Ref N4
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Peptide libraries are cheap to diversify and easy to design badly. We design combinatorial and AI-directed libraries for phage display, mRNA display and chemical synthesis, choosing length, randomisation scheme and cyclisation strategy for the platform and the target, and checking coverage of the sequence space you intend to sample. Where a target structure or known binders exist, the library is biased toward plausible binding chemistry instead of sampling uniformly.
Key Features & Advantages
Designed For The Platform
Phage, mRNA and chemically synthesised libraries have different diversity ceilings, length limits and chemistry options, and the design follows the platform rather than a generic template.
Coverage Calculated, Not Assumed
We compute what fraction of the intended sequence space the library actually samples at your achievable diversity, which is frequently far less than expected.
Structure- Or Data-Biased Diversity
Where a target structure or a set of known binders exists, generative and pharmacophore-guided methods bias the library toward chemistry with a plausible route to binding.
Positional Scanning Designed In
Scanning and deconvolution strategies are built into the design so that selection results can be interpreted residue by residue.
Where This Service Is Used
1Designing a display library for a new target with no known peptide binders
2Building focused libraries around a confirmed peptide lead for affinity maturation
3Designing cyclic or constrained peptide libraries for display selection
4Creating positional scanning sets to map the contribution of each residue
5Designing synthetic peptide arrays for epitope mapping or specificity profiling
Methods and Techniques Applied
1Platform assessment: diversity ceiling, length limits, chemistry compatibility and selection strategy
2Length and randomisation scheme design with codon selection for genetically encoded libraries
3Sequence space coverage calculation at the realistically achievable diversity
4Structure-guided or generative biasing where target information exists
5Cyclisation and constraint strategy design for conformationally restricted libraries
6Liability filtering to limit aggregation-prone, oxidation-prone and protease-labile sequences
What We Need From You
1Display or synthesis platform and its practical diversity limit
2Target structure, epitope or known binding peptides where available
3Required peptide length, chemistry and any constraint or cyclisation strategy
4Downstream selection or screening plan, since it affects how the library should be structured
What You Receive
1Library design with length, randomisation scheme and codon specification
2Coverage analysis against the intended sequence space
3Oligonucleotide or synthesis specifications ready for construction
4Predicted property distribution for the library, including liability statistics
5Selection and deconvolution strategy recommendations
What We Compute and Report
1Sequence space coverage at the diversity your platform can actually achieve
2Predicted property distribution across the library, including charge and hydrophobicity
3Liability frequency statistics: oxidation, aggregation and protease-labile motifs
4Positional composition tables for scanning and deconvolution designs
5Codon or synthesis specifications with the resulting amino acid frequencies
Questions We Are Asked Before Scoping
Random or biased library?
▸Random libraries sample broadly and suit novel targets; biased libraries concentrate diversity where binding is plausible and suit targets with structural information or known binders. Many programmes run both.
How much of the sequence space will we cover?
▸For anything beyond about seven random residues, only a vanishing fraction. We calculate the number for your design so expectations and selection strategy are set realistically.
Can you design cyclic peptide libraries?
▸Yes, including disulfide, head-to-tail and chemically cyclised formats, with the constraint chemistry matched to the display platform you are using.
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 display or synthetic peptide library is being designed for a new target
Cyclic or constrained peptide libraries are required
Positional scanning is needed to map residue contributions
Consider another service when
Specific sequences should be designed instead of selected
Linear Therapeutic Peptide Design The library is antibody-based
Antibody Library 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.
Sequence space coverage assumed rather than calculated
The fraction of the intended space the library actually samples is computed at your achievable diversity.
Library chemistry incompatible with the display platform
Length limits, cyclisation chemistry and encoding constraints are matched to the platform before the design is issued.
Aggregation-prone or oxidation-prone sequences dominating the output
Liability filtering is applied at the design stage so the selected pool is developable.
A Worked Example
A cyclic library sized to the platform
The situation
The group wanted a constrained peptide library but had not established what diversity their platform could realise.
What the work involved
Platform diversity limits were established, length and randomisation schemes were designed around the cyclisation chemistry, coverage was computed, and liability filtering was applied to the composition.
What came back
A design sampling a stated fraction of the intended space, with codon specifications, expected property distribution, and a deconvolution strategy for interpreting the selection output.
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.
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