Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering

Applications and limitations of AI tools in enzyme design

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.

Enzyme Engineering: From Classical Strategies to AI-Driven Biocatalyst Design.

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.

Protein engineering: status report

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.

Hybrid ML/MM Simulations for Computational Enzymology: Recent Advances and Challenges.

The integration of QM/MM methods with molecular dynamics (MD) simulations has become a powerful tool for elucidating enzymatic reaction mechanisms at atomistic resolution, providing valuable insights for biocatalyst design and drug development. However, the high computational cost of QM methods, amplified by the extensive configurational sampling inherent in MD, remains a key bottleneck in studying complex enzymatic processes. Hybrid machine learning/molecular mechanics (ML/MM) methods, which replace the quantum calculations with machine-learned interatomic potentials, offer a promising alternative toward near-QM/MM accuracy at substantially reduced computational cost. This Perspective surveys the central methodological challenges in developing ML/MM frameworks, including the generation of high-quality reference data and the treatment of multiscale coupling. Emerging applications in biosystems are highlighted, and future directions are outlined toward accurate, efficient, and broadly transferable ML/MM models to support next-generation workflows for biomolecular simulations.

Characterization of de novo-designed proteins: towards certified reference materials

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.

Perspectives on Hybridizing Quantum Mechanics and Artificial Intelligence for Drug Design.