Ref I1
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Computational antibody design covers more ground than the variable domains alone. We model and engineer full-length IgG molecules: CDR grafting and framework selection, germline and humanness assessment, developability liability screening across the sequence, isotype and Fc engineering for the effector profile you need, and structural modelling of the antigen interaction where an epitope is known. The output is a ranked panel of designed sequences, each with the reasoning and the predicted risks stated, ready for expression and testing.
Key Features & Advantages
Developability Screened Sequence-Wide
Deamidation, isomerisation, oxidation, unpaired cysteines, glycosylation motifs, charge patches and hydrophobic patches are mapped across the whole molecule, not only the CDRs.
Fc Engineering To A Stated Goal
Isotype and Fc mutations are selected for the effector function you actually want — enhanced or silenced ADCC and CDC, extended half-life — rather than left at a default.
Framework Choice Justified
Germline framework selection balances humanness, structural compatibility with the grafted loops and manufacturability, and the reasoning is documented per design.
Structural Model Behind Every Design
Variable domain and, where the epitope is known, antigen complex models underpin the design decisions, so proposals rest on geometry rather than on sequence heuristics alone.
Where This Service Is Used
1Converting a hybridoma or phage-derived binder into a developable IgG format
2Designing sequence variants to remove manufacturing and stability liabilities
3Engineering the Fc region for enhanced, silenced or extended-half-life function
4Grafting CDRs from a non-human antibody onto human frameworks
5Building a designed panel for expression when an existing candidate has failed on developability
Methods and Techniques Applied
1Sequence numbering, germline assignment and CDR definition across standard numbering schemes
2Variable domain structure prediction and, where the antigen is known, complex modelling
3Framework selection and CDR grafting with structural compatibility assessment at the graft junctions
4Developability liability mapping with position-specific risk assessment and mitigation design
5Aggregation, charge and hydrophobic patch analysis on the modelled surface
6Fc and isotype engineering for the target effector profile, with known variants applied deliberately
What We Need From You
1Parental antibody sequences (VH and VL), and the constant regions if already fixed
2Antigen sequence or structure and any epitope information from mapping or mutagenesis
3The required functional profile: effector function, half-life, format and intended indication
4Existing expression, stability or binding data for the parental molecule
What You Receive
1Ranked designed sequences in annotated FASTA with the full rationale per variant
2Structural models of the variable domains and, where applicable, the antigen complex
3Developability report with liabilities mapped by position and severity
4Germline and humanness assessment for each design
5Recommended expression and testing order, with the hypothesis each variant is testing
What We Compute and Report
1Designed sequences with germline assignment and humanness scores
2Variable domain and complex models with per-region confidence
3Developability liability map by position: deamidation, isomerisation, oxidation, glycosylation, free cysteine
4Surface charge and hydrophobic patch statistics relevant to aggregation risk
5Predicted effect of each Fc modification on the intended effector profile
Questions We Are Asked Before Scoping
Do you express and test the antibodies?
▸No. ICDMO is a computational CDMO. We design and prioritise; expression, purification and binding characterisation are carried out by your team or a partner, and we help plan and interpret that work.
How reliable is de novo antibody design against a new antigen?
▸Genuine de novo binder design remains low-yield and experimental. Engineering and optimisation of an existing binder is far more reliable, and we distinguish the two clearly at scoping.
Can you predict affinity?
▸Not to a number. We can rank variants and flag mutations likely to be harmful, but predicted affinity values are not reliable enough to act on without measurement.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
A binder from hybridoma or display must be converted into a developable IgG
Effector function has to be tuned deliberately rather than inherited from a default isotype
A panel is needed for expression and the selection should be evidence-based
Consider another service when
The molecule is a single-domain format
VHH / Nanobody Design The priority is removing immunogenicity risk from a non-human sequence
Antibody Humanization What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Liabilities found only in the CDRs while the framework carries its own
Deamidation, isomerisation, oxidation, free cysteines and glycosylation motifs are mapped across the entire sequence.
Isotype chosen by convention, then fighting its effector profile later
Isotype and Fc mutations are selected against the effector outcome you actually want, and the reasoning is documented.
Predicted affinity values used to pick a single construct
Designs are delivered as a ranked panel; predicted affinity is treated as ordering information, not as a measurement.
A Worked Example
Removing a CDR liability without losing binding
The situation
The lead antibody carried an isomerisation-prone motif inside CDR-H2 and degraded on storage.
What the work involved
The variable domains and the antigen complex were modelled, the motif was confirmed to sit at the periphery of the paratope, and substitutions were scored for both binding effect and folding stability.
What came back
Eleven variants across three substitution strategies, with the predicted binding cost stated for each. The panel located a replacement that removed the motif at minimal measured affinity cost.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
What Published Work Reports
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
10- to 140-fold affinity improvements from computational design
Computational interface redesign produced a tenfold affinity improvement in cetuximab, reaching 52 pM, and a 140-fold improvement to 30 pM for an anti-lysozyme model antibody — improvements achieved by designing a small number of interface substitutions rather than by library selection.
Lippow, Wittrup & Tidor, Nature Biotechnology, 2007 ↗Interface models predict which substitutions help, with measurable accuracy
A 2024 study combining structural interface analysis with sequence-derived evolutionary information reported a predictive model for antibody–antigen interaction with an area under the curve of 0.83 and precision of 0.89 on held-out data, and experimentally confirmed a designed point mutation that improved affinity 2.5-fold to 2 nM. Typical computational maturation gains are modest per mutation, which is why a designed panel is measured rather than a single variant.
Experimental validation study, 2024 ↗Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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5
Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day