mRNA Sequence Optimization
✓ 已验证RNALens · Spearman 0.92 · CAI 0.7->0.95 · Self-tuned model
mRNA vaccine sequence design is not just "translating amino acids to bases". Codon usage frequency affects translation efficiency, UTR structure affects stability and loading efficiency. DiVo Gen²AI uses the self-tuned RNALens model to predict ribosome loading efficiency--not calling third-party APIs, but part of 560K lines of self-developed code. This page serves three audiences: patients and public wanting to understand mRNA optimization, vaccine/CAR-T teams seeking sequence optimization partners, and investors and peers evaluating technical capabilities.
Read "What is mRNA Sequence Optimization" below--understand why vaccine design is not just translating bases.
Focus on "DiVo's Role", "4-Step Pipeline", "Benchmarks"--self-tuned, not API wrapper.
Focus on "Differentiation", "Benchmarks", "Glossary"--evaluate the technical barrier of self-tuned models.
mRNA sequence optimization is a key step in the following flagship services
What is mRNA Sequence Optimization
The core of an mRNA vaccine is a messenger RNA sequence--after being delivered into human cells, it directs cells to synthesize target proteins (e.g., tumor antigens), triggering immune response. But the same amino acid can be encoded by multiple different codons, and codon usage frequencies vary greatly.
mRNA sequence optimization is not simply translating amino acid sequences to bases--it is selecting the sequence with the highest translation efficiency and best stability from all candidate sequences that can encode the same protein. Codon choice affects translation speed (CAI), UTR structure affects mRNA half-life and ribosome loading (MRL), and different target tissues have different codon preferences.
DiVo uses the self-tuned RNALens model to predict ribosome loading efficiency (Spearman=0.92), not calling third-party APIs--fine-tuning data, training pipeline, and inference service are all self-deployed.
The same amino acid has 2-6 codons. Human cells translate some codons much faster than others--choosing the right codons can improve translation efficiency from 0.7 to 0.95 (CAI index).
General pre-trained models have limited prediction accuracy for mRNA sequences. Three-stage fine-tuning makes RNALens specifically adapted to mRNA MRL tasks--Spearman=0.92 is not a zero-shot performance of a general model, but a specialized result after domain adaptation.
DiVo Gen²AI's Role
mRNA sequence optimization is Step 7 of DiVo's neoantigen 8-step pipeline, and the core capability for CAR construct mRNA optimization in the CAR-T 5-step pipeline. We provide end-to-end from codon optimization to ribosome loading prediction to UTR optimization--not just doing CAI optimization, but simultaneously optimizing coding and regulatory regions, with self-tuned model prediction for functional validation.
We do not do mRNA delivery system design (LNP formulation, etc.), do not do in vivo expression efficiency experimental validation. We deliver computationally optimized mRNA sequence design proposals.
Core Capability · 4-Step Pipeline
From amino acid sequence to tissue-adapted optimal mRNA sequence
Codon Optimization (CAI)
✓ 已验证In-house codon optimization algorithm
CAI 0.7 -> 0.95 optimized sequence
Ribosome Loading Efficiency Prediction
✓ 已验证RNALens (DNABERT-Z 117M fine-tuned)
MRL prediction + Spearman=0.92
UTR Optimization + Stability Assessment
✓ 已验证GEMORNA + ViennaRNA
5'/3' UTR optimized sequence + secondary structure score
Tissue-Adapted Model Selection
✓ 已验证HEK293T / Muscle / PC3
Target tissue-adapted optimal sequence
Differentiation
Core differences from "CAI-only" and "third-party API" approaches
Self-Tuned, Not Third-Party API
RNALens is based on DNABERT-Z 117M parameter three-stage fine-tuning. Fine-tuning data, training pipeline, and inference service are all self-deployed. Not a wrapper around third-party model APIs, but part of 560K lines of self-developed code.
Optimizing Regulatory Regions, Not Just Coding
Most mRNA optimization services only do CDS codon optimization. DiVo uses GEMORNA to generate UTR + ViennaRNA to assess stability, simultaneously optimizing coding and regulatory regions--both translation efficiency and stability.
Core Capabilities
CAI Codon Adaptation Index Optimization
✓ 已验证Improved from 0.7 to 0.95--codon usage frequency aligned with host cell preferences, greatly improved translation efficiency.
MRL Ribosome Loading Efficiency Prediction
✓ 已验证RNALens model based on DNABERT-Z 117M parameter three-stage fine-tuning. Spearman=0.92, R²=0.87--predicts ribosome loading efficiency of mRNA sequences.
5'/3' UTR Optimization
✓ 已验证GEMORNA generation + ViennaRNA stability assessment. Not just optimizing coding regions, but also regulatory regions.
3 Tissue-Adapted Models
✓ 已验证HEK293T / Muscle / PC3 three target tissue/cell line adapted models--different tissues prefer different codons.
Why We Emphasize "Self-Tuned"
Not Calling Third-Party APIs
RNALens is a self-developed model based on DNABERT-Z 117M parameter three-stage fine-tuning. Fine-tuning data, training pipeline, and inference service are all self-deployed.
Not General Pre-Trained Weights
Three-stage fine-tuning: (1) DNABERT-Z 117M pre-training -> (2) mRNA MRL downstream task fine-tuning -> (3) DiVo self-developed data domain adaptation fine-tuning.
Has Real-World Validation
Asparaginase project: 6194 mutations × 11 iterations. CAI 0.7->0.95. Not a toy project.
Benchmarks
| Metric | Value | Note |
|---|---|---|
| MRL Spearman | 0.92 | Ribosome loading efficiency prediction correlation |
| R² | 0.87 | Coefficient of determination |
| CAI optimized | 0.95 | Improved from 0.7 |
| DNABERT-Z parameters | 117M | Three-stage fine-tuning |
| Tissue-adapted models | 3 | HEK293T / Muscle / PC3 |
| Real-world validation | Asparaginase 6194 mutations | 11 iterations |
Honest Boundaries
What we can and cannot do, clearly stated
What We Can Do
What We Don't Do
Glossary
5 core terms in mRNA sequence optimization
| Abbr. | Full Name | Translation | Explanation |
|---|---|---|---|
| CAI | Codon Adaptation Index | Codon Adaptation Index | Measures how well codon usage frequency matches host cell preferences; higher means better translation efficiency |
| MRL | Mean Ribosome Loading | Mean Ribosome Loading | Average number of ribosomes on an mRNA, reflecting translation initiation efficiency |
| UTR | Untranslated Region | Untranslated Region | Regulatory sequences at both ends of mRNA that don't encode proteins; 5' UTR affects translation initiation, 3' UTR affects stability |
| RNALens | DiVo RNALens | DiVo RNA Prediction Model | Self-developed mRNA property prediction model based on DNABERT-Z 117M three-stage fine-tuning |
| GEMORNA | Generative mRNA | mRNA Generative Model | Generative model for generating and optimizing mRNA UTR sequences |
CAPACITY TRACE
能力回溯
这项服务由哪些能力支撑——从硅片到你的场景
硅片(L1) → 模型(L2) → Agent(L3) → 管线(L4) → 你的场景