Core Insights - The research team from Westlake University's School of Life Sciences achieved two "world firsts" in the field of protein design: the precise de novo design of voltage-gated anion channels and the successful in vivo testing of artificially designed ion channel proteins [1][3][4] Group 1: Research Achievements - The team successfully designed a stable channel framework using traditional protein design algorithms combined with deep learning methods, resulting in a pentameric transmembrane protein that forms an ion channel [3] - The design includes a critical "gate" mechanism that incorporates three layers of arginine, functioning as both a voltage sensor and an ion-selective filter [3][4] - Experimental results demonstrated that at a voltage of 40 millivolts, the current significantly increased, confirming the voltage-gated design intent and showcasing the ion-selective capabilities of the channel [3] Group 2: Implications for Future Research - This breakthrough allows for the transition from designing static membrane proteins to creating dynamic transmembrane proteins that can respond to external stimuli and undergo conformational changes [4] - The collaboration with another research team led to the implantation of the artificial channel into mouse brain neurons, resulting in a significant decrease in neuronal firing frequency, indicating the functionality of the designed ion channel protein under physiological conditions [4] - The research highlights the immense potential of de novo protein design and brings the development of artificial ion channel protein drugs for regulating cellular and neural activities closer to reality [4]

Core Viewpoint - The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks [1] Group 1: Awardees Background - Susumu Kitagawa, born in 1951, is affiliated with Kyoto University and focuses on the fundamental research and application development of metal-organic framework materials [3] - Richard Robson, born in 1937, works at the University of Melbourne and has made significant contributions to the theoretical foundations of metal-organic frameworks [6] - Omar M. Yaghi is a professor at the University of California, Berkeley, known for major breakthroughs in the synthesis methods and practical applications of metal-organic frameworks [9] Group 2: Nobel Prize History - As of October 2024, the Nobel Prize in Chemistry has been awarded 116 times to 197 recipients, with 63 awards given to individuals, 25 shared by two, and 28 shared by three [12] - Notable statistics include 8 years where the award was not given, 9 years of delayed awards, and the recognition of 8 female laureates [12] - The youngest laureate was Jean Frédéric Joliot-Curie, who won at age 35 in 1935, while the oldest was John Goodenough, awarded at age 97 for his work on lithium batteries [12] Group 3: Recent Nobel Prize Winners - In 2024, half of the prize was awarded to David Baker, with the other half shared by Demis Hassabis and John Jumper for their contributions to protein design and structure prediction [13] - The 2023 prize was awarded to Mogi Bawendi, Louis Brus, and Alexei Ekimov for their discovery and synthesis of quantum dots [14] - In 2022, the award went to Carolyn Bertozzi, Morten Meldal, and Carolyn Bertozzi for their work in click chemistry and bioorthogonal chemistry [15]

Core Points - The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks [1] Group 1 - As of October 2024, the Nobel Prize in Chemistry has been awarded 116 times to 197 recipients, with 63 awards given to individuals, 25 shared by two, and 28 shared by three [2] - Notable statistics include 8 years where the award was not given, 9 years of delayed awards, and the fact that 2 individuals have won the prize twice [2] - The youngest laureate was Jean Frédéric Joliot-Curie, who won at age 35 in 1935, while the oldest was John Goodenough, who won at age 97 and passed away in June 2023 at the age of 100 [2] Group 2 - Recent Nobel Prize winners in Chemistry include David Baker, Demis Hassabis, and John Jumper in 2024 for their work in protein design and structure prediction [3] - In 2023, the prize was awarded to Mogi Bawendi, Louis Bruce, and Alexei Ekimov for their discovery and synthesis of quantum dots [4] - The 2022 award went to Carolyn Bertozzi, Morten Meldal, and Carolyn Bertozzi for their contributions to click chemistry and bioorthogonal chemistry [4]

Core Insights - The article discusses the transformative impact of artificial intelligence (AI) on protein design, revolutionizing methods for drug discovery, biotechnology, and synthetic biology applications [2][3]. - A comprehensive and actionable roadmap for integrating advanced AI tools into protein design workflows is provided, highlighting AI's potential to innovate synthetic biology, accelerate drug development, and drive sustainable biotechnology [3][8]. Summary by Sections Overview of Protein Design - Protein design has long been a cornerstone of scientific innovation, driving breakthroughs in drug development, biotechnology, and synthetic biology. However, traditional methods are nearing their limits in addressing the vast complexity and diversity of protein sequences [5][6]. AI's Role in Protein Design - AI is emerging as a transformative force to tackle challenges previously deemed unsolvable, enhancing both directed evolution and rational design strategies. Directed evolution simulates natural selection through random mutations, while rational design relies on structural and functional data [6][7]. - The search space for protein design is immense, with a typical protein of 350 amino acids having approximately 10^455 possible sequences, making exhaustive exploration impractical [6][7]. Development of AI Tools - AI-driven advancements have led to the development of new tools that provide unprecedented speed, scale, and precision in both directed evolution and rational design. AI tools can accurately propose beneficial mutations and predict functions from sequences, significantly shortening experimental cycles [7][8]. - The integration of deep learning methods into protein design workflows is not only feasible but essential, transforming the process from trial-and-error to a predictive and efficient discipline [7][9]. AI-Driven Protein Design Roadmap - The article outlines a roadmap for integrating AI tools into protein design, categorizing them into seven toolkits that support various tasks throughout the workflow, from initial design to experimental validation [9][22]. - Each stage of the protein design process is matched with the most suitable AI toolkit, guiding designers in assembling end-to-end AI-driven workflows [9][24]. Case Studies - AI-driven directed evolution of adeno-associated virus (AAV) capsids involved introducing random mutations to generate a virtual library of 10^10 AAV2 sequences, resulting in 20,426 sequences being experimentally validated [27]. - AI-driven antibody directed evolution utilized the ESM protein language model to generate heavy and light chain variants, achieving binding affinity improvements of up to 160 times [27]. - Rational design of a novel luciferase involved using AI tools to optimize the structure and function, resulting in variants with excellent thermal stability and specificity [28]. Future Directions - The next generation of AI tools must be built on robust and diverse data foundations to address challenges in protein design, including the need for explainable AI methods to enhance trust and adoption [29][30]. - AI-driven protein design is poised to open a new era of precision therapeutics, enabling the targeting of previously "undruggable" proteins and accelerating the design-manufacture-test-analyze cycle [31][32].

Core Viewpoint - The article discusses the breakthrough technology BindCraft, which allows for the one-shot design of functional protein binders with a success rate of 10%-100%, significantly improving the efficiency of protein design compared to traditional methods [2][3][5]. Summary by Sections BindCraft Technology - BindCraft is an open-source, automated platform for de novo design of protein binders, achieving high-affinity binders without the need for high-throughput screening or experimental optimization [3][5]. - The technology leverages AlphaFold2's weights to generate protein binders with nanomolar affinity, even in the absence of known binding sites [3][5]. Applications and Results - The research team successfully designed binders targeting challenging targets such as cell surface receptors, common allergens, de novo proteins, and multi-domain nucleases like CRISPR-Cas9 [3][7]. - Specific applications include: 1. Designing antibody drugs targeting therapeutic cell surface receptors like PD-1 and PD-L1, achieving nanomolar affinity without extensive design and screening [7]. 2. Blocking allergens, with a designed binder for birch pollen allergen Bet v1 showing a 50% reduction in IgE binding in patient serum tests [7][8]. 3. Regulating CRISPR gene editing by designing a new inhibitory protein that significantly reduces Cas9's gene editing activity in HEK293 cells [8]. 4. Neutralizing deadly bacterial toxins, with a designed protein completely eliminating cell death caused by the toxin from Clostridium perfringens [8]. 5. Modifying AAV for targeted gene delivery by integrating mini-binders that specifically target HER2 and PD-L1 expressing cancer cells [8]. Impact and Future Potential - BindCraft addresses long-standing success rate bottlenecks in protein design and offers direct solutions for allergy treatment, gene editing safety, toxin neutralization, and targeted gene therapy [9]. - The open-source nature of the technology allows ordinary laboratories to design custom proteins, potentially reshaping drug development, disease diagnosis, and biotechnology [9].

Core Insights - The article discusses the significant advancements in the field of protein design, particularly focusing on light-responsive proteins and the challenges associated with their de novo design [5][6][12]. Group 1: Research Development - On August 28, 2025, a team from West Lake University published a study in Nature Chemistry, detailing the de novo design of light-responsive protein-protein interactions, enabling reversible formation of protein assemblies [3]. - The research developed a protein docking program suitable for non-natural amino acids, integrating light-responsive non-natural amino acid AzoF with codon expansion technology to design a series of reversible light-responsive proteins [3][7]. Group 2: Challenges in Protein Design - Despite advancements, a major challenge remains in programming new proteins to respond to environmental stimuli and switch between different structural states, which is crucial for precise control of their structure and function [5][6]. - Natural light-responsive proteins have limitations, such as the need for continuous light exposure, long reverse process times, and complex folding, making them difficult to express and apply in heterologous systems [6]. Group 3: Innovations and Applications - The research team successfully designed a variety of protein complexes, including light-responsive homopolymeric and heterodimeric proteins, demonstrating excellent light-responsive characteristics [7][8]. - The heterodimer LRD-7 showed a remarkable affinity change of 167 times in response to light, and the designed proteins exhibited strong thermal stability, maintaining their secondary structure even after heating to 95°C [8]. - The study also explored the creation of light-responsive protein hydrogels and the use of heterodimeric proteins to control gene expression signals [10][12]. Group 4: Future Implications - This research not only resulted in a series of de novo designed light-responsive proteins but also provided new methods and ideas for the design of light-controlled protein-protein interactions, laying a solid foundation for future developments in light-responsive target-binding proteins and molecular machines [12].

Core Viewpoint - The article discusses the development of a new framework, RFpeptides, for the de novo design of high-affinity macrocyclic peptides targeting proteins, utilizing advancements in deep learning and artificial intelligence [2][3][10]. Group 1: Background and Challenges - Traditional methods for peptide drug development rely on natural product discovery or high-throughput screening of random peptides, which are resource-intensive and limited in scope [6][8]. - The challenges in natural product discovery include difficulties in synthesis, poor stability, and low tolerance to mutations [6]. - High-throughput screening methods, while powerful, are time-consuming and costly, covering only a small fraction of the chemical diversity available in macrocyclic compounds [6][9]. Group 2: Innovations in Design Methodology - The RFpeptides framework allows for precise de novo design of macrocyclic peptides with high affinity for target proteins, addressing the limitations of previous methods [3][12]. - The research team expanded existing structural prediction networks and protein backbone generation frameworks to incorporate cyclic relative position encoding, enhancing the design process [12]. Group 3: Experimental Results - The team tested up to 20 designed macrocyclic peptides against four different proteins (MCL1, MDM2, GABARAP, and RbtA), achieving medium to high affinity binders for all targets [13]. - Notably, a high-affinity binder for RbtA was designed with a dissociation constant (K_d) of less than 10 nM based solely on predicted target structure [13]. - Structural analysis of the designed macrocyclic peptide complexes with MCL1, GABARAP, and RbtA showed high agreement with computational models, with Cα RMSD values less than 1.5 Å [14]. Group 4: Implications and Future Applications - The RFpeptides framework provides a systematic approach for the rapid custom design of macrocyclic peptides for diagnostic and therapeutic applications, indicating significant potential in the pharmaceutical industry [16].