Accelerate vaccine development with structure-guided antigen design. CD ComputaBio integrates homology modeling, epitope prediction, molecular dynamics, and immunoinformatics to engineer stable, immunogenic antigens. We bridge dry-lab predictions with targeted wet-lab validation—ELISA, surface plasmon resonance, and thermal shift assays—to confirm binding and stability.
We build high-confidence 3D models of viral, bacterial, or tumor antigens using homology modeling, threading, and AlphaFold2/ColabFold, followed by loop refinement and stereochemical validation.
We predict linear and conformational epitopes, MHC class I/II binding, and immunogenicity scores. We also design epitope scaffolds and miniproteins for focused immune responses.
We model antigen–antibody complexes using HADDOCK, ClusPro, or AlphaFold-Multimer, then refine with atomistic MD to assess interface stability, hydrogen bonds, and salt bridges.
We identify aggregation-prone regions, design stabilizing mutations, and optimize disulfide bonds to improve antigen half-life and manufacturability.
We model self-assembling nanoparticles displaying antigen trimers or mosaics, predicting assembly stability and immune presentation.
We collaborate with wet-lab partners to validate key predictions—ELISA, SPR, BLI, and thermal shift—ensuring your antigen design is translationally relevant.
We combine deep-learning folding with classical homology modeling to generate reliable antigen structures even for low-sequence-identity targets.
We simulate antigen–antibody complexes in explicit solvent to capture conformational dynamics and compute binding free energies with MM/PBSA or umbrella sampling.
We integrate NetMHC, IEDB tools, and ElliPro to predict linear/conformational epitopes and MHC presentation.
Collect antigen sequence, search for homologous structures, and evaluate template quality. Perform multiple sequence alignment to identify conserved regions.
Generate initial models using AlphaFold2 or homology modeling. Refine loops, optimize side chains, and validate stereochemistry.
Predict B-cell and T-cell epitopes, prioritize neutralizing regions, and design epitope scaffolds or stabilized antigens.
Dock representative antibodies or receptors, then run atomistic MD to assess interface stability and compute binding free energy.
Identify aggregation hotspots and design stabilizing mutations, disulfide bonds, or consensus sequences to improve expression and thermostability.
Collaborate with our wet-lab partners to express, purify, and validate top designs via ELISA, SPR, or thermal shift assays.
Deliver a comprehensive report with structural models, epitope maps, binding energies, stability data, and recommended antigen variants.
| Challenge | Computational approach | Key deliverables | Wet-lab validation |
|---|---|---|---|
| No experimental structure for viral antigen | AlphaFold2 + homology modeling + MD refinement | Validated 3D model, flexibility profile | Circular dichroism (optional) |
| Identify neutralizing epitopes | Epitope prediction + docking + MD | Epitope map, hot-spot residues | Peptide ELISA / SPR |
| Improve antigen thermostability | Aggregation prediction + mutation design + MD | Stabilizing mutations, ΔΔG | Thermal shift assay |
| Design multivalent nanoparticle vaccine | Scaffold modeling + antigen trimer docking | Assembly model, linker design | SEC / DLS (optional) |
Our team combines structural biology, immunoinformatics, and molecular simulation with a pragmatic wet-lab bridge. We don't just predict—we help you prioritize antigen candidates with the highest chance of translational success.
Yes. We routinely use AlphaFold2 and homology modeling to generate high-confidence models from sequence alone, followed by MD refinement and validation.
We validate through multiple orthogonal metrics: binding free energy (MM/PBSA), structural quality assessment, and—when possible—wet-lab assays like SPR or ELISA.
Absolutely. We split large antigens into domains, model each separately, and assemble full-length models. We also handle glycosylation and membrane-proximal regions.
A standard antigen modeling package (model + epitope mapping + MD) takes 2–3 weeks. With wet-lab validation, expect 4–6 weeks.
Yes, we have experience modeling antigen-adjuvant interactions and nanoparticle delivery systems. Please contact us to discuss your specific formulation.
Share your antigen sequence or target pathogen. Our team will design a structure-guided modeling workflow that delivers immunogen candidates, stability improvements, and validation-ready predictions.
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