CD ComputaBio offers integrated CDR loop modeling and optimization to improve antibody affinity, specificity, and developability. We combine high-resolution loop conformational sampling, interface analysis, and protein structure modeling with targeted wet-lab validation, delivering actionable design suggestions for humanization, affinity maturation, and bispecific formatting. Our workflow focuses on the complementarity-determining regions—where binding is won or lost—and translates computational predictions into experimentally testable variants.
We build CDR loop models using canonical templates, ab initio methods, and molecular dynamics to capture flexibility. The output is a conformational ensemble for docking and mutation design.
We perform computational alanine scanning and saturating mutagenesis on CDR residues, predict ΔΔG, and prioritize mutations that enhance binding without compromising stability.
We identify critical Vernier zone residues and design back-mutations that preserve loop conformation after humanization. The result is a humanized antibody with retained affinity.
We dock CDR loops against homologs and anti-targets to predict cross-reactivity. In silico specificity profiling helps de-risk early antibody candidates.
We deliver a prioritized list of 5–20 variants with suggested expression tags and SPR/BLI assay conditions, enabling efficient experimental validation.
We evaluate CDR loop mutations for their impact on folding stability, aggregation propensity, and expression, flagging candidates with poor developability early.
We classify CDR loops by canonical class and use template-based modeling for rigid loops. For H3 and other hypervariable loops, we apply ab initio sampling with Rosetta or MODELLER.
We compute binding scores using Rosetta, FoldX, or MM/GBSA and correlate with experimental ΔΔG when available. The best-scoring mutations are advanced for synthesis.
We run explicit-solvent MD simulations to relax loop conformations, capture induced fit, and identify stable interaction patterns for design.
We identify framework residues that support CDR conformation (Vernier zone) and design back-mutations to restore binding after humanization.
We dock the antibody against a panel of homologous proteins and predict binding to off-targets, helping prioritize specificity-enhanced variants.
We use sequence- and structure-based tools (e.g., Aggrescan, Rosetta) to flag mutations that may increase aggregation or reduce thermal stability.
We define the optimization goal (affinity, specificity, humanization, stability) and collect antibody sequences, available structures, and any experimental binding data.
We number the antibody (Kabat/IMGT), classify CDR canonical structures, and build initial loop models using the best templates or ab initio methods for H3.
Loop ensembles are generated via MD or Rosetta, and the antibody-antigen interface is analyzed for hot-spot residues, hydrogen bonds, and shape complementarity.
We perform computational mutagenesis on CDR residues, predict ΔΔG and developability metrics, and rank variants by predicted affinity improvement and risk.
We design a panel of 5–20 variants with expression and assay recommendations. Optionally, we coordinate with wet-lab partners for SPR/BLI validation.
Deliverables include models, ranked variants, predicted mechanisms, and a wet-lab validation plan. We support iterative design cycles based on experimental feedback.
| Objective | Computational Focus | Wet-Lab Integration | Key Deliverables |
|---|---|---|---|
| Increase binding affinity 10–100× | CDR saturation mutagenesis, ΔΔG prediction, MD refinement | Design 10–20 variants for SPR/BLI | Ranked mutation list, predicted ΔΔG, structural models |
| Humanize without losing affinity | CDR grafting, Vernier zone back-mutation analysis | Recommend 5–10 back-mutations for expression | Humanized sequence, retained loop conformation models |
| Reduce polyreactivity / off-target binding | Homolog docking, electrostatic complementarity | Design specificity-enhanced variants for ELISA | Off-target profile, specificity mutation suggestions |
| Improve stability & developability | Aggregation hotspot prediction, stability ΔΔG | Recommend formulation-friendly variants | Stability score, aggregation risk map, final candidate list |
| Build a homology model for a new antibody | Canonical loop modeling, ab initio H3 | Optional: model validation via mutagenesis | 3D antibody model, loop confidence scores |
We improve binding affinity of a lead antibody by 20-fold through targeted CDR-H3 mutations while maintaining stability and low immunogenicity.
We humanize a murine antibody with 95% human framework while restoring key Vernier residues, retaining 90% of parental affinity.
We remodel CDR loops to accommodate two different antigens in a bispecific format, reducing steric clashes and improving dual-binding affinity.
We build a high-confidence homology model of an antibody-antigen complex to guide epitope mapping and rational design.
We identify aggregation-prone CDR motifs and propose conservative mutations that reduce self-association without impacting binding.
We predict off-target binding to closely related proteins and design CDR mutations that enhance specificity for the intended target.
Generic loop modeling often ignores developability and experimental feasibility. Our service integrates high-resolution loop conformational analysis with practical variant design for wet-lab validation. We don't just predict structures—we deliver a ranked, testable panel of antibody variants with clear guidance on assay implementation, helping you save months of screening.
Goal: increase KD from 10 nM to sub-nanomolar while maintaining humanization and low aggregation.
Goal: humanize framework while preserving CDR conformation and binding affinity.
Goal: eliminate off-target binding to dsDNA and insulin while retaining target affinity.
No. We can build homology models from sequence alone using canonical loop templates and ab initio methods for H3. If a crystal structure or cryo-EM map is available, it will improve accuracy, but it is not required for a successful optimization campaign.
Typically we deliver 5–20 prioritized variants depending on the objective and screening capacity. Each variant is ranked by predicted ΔΔG, developability, and synthetic feasibility. We also provide suggested assay conditions to streamline validation.
Yes. We model both binding arms and optimize CDR loops for dual targeting, reducing steric clashes and improving simultaneous binding. We can also evaluate the impact of different formats (e.g., IgG-like, Fab-scFv) on loop conformation.
We combine Rosetta ddG, FoldX, MM/GBSA, and where appropriate, free energy perturbation (FEP) to predict binding affinity changes. Predictions are benchmarked against any available experimental data to calibrate confidence.
We integrate developability filters at every step: aggregation hotspot prediction, charge patch analysis, and thermodynamic stability estimation. Mutations with high aggregation risk are flagged and deprioritized even if they improve affinity.
Share your antibody sequences, optimization goal, and any available binding data. Our team will design a tailored CDR loop modeling and optimization plan, combining computational design with wet-lab validation recommendations to accelerate your antibody engineering.
Submit Project DetailsSubmit your project details below, and our team will respond within 24 hours.
Talk to our technical team about your project!
I Want To Talk
