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Record W7117139035 · doi:10.1021/accountsmr.5c00169

Genetically Encoded Design and Biomanufacturing of Mechanical Protein Materials

2025· article· en· W7117139035 on OpenAlexaff
Sikang Wan, Baiqi Shao, Chao Ma, Hongjie Zhang, Kai Liu

Bibliographic record

VenueAccounts of Materials Research · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsMinistry of Advanced Education
FundersBeijing Municipal Natural Science FoundationChina Postdoctoral Science FoundationWenzhou Institute of Biomaterials and EngineeringNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaChina Association for Science and Technology
KeywordsBiomanufacturingSynthetic biologyBespokeGenetically engineeredProtein engineeringProtein designModular design

Abstract

fetched live from OpenAlex

Conspectus Natural protein materials such as spider silks, fibroins, and mussel foot proteins epitomize the pinnacle of mechanical performance in biological matter. Their unparalleled strength-to-weight ratios, inherent biocompatibility, and precise programmability at the amino acid level make them ideal blueprints for next-generation structural biomaterials. However, these advantages are counterbalanced by inherent limitations: scarce natural availability, limited functional diversity in native sequences, and challenges in scalable processing and reproduction. Recent advances in gene editing and synthetic biology have enabled the de novo design of artificial mechanical proteins, bypassing these constraints. By modularly integrating mechanical, metal-coordinating (e.g., LanM and amyloid motifs), and bioactive domains (e.g., antifreeze and epidermal growth factor modules) from phylogenetically diverse species, researchers now exert unprecedented control over the protein sequence, hierarchical architecture, and macroscopic behavior. These engineered variants have already led to advanced biomedical products, including surgical sutures, hernia meshes, and hemostatic sealants, that outperform conventional synthetic polymers in both functionality and biocompatibility. Nonetheless, scalable biomanufacturing remains hindered by the structural features that underpin mechanical superiority: highly repetitive motifs and high molecular weights often promote aggregation, proteolytic instability, and low recombinant yields in conventional expression systems. To overcome these bottlenecks, extensive efforts have focused on optimizing chassis cells, such as E. coli and C. glutamicum, through glycyl-tRNA enrichment, CRISPRi-enabled secretion screening, codon scrambling, and signal peptide engineering. Moreover, the fidelity of supramolecular assembly critically determines the ultimate performance of the material, necessitating precise control over the processing conditions. This Account provides a critical review of recent advances in modular protein strategies for fabricating high-performance biomaterials, organized around three synergistic pillars: (i) rational modular design, which combines rigid β-sheet domains (e.g., SRT or spidroins), flexible linkers (e.g., ELP or resilin), metal-binding motifs, and functional peptides to orthogonally tailor strength, toughness, and bioactivity; (ii) chassis cell optimization through transcriptional, translational, and post-translational engineering to enhance the synthesis and secretion of repetitive proteins; and (iii) biomimetic assembly techniques, such as microfluidic spinning and liquid–liquid phase separation, enabling the precise alignment of β-sheet nanocrystals and multiforce-directed cross-linking into hierarchical fibers and glues with toughness exceeding 200 MJ/m 3 and adhesive strength over 30 MPa. Looking forward, we advocate for the integration of these experimental approaches with AI-driven design loops that combine de novo sequence generation, mechanical property prediction, and automated process optimization. Such closed-loop, data-driven frameworks promise to drastically compress development timelines, improve production yields, and enable scalable, sustainable manufacturing of protein materials tailored for cutting-edge applications in biomedicine, soft robotics, and sustainable materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.356
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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