Protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain.
Abstract
Abstract With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful functions. The report is organized into eight thematic areas spanning core methodologies and major application domains, including enzymes, therapeutics, detection, synthetic biology, and materials. Contributions from experts across these areas highlight both the historical foundations and recent advances in their respective fields, with particular emphasis on the growing influence of machine learning and artificial intelligence-based methods. Emerging from this broad overview is a central message: protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain. Looking ahead, the continued integration of computational and experimental strategies is poised to further accelerate the impact of protein engineering across an expanding range of economically and societally important sectors, from therapeutics and molecular imaging to diagnostics, plastic recycling, and industrial chemistry.
The chemistry department of the BIPM and the National Physics Laboratory (UK) have collaborated to investigate potential candidate materials for such comparisons, including the measurement of the purity of a de novo peptide, C3triskelion, capable of self-assembling into artificial virus-like capsids exhibiting strong antimicrobial activity.
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Protein assemblies, such as fibers, cages, and sheets, are essential components of biological systems, with versatile functions that make them attractive engineering targets for biotechnological applications. Understanding the complex sequence–structure–function relationships that govern these assemblies is critical for both basic science and the engineering of novel nanomaterials. Deep mutational scanning (DMS) has emerged as a powerful technique for mapping these relationships across large sections of protein sequence space. Specifically, DMS couples high‐throughput assays with next‐generation sequencing technologies to create datasets that report on how changes to protein sequence alter protein function. This review provides an overview of protein assemblies and the basic principles of DMS, followed by a discussion of how DMS has been applied to protein assemblies, and what unique considerations arise when performing such studies. We aim to provide a comprehensive foundation for researchers across biochemistry and chemical biology looking to leverage such high‐throughput approaches to understand and engineer the next generation of protein‐based assemblies.
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This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.
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This survey reviews the basic principles of LLMs and summarizes representative applications in gene and genome sequence analysis, protein structure and function prediction, and drug design, including virtual screening and personalized medicine.
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