mRNA's function relies on its 3D structure, which affects stability, protein binding, and translation. In Silico mRNA Structure Prediction Service uses free energy minimization algorithms and published data to predict/optimize sequences, accelerating development before synthesis. Creative Biolabs offers bespoke services, providing insights into folding, stability, and efficiency, plus tailored design recommendations for vaccines, gene editing, or protein replacement to ensure high, stable protein expression.
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The ability to predict and manipulate the complex structures of mRNA is the foundation of next-generation nucleic acid therapeutics and vaccines.
Fig.1 By leveraging artificial intelligence technology, the structural and biological activity information of viruses can be automatically extracted, thereby predicting their molecular characteristics.1,3
We employ a sophisticated suite of methods for predicting and analyzing mRNA structures:
Leveraging computational prediction offers significant benefits over solely empirical methods:
Our In Silico mRNA Structure Prediction Service is critical across numerous applications:
Our structured, data-driven process ensures optimal mRNA design with documented predictability, making the path from sequence to clinic clear and efficient.
Review the provided sequence, define critical functional domains, and establish the design baseline.
Use dynamic programming algorithms to predict the lowest free energy (LFE) secondary structure, identifying unstable regions and internal base-pairing.
Apply advanced modeling to evaluate 3' folding and ribosome accessibility, predicting ribosomal stalling points and efficiency bottlenecks.
Modify the sequence via proprietary algorithms to reduce immunogenicity, enhance stability, and optimize codon usage for the target organism, generating an optimized candidate mRNA sequence.
Compile computational data, structural insights, and design recommendations for client review.
Customized Computational Structure Design:
Use advanced Deep Learning (DL) models to predict therapeutic mRNA's minimum free energy (ΔG) conformation, ensuring maximum stability against nuclease degradation for in vivo applications.
Translational Efficiency Optimization:
Analyze mRNA sequences to identify and eliminate internal secondary structures/inhibitory motifs, maintaining superior translational efficiency for high-level in vivo protein expression.
Integrated RNA Component Engineering:
Custom-design and optimize key RNA components (therapeutic mRNA payloads, auxiliary sequences) to ensure synergy and peak performance in complex biological systems.
Delivery Vector Compatibility Assessment:
Provide sequence-level guidance to optimize mRNA for LNP/EV packaging, enhancing mRNA-vector biocompatibility and promoting targeted transport to specific tissues/cells.
Quality-by-Design (QbD) Documentation Support:
Generate comprehensive docs for in silico modeling, optimization, and data outcomes, providing a data-driven basis to streamline regulatory and CMC submissions.
End-to-End Service Integration:
Link mRNA structure prediction with downstream synthesis, validation, and vector engineering, offering a complete, accelerated path from sequence design to preclinical/clinical readiness.
Recently, a new method based on mRNA structure prediction has been developed, with bone morphogenetic protein 2 (BMP2), an osteogenic growth factor belonging to the TGF-β family, as the test object. The aim is to improve the secretion method of BMP2, with the expectation of enhancing the efficacy of BMP2 gene therapy and reducing the production cost of recombinant BMP2. After obtaining the TGF-β family protein sequences, further nucleotide sequence analysis was conducted on the initial data screened by the computer. Through steps such as initial data set screening, fusion protein screening, mRNA secondary structure and sequence optimization, the nucleotide sequence alignment of 7 SP sequences was finally selected.
Fig.2 Further nucleotide sequence analysis of the initial TGF-β family data screened out by the computer.2,3
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Our predictions are based on established thermodynamic principles and dynamic programming, offering a high degree of confidence by predicting the lowest free energy state. While no computational model is 100% accurate, our methodology significantly narrows down candidates, often achieving excellent correlation with stability and expression data, drastically reducing your experimental workload.
Standard codon optimization focuses only on tRNA abundance (tRNA), but our service is far more comprehensive. We couple codon optimization with structural optimization, ensuring the new sequence doesn't inadvertently create inhibitory secondary structures. This structure-aware design is essential for clinical-grade mRNA performance.
Recommendations may include optimizing the length and sequence of UTRs, adjusting the 5' terminal region for better capping efficiency, or recommending the strategic placement of modified nucleotides (like pseudouridine) to disrupt unfavorable secondary structures and increase overall stability.
While our service provides the most robust sequence design, we strongly recommend follow-up in vitro and in vivo validation. Our goal is to provide you with the optimal sequence to begin those assays, ensuring you test the best possible therapeutic candidate from the start, saving time and resources.
Creative Biolabs' In Silico mRNA Structure Prediction Service delivers the computational foresight required to master therapeutic mRNA design. By leveraging Deep Learning and established thermodynamic principles, we ensure that your therapeutic mRNA is maximally stable and translationally efficient, accelerating your drug candidate from design to clinic.
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