: Enzyme Discovery Model - VenusMine is a protein language model that integrates structural analysis to efficiently mine for PET hydrolases from vast protein databases [6][7]. - The model identifies and clusters target proteins based on the crystal structure of IsPETase, followed by screening for solubility and thermal stability [7][8]. Group 2: Findings and Results - The research team successfully discovered a series of PET hydrolases, with KbPETase from Kibdelosporangium banguiense exhibiting a catalytic efficiency 97 times higher than that of IsPETase [3][8]. - Among the 34 candidate proteins, 14 demonstrated PET degradation activity within the temperature range of 30-60 °C, with KbPETase showing a melting temperature 32°C higher than IsPETase [8][12]. Group 3: Structural Insights - X-ray crystallography and molecular dynamics simulations revealed that KbPETase possesses a conserved catalytic domain and enhanced intramolecular interactions, supporting its improved functionality and thermal stability [12].

Core Viewpoint - The research introduces a machine-learning-assisted strategy called CAGE-Prox vivo for precise protein activation in living organisms, providing a universal platform for time-resolved biological studies and on-demand therapeutic interventions [1][13]. Group 1: Research Background - The study emphasizes the importance of gain-of-function research in understanding biological processes and disease pathology, highlighting various protein engineering techniques that have been developed to manipulate proteins [4]. - Current techniques, while effective, often rely on complex protein constructs that may alter the natural function of target proteins [4][5]. Group 2: CAGE-Prox Strategy - CAGE-Prox is a more universal strategy for controlled activation of a wide range of protein targets, independent of the amino acid residue type at the active site [5]. - The strategy utilizes a light-degradable tyrosine residue (ONBY) to temporarily mask protein activity, allowing for high temporal resolution in studying stimulated cellular processes [5][6]. Group 3: CAGE-Prox vivo Development - The CAGE-Prox vivo strategy incorporates a non-natural amino acid, trans-cyclooctene-tyrosine (TCOY), which can be introduced near the active site of target proteins to temporarily deactivate their function [7][9]. - The research team developed an integrated machine learning process to evolve an aminoacyl-tRNA synthetase (aaRS) that can efficiently incorporate TCOY into proteins [10][11]. Group 4: Applications of CAGE-Prox vivo - The CAGE-Prox vivo system enables precise killing of tumor cells by temporarily inactivating the anthrax lethal factor (LF) and then restoring its activity through a small molecule-triggered bioorthogonal reaction [9][10]. - The strategy also allows for the construction of safer bispecific antibodies that only regain their tumor-targeting function upon specific chemical activation, reducing the risk of cytokine storms and related toxicities [11][12].