Collagen, a principal component of the extracellular matrix, provides mechanical strength and stability to tissues and organs through its structural organization. Its biocompatibility has established it as a crucial material in biomedical applications such as drug delivery systems, cell culture matrices, and tissue engineering scaffolds. However, the use of animal-derived collagen carries risks of pathogen transmission, which has driven research towards developing synthetic collagen alternatives. Advances in AI-assisted protein engineering are accelerating the design of synthetic collagens and their applications in biomaterials. This review examines collagen’s structural characteristics, biosynthesis strategies, biological activities as well as AI-assisting engineering.
The discovery of CRISPR based technologies has transformed genome engineering and synthetic biology. With advancements in the ability to do multiplex genome editing, it is now emerging as an ideal approach for trait stacking to improve crops, functional genomics, and complex metabolic engineering in various biological systems. This review discusses engineering and optimization of the latest CRISPR effectors for scalable and precise multiplex editing, ranging from well-known systems like Cas9 and Cas12 variants, to newer, smaller variants such as CasMINI, Cas12j2, and Cas12k. We highlight how the emergence of base editors and prime editors enabled efficient editing across multiple loci without double strand breaks. We also elaborate on the expression and processing strategies of crRNA arrays, which are central to any multiplexing approach. These include tRNA-based and ribozyme-mediated methods, synthetic modular designs, and AI-optimized guide RNAs tailored to diverse systems. Additionally, we assess next-generation delivery platforms such as lipid nanoparticles, virus-like particles, and metal-organic frameworks that overcome conventional barriers in in vivo applications. This review provides a critical take on technological advances enabling precise, high-throughput, and programmable multiplex genome editing across biological systems, setting the foundation for future innovations in synthetic biology, crop improvement, and therapeutic intervention in multigene diseases.
The de novo design of artificial enzymes with customized catalytic functions represents a long-standing challenge in synthetic biology. Recent breakthroughs in deep learning, particularly the rise of Generative Artificial Intelligence (GAI), have transformed enzyme design from structure-centric strategies toward function-oriented paradigms. This review outlines the emerging computational frameworks that now span the entire design pipeline, including active site design, backbone generation, inverse folding, and virtual screening. Detailed description of active site, called a theozyme, is designed to stabilize transition states and can be guided by density functional theory (DFT) calculations that define the geometry of key catalytic components. Guided by the theozyme, GAI approaches such as diffusion and flow-matching models enable the generation of protein backbones pre-configured for catalysis. Inverse folding methods, exemplified by ProteinMPNN and LigandMPNN, further incorporate atomic-level constraints to optimize sequence–function compatibility. To assess and optimize catalytic performance, virtual screening platforms such as PLACER allow evaluation of protein–ligand conformational dynamics under catalytically relevant conditions. Through representative case studies, we illustrate how GAI-driven frameworks facilitate the rational creation of artificial enzymes with architectures distinct from natural homologs, thereby enabling catalytic activities not observed in nature. With the rapid progress and widespread adoption of GAI, we anticipate that de novo enzyme design with customized catalytic functions will soon evolve into a mature and broadly applicable methodology.
