2025· 150th anniversary of the Metre Convention — From Units to the Universe· 0 citations
TL;DR
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.
Abstract
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.
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
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, M. Rizwan, Vivian Andoh et al.· Journal of Agricultural and...· 0 citations
Synthetic biology applies engineering principles to the rational design of biological systems with the aim of producing predictable and tunable behaviour. Although the field's conceptual foundations and core technologies are well established, the recent simultaneous maturation of Quality by Design (QbD), artificial intelligence (AI)-assisted biological design, and automated biofoundry workflows is beginning to outline a more replicable pathway from laboratory innovation to industrial-scale circular biomanufacturing. This review argues that the convergence of these three elements, rather than any one alone, characterises the current phase of the field. We examine how the Design-Build-Test-Learn (DBTL) cycle is being transformed from a research heuristic into a systematic industrial development framework; show how shared toolsets now transfer across microbial, plant, and animal systems to enable a holistic bioeconomy; benchmark synthetic biology-derived products against conventional alternatives where techno-economic and life-cycle data permit; and examine scale-up, regulatory, and societal bottlenecks through recent market-scale case studies in which these bottlenecks have been navigated in practice. We also contrast EU and US regulatory frameworks to show how policy divergence shapes technology adoption. We conclude by identifying what the coming decade of convergent synthetic biology must deliver to support a circular bioeconomy at the scale the 2030 Agenda demands.
Carlos Belloch-Molina, Francisco Vitor Santos da Silva, John P. Morrissey et al.· Biotechnology Advances· 0 citations
Results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope, and suggest that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope.
Ravi G. Lal, Jason Yang, Zi-Yan Zhang et al.· bioRxiv· 0 citations
A snapshot of AI-driven technologies for AMP design is provided and two modes of AI-driven technologies for AMP design are surveyed, one concentrated on identifying whether current data possess antimicrobial activity and the other on generating AMP candidates with potential therapeutic properties (generation-oriented).
Yong-Qiang Liu, Jie Hu, Ning Zhang et al.· Synthetic and Systems Biotec...· 0 citations
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.
Jenna B. Wolfanger, Shoili Banerjee, Carolyn E. Mills· Chemistry–Methods· 0 citations
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