Ref I5
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Humanisation reduces the immunogenicity risk of a non-human antibody while trying not to lose the binding that made it worth developing. We assess the parental sequence against human germlines, select frameworks on structural as well as sequence grounds, design the graft with a reasoned set of back-mutations, and score each variant for humanness and predicted immunogenicity. Designs are delivered as a graded series so the trade-off between humanness and retained affinity can be measured rather than argued about.
Key Features & Advantages
Structure-Guided Back-Mutation
Vernier zone, canonical-class and interface residues are evaluated structurally, so back-mutations are proposed where they support the loop conformation rather than by sequence rule alone.
Graded Variant Series
Conservative through aggressive designs are delivered together, letting a small expression panel establish where affinity is lost instead of betting on a single construct.
Immunogenicity Assessed Two Ways
Germline content scoring and MHC class II binding prediction are used together, since each captures a different aspect of the clinical immunogenicity risk.
Liabilities Removed In The Same Pass
Developability motifs present in the parental sequence are addressed during humanisation, avoiding a second engineering cycle later.
Where This Service Is Used
1Humanising a murine antibody derived from hybridoma work
2Humanising a camelid VHH framework for therapeutic development
3Improving the humanness score of an existing humanised antibody
4Assessing the immunogenicity risk of a candidate before committing to development
5Recovering affinity in a humanised construct that lost potency relative to the parent
Methods and Techniques Applied
1Germline assignment, CDR definition and identification of the structurally critical framework positions
2Variable domain structure modelling of the parental antibody, with the antigen complex where available
3Human framework selection balancing sequence identity, canonical class compatibility and germline frequency
4CDR grafting with structural evaluation of the graft junctions and supporting loops
5Back-mutation design informed by Vernier zone, interface and canonical-structure analysis
6Humanness scoring, MHC class II epitope prediction and developability liability screening
What We Need From You
1Parental VH and VL sequences with the species of origin
2Antigen information and any structural or epitope mapping data
3Measured affinity and stability data for the parent, used as the benchmark
4The intended clinical context, which determines how much immunogenicity risk is acceptable
What You Receive
1Graded series of humanised sequences from conservative to fully germline-matched
2Humanness scores and predicted immunogenicity assessment per variant
3Structural models showing the graft and the rationale for each back-mutation
4Developability liability report for the designed sequences
5Recommended expression and testing order for the panel
What We Compute and Report
1Germline content and humanness score per designed variant
2Predicted T-cell epitope content across the sequence
3Structural assessment of each graft junction and supporting framework position
4Predicted stability and binding consequence for every back-mutation considered
5Developability liability map for the humanised sequences
Questions We Are Asked Before Scoping
Will humanisation reduce affinity?
▸Often somewhat, which is why designs are delivered as a graded series. Measuring several variants identifies where affinity is lost far more efficiently than iterating on one construct at a time.
Does a high humanness score mean low immunogenicity?
▸It correlates but does not guarantee. Clinical immunogenicity also depends on T-cell epitopes, aggregation, dose and patient population. We report both sequence and epitope-based assessments.
Can you humanise a VHH?
▸Yes, though camelid framework-2 hallmark residues require particular care because they govern solubility in the absence of a light chain. Designs treat those positions explicitly.
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 murine or camelid antibody is heading toward therapeutic development
Immunogenicity risk has to be documented for an internal or partner review
A previous humanisation attempt lost too much affinity
Consider another service when
The sequence is already human and needs affinity work
CDR Affinity Maturation The format itself is changing as well
Bispecific & Multispecific Antibody Design 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.
Framework selected on sequence identity while the loops need different support
Canonical class compatibility and Vernier zone identity drive selection, and a slightly less identical framework is often the better choice.
Back-mutations added one at a time across sequential rounds
A graded series is delivered so a single expression round locates the affinity cliff instead of three sequential ones.
A high humanness score presented as low clinical immunogenicity
Germline content and predicted T-cell epitope content are reported separately, and neither is described as predicting clinical anti-drug antibodies.
A Worked Example
Locating the humanness limit in one expression round
The situation
A murine antibody needed humanising with as little affinity loss as possible and limited expression capacity.
What the work involved
Frameworks were shortlisted on canonical compatibility, back-mutation candidates were ranked structurally, and twelve variants were designed spanning conservative through fully germline-matched.
What came back
The panel identified the point at which affinity dropped sharply — between two specific back-mutations — allowing the most human variant on the acceptable side to be chosen with measured evidence.
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