Jul 2026· Theoretical and Natural Science· Vol 178, pp. 124-136· 0 citations
TL;DR
This paper focuses on protein structure prediction tools represented by AlphaFold, generative models such as RFdiffusion and ProteinMPNN, and the de novo protein design methods driven by these tools, and analyzes the progress of artificial intelligence in protein design and optimization.
Abstract
Protein engineering is a critical technology that modifies protein sequences and structures to optimize specific functions or introduce new ones, and it is widely applied in biomanufacturing, pharmaceutical research and development, synthetic biology and other fields. Traditional protein engineering mainly relies on the modification of natural proteins, and its strategies usually include simulating natural evolution processes or designing based on protein structures, with the aim of enhancing protein stability, activity and other properties. However, the high complexity of protein sequences and the limited understanding of the correspondence between amino acid sequences, protein structures and biological functions have restricted the further development of traditional methods. In recent years, the rapid development of artificial intelligence has greatly driven the paradigm shift in protein engineering research, enabling researchers to efficiently explore proteins with specific structures and functions in a broader sequence space. This paper reviews the basic principles and development history of protein engineering, and introduces traditional strategies such as directed evolution and rational design. It focuses on protein structure prediction tools represented by AlphaFold, generative models such as RFdiffusion and ProteinMPNN, and the de novo protein design methods driven by these tools. Furthermore, combined with classic applications of protein engineering including metabolic engineering, enzyme engineering and antibody engineering, this paper analyzes the progress of artificial intelligence in protein design and optimization. By sorting out relevant studies, this paper aims to provide a reference for understanding the development context of protein engineering and the application of artificial intelligence in this field.
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.
Mati Ullah, Muhammad Rizwan, Vivian Andoh et al.· Journal of Agricultural and...· 0 citations
Engineering biology is a critical technology of global significance, which through the application of rigorous engineering principles promotes the industrialization of biology. A major roadblock towards the sustainable impact of this technology remains in the lack of confidence in the predictability and reproducibility of biological design. This sets new requirements for the development of underpinning metrology which will enable the benchmark assessment of bioengineered systems and processes.
The understanding of the principles governing the folding of primary amino acid sequences has allowed the engineering of de novo peptide and proteins with desirable functions, including self-assembling virus-like particles (VLPs). More recently, machine learning algorithms have arisen as powerful tools to accurately predict the 3D protein structures. The training of these models with larger, high-fidelity datasets opens the possibility to the AI-assisted design of peptides and proteins with promising applications in fields such as cell and gene therapy or vaccine and drug development.
The reproducibility of the emerging manufacturing processes and the traceability of the materials’ desired properties is essential to ensure their efficacy and safety. De novo peptide and protein standards of well-characterized identity, purity, structure and activity are therefore needed to benchmark AI-driven engineered proteins. The CCQM Protein Analysis Working Group is running a series of interlaboratory comparisons to assess National Metrology Institutes’ (NMIs) capabilities to characterize peptide and protein pure standards materials. 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. The material could also be a candidate reference material for VLPs, such as gene-delivery products.
The methodology developed at the BIPM to assign the purity of the C3triskelion included the mass balance method, qNMR and amino acid analysis. Despite challenges in the determination of structurally related impurities, the applied methods showed consistent results, demonstrating for the first time the possibility to value assign the mass fraction content with well-defined measurement uncertainty for this type of bioengineered material.
In addition to describing the measurement methods that have been developed for materials such as triskelion, the poster will also present potential future candidate materials for comparisons and their potential applications, notably how they may be employed to: confirm the purity of a commercial VLP, or virus-derived product as required by the manufacturer or a regulatory body; assist in the quantification of the encapsulation efficacy of a designed gene-delivery system; support validation of VLP performance in different sample matrices, in vitro, and in cell extracts and ultimately in live cells and tissues; and provide a route for measuring the amount of a desired material in target media with well-defined uncertainty traceable to well characterized reference materials.
G. Martos, Andrea Briones, R. Josephs et al.· 150th anniversary of the Met...· 0 citations
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.
Hui-wang Ai, Frances H. Arnold, Doug Barrick et al.· Protein engineering, design...· 0 citations
The extensive application of biosynthetically produced proteins in advanced food systems remains limited because of functional deficiencies, including poor solubility, inadequate emulsifying activity, and low in vitro digestibility. To address this gap, this study aimed to critically examine recent advances in the functional enhancement of biosynthetically produced proteins. It highlights emerging strategies that integrate computational design, artificial intelligence-guided protein engineering, and genetic code expansion to enable precise molecular-level customization. Additionally, the study emphasizes the synergistic benefits of combining chemical, enzymatic, and physical field-assisted modification techniques with macro-scale approaches such as multicomponent self-assembly and nanofabrication. These integrated modification strategies have demonstrated substantial functional gains, including markedly improved thermal stability, substantially enhanced biosynthetic yields, and significantly strengthened antimicrobial activity under food-relevant conditions. In summary, this cross-disciplinary synthesis underscores a transformative pathway toward the development of sustainable, high-performance protein ingredients through closed-loop AI-guided protein design.
Peng Liu, Di Wu, Zhong Zhang et al.· Food Chemistry· 0 citations
A comprehensive introduction and overview of several current artificial intelligence (AI)‐driven methods available for enzyme design, with a focus on reaction‐to‐sequence design, structure prediction, substrate scope prediction, engineering of stable variants, design of enzymes with non‐canonical amino acids, and de novo design is offered.
Rosa Teijeiro-Juiz, Nina Egeler, Grzegorz Jamróg et al.· Protein Science· 2 citations
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
Guodong Min, Huan Peng· Methods in molecular biology· 0 citations