Top De Novo Protein Design Papers: November 2025

Hey guys! Here's a rundown of some essential papers in the de novo protein design field, current as of November 1, 2025. This list is your go-to for staying updated on the latest and greatest. Let's dive in!

Last Updated: 2025-11-01 06:38:29 UTC Total Papers: 3

Top Papers by Importance Score

1. De Novo Design of Transmembrane Fluorescence-Activating Proteins

Authors: Jingyi Zhu, Mingfu Liang, Ke Sun, Yu Wei, Ruiying Guo, Lijing Zhang, Junhui Shi, Dan Ma, Qi Hu, Gaoxingyu Huang, Peilong Lu Journal: Nature Publication Date: 2025-02-19 PMID: 39972138 Citations: 15 Impact Factor: 64.80 Importance Score: 70.2/100 DOI: 10.1038/s41586-025-08598-8

Delving into Transmembrane Protein Design

Transmembrane proteins are crucial for various biological processes, including material exchange, energy transfer, and information signaling across cell membranes. De novo design in this area has seen significant progress, building on prior work to create water-soluble proteins that can bind small molecules. This groundbreaking paper focuses on designing transmembrane fluorescence-activating proteins, pushing the boundaries of what's possible in synthetic biology and protein engineering. The implications of successfully designing such proteins are vast. Imagine being able to create custom sensors for detecting specific molecules within a cell or engineering novel therapeutic proteins that can target specific membrane receptors. This research opens up exciting avenues for creating advanced biotechnological tools with applications ranging from diagnostics to drug delivery. Furthermore, the ability to design proteins with specific functions could revolutionize our understanding of cellular processes and pave the way for new treatments for diseases linked to membrane protein dysfunction. The team's approach not only demonstrates the feasibility of de novo design for complex transmembrane proteins but also provides a valuable framework for future research in this area. By combining computational modeling with experimental validation, they have created a powerful methodology that can be adapted to design other types of membrane proteins with tailored functionalities. This research is a significant step forward in the field of protein engineering and highlights the potential of synthetic biology to address some of the most pressing challenges in medicine and biotechnology. The precise control over protein structure and function offered by de novo design promises to unlock new possibilities for creating innovative solutions to a wide range of problems, from developing new drugs to engineering more efficient biofuels.

2. Data-Driven De Novo Design of Super-Adhesive Hydrogels

Authors: Hongguang Liao, Sheng Hu, Hu Yang, Lei Wang, Shinya Tanaka, Ichigaku Takigawa, Wei Li, Hailong Fan, Jian Ping Gong Journal: Nature Publication Date: 2025-08-06 PMID: 40770436 Citations: 13 Impact Factor: 64.80 Importance Score: 69.4/100 DOI: 10.1038/s41586-025-09269-4

Unlocking the Potential of Super-Adhesive Hydrogels through Data

Data-driven methodologies are revolutionizing material science, particularly in the design of materials with well-defined atomic structures. These methods leverage standardized datasets to accurately predict properties and efficiently explore design spaces. This paper highlights the application of these methodologies to de novo design, focusing on the creation of super-adhesive hydrogels. Hydrogels, known for their high water content and biocompatibility, have a wide range of applications, from biomedical implants to tissue engineering. However, their adhesive properties often limit their use in demanding applications. This research addresses this limitation by using data-driven techniques to design hydrogels with enhanced adhesion. The team utilized machine learning algorithms trained on vast datasets of material properties to identify the key factors that contribute to strong adhesion. This allowed them to optimize the composition and structure of the hydrogels, resulting in materials with unprecedented adhesive strength. The implications of this work are significant. Super-adhesive hydrogels could revolutionize wound healing, enabling the creation of more effective wound dressings that promote faster and more complete tissue regeneration. They could also be used in surgical procedures to create stronger and more reliable tissue adhesives, reducing the risk of complications and improving patient outcomes. Furthermore, these hydrogels could find applications in the development of advanced drug delivery systems, allowing for targeted and sustained release of therapeutic agents at the site of injury or disease. The success of this research demonstrates the power of data-driven approaches in material science. By combining experimental data with computational modeling, researchers can accelerate the discovery and design of novel materials with tailored properties, opening up new possibilities for innovation across a wide range of industries.

3. AI-Driven De Novo Enzyme Design: Strategies, Applications, and Future Prospects

Authors: Xi-Chen Cui, Yan Zheng, Ye Liu, Zhiguang Yuchi, Ying-Jin Yuan Journal: Biotechnology Advances Publication Date: 2025-05-12 PMID: 40368118 Citations: 9 Impact Factor: 55.21 Importance Score: 67.6/100 DOI: 10.1016/j.biotechadv.2025.108603

The Future is Now: AI Revolutionizing Enzyme Design

Enzymes are essential for biological processes and have diverse applications across various industries. Traditional enzyme engineering relies on top-down modification strategies like directed evolution to optimize existing enzymes. However, de novo enzyme design, a bottom-up approach, has emerged as a transformative strategy. This review article explores the use of AI-driven methodologies in de novo enzyme design, highlighting strategies, applications, and future prospects. The application of artificial intelligence in this field has the potential to drastically accelerate the design process and enable the creation of enzymes with novel functionalities. AI algorithms can analyze vast datasets of protein structures and sequences to identify patterns and predict the properties of newly designed enzymes. This allows researchers to design enzymes with specific catalytic activities, substrate specificities, and stability profiles. The potential applications of AI-driven de novo enzyme design are vast. In the biopharmaceutical industry, it could be used to create enzymes for the synthesis of complex drug molecules and the production of biologics. In the food industry, it could be used to develop enzymes for improving food processing and enhancing nutritional value. In the environmental sector, it could be used to design enzymes for bioremediation and waste management. Furthermore, AI-driven enzyme design could play a critical role in the development of sustainable biofuels and bioplastics, reducing our reliance on fossil fuels and promoting a more circular economy. This review provides a comprehensive overview of the current state of the field and highlights the key challenges and opportunities that lie ahead. As AI algorithms become more sophisticated and the availability of protein data continues to grow, we can expect to see even more groundbreaking advances in de novo enzyme design in the years to come.

About This List

This list is automatically generated based on:

• Citation Count (40%): Number of times the paper has been cited
• Journal Impact Factor (30%): Impact factor of the publishing journal
• Publication Recency (20%): How recently the paper was published
• Query Relevance (10%): How well the paper matches the search query

Papers are ranked by their composite importance score and updated daily.

Generated by PubMed Miner - 2025-11-01