Ref K6
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Membrane proteins carry a large share of drug targets and a disproportionate share of the technical difficulty. Design and engineering must account for the lipid environment, the packing of transmembrane helices, the conformational cycling that transporters and channels depend on, and the practical problem of expressing and purifying the result. We model these systems in realistic bilayers and design for stability, conformational state or function while keeping the folding and expression constraints in view.
Key Features & Advantages
Realistic Bilayer Environment
Systems are modelled in lipid compositions matched to the relevant membrane, since transmembrane behaviour depends on the bilayer and not only on the protein.
Helix Packing Designed Explicitly
Transmembrane helix interfaces and packing motifs are analysed and designed, as they govern both fold stability and oligomeric assembly.
Lipid-Facing Surfaces Treated Separately
Residues facing lipid follow different design rules from those facing water or the protein core, and designs respect that distinction rather than applying uniform criteria.
Conformational States Modelled
For transporters, channels and receptors, multiple functional states are modelled so that designs can stabilise the state the project requires.
Where This Service Is Used
1Thermostabilising a GPCR or transporter for structural studies or assay development
2Designing constructs that trap a specific conformational state
3Engineering improved expression and detergent or nanodisc stability
4Modelling and designing ion channel gating behaviour
5Designing de novo transmembrane assemblies such as pores or scaffolds
Methods and Techniques Applied
1Membrane topology determination and structure prediction or modelling in a bilayer context
2Bilayer system construction with appropriate lipid composition and equilibration
3Molecular dynamics to characterise helix packing, lipid interactions and conformational behaviour
4Stability-directed mutational scanning with membrane-aware scoring functions
5State-selective design targeting active, inactive or intermediate conformations
6Expression and construct design: truncations, fusion partners, thermostabilising mutation sets
What We Need From You
1Sequence and, where available, structure or related family structures
2The functional state of interest and any ligands, lipids or ions involved
3Expression and purification history, including where the current construct fails
4The objective: stability, state trapping, expression, or de novo design
What You Receive
1Designed sequences or construct definitions with the rationale for each change
2Membrane-embedded structural models with lipid interaction analysis
3Simulation results characterising stability and conformational behaviour
4Ranked mutation sets for thermostabilisation or state selection
5Construct design recommendations for expression and purification trials
What We Compute and Report
1Membrane-embedded models with lipid contact and helix packing analysis
2Predicted stability change per substitution using membrane-aware scoring
3Conformational state populations observed across simulation replicates
4Construct-level assessment: boundaries, fusion partners and predicted expression risk
5Simulation metrics for bilayer insertion depth, tilt and interaction persistence
Questions We Are Asked Before Scoping
Why are membrane proteins harder?
▸Structures are scarcer, prediction accuracy is lower for the loops and termini that matter, the lipid environment affects everything, and expression and purification fail more often. We plan projects with that attrition assumed rather than discovered.
Can you improve expression?
▸We can design constructs, truncations, fusions and thermostabilising mutations that frequently help, but membrane protein expression remains substantially empirical, and several constructs should be tested in parallel.
Is de novo membrane protein design realistic?
▸For simple transmembrane bundles and pores it has been demonstrated. For complex transporters and receptors with functional cycling it remains a research problem, and we would scope it as such.
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 receptor, transporter or channel must be stabilised for structural or assay work
A specific conformational state needs trapping
Expression or detergent stability is blocking the programme
Consider another service when
The protein is soluble
Protein Stability & Thermostability Engineering The question is ligand binding at a known site
Molecular Docking 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.
Modelling in water a protein that lives in a bilayer
Systems are built in lipid compositions matched to the relevant membrane, because transmembrane behaviour depends on the bilayer.
Design rules from soluble proteins applied to lipid-facing residues
Lipid-facing, core-facing and water-facing positions are treated under different criteria.
Endogenous receptor activity in the host confusing the assay
Host background expression is profiled as part of the construct and assay recommendation.
A Worked Example
Thermostabilising a receptor for structural work
The situation
The receptor was too unstable in detergent to progress to structure determination.
What the work involved
The receptor was modelled and simulated in a matched bilayer, helix packing and lipid interactions were analysed, and stability scanning was run with membrane-aware scoring while conformational state was tracked.
What came back
Twenty-four candidate mutations ranked by predicted stabilisation, annotated with which conformational state each was expected to favour, plus construct boundary and fusion recommendations.
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.
Machine-learning-guided evolution raised a reaction yield from 12% to 93%
An active-learning workflow optimising five interacting active-site residues improved the yield of a desired non-native cyclopropanation product from 12% to 93% in three rounds of laboratory experimentation. The design principle is the one we apply: computation chooses which small set of variants to make, and measured results retrain the model between rounds.
Nature Communications, 2025 ↗1.6- to 42-fold activity improvements from model-selected enzyme variants
A machine-learning-guided enzyme engineering campaign evaluated 1,217 variants across 10,953 reactions to build predictive models for amide synthetases. Model-selected variants showed 1.6- to 42-fold improved activity relative to the parent enzyme across nine pharmaceutical targets, illustrating both the scale of data that makes models predictive and the spread of outcomes that remains.
Nature Communications, 2024 ↗De novo binder design succeeds at rates that vary enormously by target
Recent design methods report nanomolar binder hit rates between roughly 17% and 82% across evaluated targets, while independent evaluations of earlier pipelines on other targets report designs failing on expression, non-specific binding or undetectable affinity. The honest reading is that de novo binder design now works, that success is strongly target-dependent, and that panels must be sized accordingly rather than on a best-case figure.
Comparative design studies, bioRxiv, 2025 ↗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