Accelerate antibody discovery with structure-guided epitope prediction, paratope mapping, and cross-reactivity filtering. CD ComputaBio integrates AI-driven epitope modeling, molecular dynamics, and interface energetics to prioritize antigen regions, design antibody panels, and de-risk affinity maturation—complemented by targeted wet-lab validation for critical candidates.
We combine sequence-based immunogenicity scores, surface accessibility, and structural dynamics to predict B-cell epitopes on your antigen—including discontinuous and conformational epitopes missed by linear predictors.
Given antibody sequences or structures, we model paratope residues, CDR loop conformations, and interface contacts to assess complementarity and guide affinity maturation.
We screen predicted epitopes against homologous proteins and human proteome to flag cross-reactivity risks, helping you select antibodies with clean specificity profiles early.
Using interface energetics and molecular dynamics, we predict mutations that enhance binding affinity without compromising stability or introducing developability liabilities.
We model simultaneous binding of two paratopes to distinct epitopes, checking for steric clashes and synergy to support bispecific format design.
We collaborate with wet-lab partners to validate top computational candidates via surface plasmon resonance, biolayer interferometry, or epitope binning, ensuring your project stays grounded in experimental reality.
We leverage transformer-based and graph neural network models trained on antibody-antigen complexes to predict epitope residues and paratope pairing with high accuracy.
We perform rigid and flexible docking followed by MD refinement to generate reliable 3D models of antibody-antigen complexes, even for homology-modeled antibodies.
All-atom MD simulations reveal interface stability, hydrogen bond persistence, and cryptic epitope dynamics that static structures cannot capture.
We combine in silico mutagenesis, free energy calculations, and machine learning to predict mutations that improve affinity while maintaining stability.
We identify non-human framework residues, model back-mutations, and predict immunogenicity risk to support antibody humanization with minimal affinity loss.
Where experimental binding data exist, we use it to retrain or adjust our models, improving prediction accuracy for your specific antigen-antibody system.
We analyze antigen sequence, structure (or model), and known biology to predict immunogenic hotspots, functional epitopes, and potential cross-reactivity regions.
From hybridoma sequences, phage display outputs, or designed variants, we build antibody structure models and prepare them for docking and interface analysis.
Antibody-antigen docking generates binding poses, which are refined and scored using interface energy, shape complementarity, and contact-based metrics.
Top poses undergo all-atom MD to assess stability, identify persistent interactions, and uncover cryptic epitopes or induced-fit effects.
Predicted paratopes are screened against off-target homologs, and antibody variants are checked for aggregation, chemical liabilities, and stability risks.
We deliver a ranked list of antibody candidates with epitope/paratope maps, binding energy estimates, and cross-reactivity flags. Top candidates can be validated via SPR/BLI at partner labs.
From target validation to lead optimization, here's how computational epitope modeling fits in.
| Research Challenge | Recommended Modeling Focus | Key Readouts | Decision Supported |
|---|---|---|---|
| Which epitope on my antigen should I target? | Conformational epitope prediction, surface accessibility, functional site mapping | Ranked epitope clusters, druggability scores, conservation analysis | Select immunogen design or antibody screening strategy |
| Which antibodies from my display campaign are best? | Antibody-antigen docking, interface energetics, MD refinement | Binding energy estimates, H-bond/salt bridge counts, shape complementarity | Prioritize clones for recombinant production and SPR |
| Will my antibody cross-react with related proteins? | Paralog/ortholog epitope similarity screening | Cross-reactivity risk score, off-target epitope alignments | Filter out risky antibodies early or engineer specificity |
| How can I improve affinity without losing stability? | In silico affinity maturation, ΔΔG prediction, stability-aware ranking | Ranked mutation list, predicted ΔG improvements, stability flags | Design a focused mutant library for experimental testing |
| Can I humanize my antibody without losing binding? | CDR grafting, back-mutation design, MHC-II immunogenicity prediction | Humanization variants, predicted immunogenicity, binding retention | Select lead humanized candidates for expression |
| How do I build a bispecific with non-overlapping epitopes? | Ternary complex modeling, epitope distance analysis | Simultaneous binding feasibility, steric clash reports | Choose antibody pairings and format design |
For challenging antigens, we identify the most immunogenic and accessible epitopes to guide immunogen engineering or library panning strategies.
When you need an antibody that binds both human and mouse target, we model cross-species epitope conservation and design paratope mutations for dual binding.
We identify paratope residues responsible for off-target binding and suggest mutations that disrupt cross-reactivity while maintaining on-target affinity.
We model simultaneous binding to two distinct epitopes on the same or different antigens, checking for steric feasibility and optimal geometry.
We map neutralizing epitopes on pathogens and design scaffolded immunogens that present these epitopes for optimal immune recognition.
We compare your target epitope to known antibody patents and literature to assess novelty and freedom-to-operate risks.
We combine state-of-the-art AI, physics-based simulation, and targeted wet-lab validation to deliver antibody discovery insights you can act on. Our models are not black boxes—we explain the structural basis for every prediction and help you design the best next experiment.
Goal: prioritize 50 unique scFv clones for conversion to IgG and SPR validation.
Goal: improve affinity by 10-fold without compromising stability or increasing immunogenicity.
Goal: pair two antibodies targeting PD-1 and CTLA-4 without steric interference.
Not necessarily. We can work with sequence alone using AlphaFold2 or homology modeling to generate a structure, then perform epitope prediction. However, an experimental structure (X-ray or cryo-EM) improves accuracy, especially for conformational epitopes.
We recommend targeted wet-lab validation for top candidates: SPR or BLI for binding kinetics, and epitope binning or competition assays to confirm epitope regions. We can coordinate with partner labs or integrate your existing data to refine our models.
Yes. We build antibody homology models from sequence, with special attention to CDR loop conformations, and then dock them against the antigen. This is a standard part of our pipeline.
For a standard epitope prediction and antibody docking project, we deliver results within 2–3 weeks. Affinity maturation or large library screening may take 4–6 weeks depending on complexity.
Absolutely. We model simultaneous binding of two paratopes to distinct epitopes, check for steric clashes, and recommend optimal pairing and format (e.g., IgG-like, Fab-Fc, etc.).
Share your antigen sequence, antibody variants, or discovery challenge. Our team will design a computational epitope modeling campaign tailored to your project, with clear deliverables and wet-lab validation options.
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