Anti-aging Peptides & Protein Engineering
✓ 已验证Finding anti-aging molecules in 3D space · 100% tools deployed & verified
Target 3D structure triple-engine prediction -> molecular docking + affinity calculation -> peptide inverse folding design -> stability + immunogenicity dual screening. 4-step pipeline with all tools deployed and verified, from Protenix V2 to MHCflurry, 38 SOTA models running locally.
Read "Core Capability" and "Benchmarks" below--evaluate our anti-aging peptide dry-computing screening capability and precision.
Focus on "Differentiation", "Pipeline Steps", "Glossary"--evaluate molecular-level computing screening differentiation.
Focus on "Differentiation", "Benchmarks", "Capacity Trace"--evaluate protein engineering model barriers.
What is Protein/Peptide Screening
Anti-aging peptide R&D is essentially finding molecules in 3D space that precisely bind target proteins. Traditional methods rely on high-throughput wet-lab screening--long cycles, high costs, low hit rates. Dry-computing screening uses AI to predict target protein 3D structure, then simulates molecular docking in silico, and finally uses inverse folding design to generate candidate peptide sequences--compressing wet-lab workload by orders of magnitude.
DiVo uses three independent structure prediction engines for cross-validation--Protenix V2, ESMFold, Boltz-2--only entering downstream pipeline when pLDDT>90. From docking, design to immunogenicity screening, 4-step pipeline with 100% tools deployed, 38 SOTA models running locally.
A single structure prediction model may perform differently across protein types. Three independent engines jointly predict, only entering downstream when pLDDT>90--covering different modeling principles and structural spaces, ensuring reliable starting points for downstream docking and design.
Traditional peptide design relies on empirical libraries and random mutations, with limited efficiency and search space. ProteinMPNN/LigandMPNN reverse-engineer optimal sequences from 3D binding pockets--given a structure, find the sequence, directly generating high-affinity candidates.
DiVo Gen²AI's Role
Protein/peptide screening is the core output of DiVo's four-in-one service Pipeline B, and a key step in functional skincare brand anti-aging R&D for dry-computing screening from target to candidate molecules. We provide end-to-end computing from 3D structure prediction to safe peptide candidates--triple-engine cross-validation, molecular docking, inverse folding design, stability + immunogenicity dual screening, all 4 steps with tools deployed.
We do not do wet-lab validation (brand partners confirm after dry-computing screening), do not do GMP production docking (provide sequences + QC standards), do not guarantee in-vivo activity (in-silico docking ≠ in-vivo efficacy). We deliver candidate peptide sequences and QC standards ready for brand partner wet-lab validation.
Core Capability · 4-Step Pipeline
Pipeline B · Protein/peptide screening · All tools deployed & verified
Differentiation
Core differences from single-engine/undeployed protein engineering pipelines
Pro-Prime Replaces FoldX--Stability SOTA
No longer using 2005 FoldX (65-70% accuracy), but deploying Pro-Prime+ProSST for ΔΔG prediction. DDGun (physics-based) as baseline control--if Pro-Prime and DDGun disagree, it signals model hallucination.
BoltzGen 5 Weights Complete--All-Atom Design
BoltzGen 5 weights (7.9G) fully downloaded, 529M parameters. boltz2_aff.ckpt is the architectural basis for DiVoPPIHead affinity prediction. All-atom diffusion design capability ready.
87 Tools Full-Stack Zero Additional Investment
From structure prediction to peptide design to immunogenicity assessment to mRNA delivery design, 87 tools all locally deployed. No additional procurement or deployment waiting needed.
Benchmarks
Model benchmarks and deployment status
| Metric | Value | Note |
|---|---|---|
| Protenix V2 pLDDT | 95.4 | Structure prediction accuracy |
| BoltzGen | 5 weights 7.9G complete | All-atom diffusion design, 529M params (boltz2_aff) |
| Pro-Prime ΔΔG | SOTA | Replaces FoldX (2005) as primary stability prediction tool |
| ProteinMPNN | >52% | Sequence recovery rate (literature benchmark) |
| Immunogenicity pre-screening | AUROC=0.7547 | MHCflurry+NetMHCpan (TESLA benchmark) |
| Deployed tools | 87 | Full tool localization across three satellite projects |
Honest Boundaries
What we can and cannot do, clearly stated
What We Can Do
What We Don't Do
Glossary
7 core terms in protein/peptide screening
| Abbr. | Full Name | Translation | Explanation |
|---|---|---|---|
| pLDDT | predicted Local Distance Difference Test | Local Distance Difference Test | AlphaFold-family model confidence metric, 0-100, >90 indicates highly reliable structure |
| ΔΔG | Delta Delta G | Binding free energy change | Metric measuring protein-ligand binding strength, more negative ΔΔG = stronger binding. Pro-Prime is current SOTA predictor |
| Pro-Prime | Pro-Prime | Protein stability prediction SOTA | Next-generation stability prediction model replacing FoldX (2005, 65-70% accuracy), significantly improved precision |
| BoltzGen | BoltzGen | All-atom diffusion protein design | MIT open-source protein design model, 5 weights (7.9G) complete. boltz2_aff contains 529M-param AffinityModule |
| ProteinMPNN | Protein Message Passing Neural Network | Protein Message Passing NN | Protein inverse folding design model, generates optimal amino acid sequences from 3D backbones, >52% recovery |
| GEMORNA | GEMORNA | mRNA sequence design model | DiVo's in-house fine-tuned mRNA design model, paired with RNALens stability prediction + UTRGAN UTR optimization |
| MMP | Matrix Metalloproteinase | Matrix Metalloproteinase | Zinc-dependent endopeptidase family, commonly used as target in anti-aging peptide screening, regulates ECM degradation |
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