Ref G3
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Allosteric sites offer what orthosteric sites often cannot: selectivity between close family members, modulation rather than blockade, and activity against targets whose active sites resist drug-like chemistry. They are also harder to find and harder to validate. We identify candidate allosteric pockets through simulation and network analysis, trace the communication pathway that connects a site to the functional centre, and design modulators against sites where the mechanistic case is supportable.
Key Features & Advantages
Sites Found Through Dynamics
Allosteric and cryptic pockets frequently appear only in simulation, so detection is driven by molecular dynamics and mixed-solvent mapping rather than by static structure inspection alone.
Communication Pathways Traced
We map how perturbation at the candidate site propagates to the functional site, which distinguishes a genuine allosteric mechanism from an unrelated surface pocket.
Selectivity As The Design Driver
Allosteric sites are typically less conserved than active sites, and we quantify that divergence across the family to show where selectivity can realistically be won.
Direction Of Effect Considered
Designs are assessed for whether they stabilise the active or inactive state, since activators and inhibitors demand different structural strategies.
Where This Service Is Used
1Achieving selectivity within a highly conserved target family such as kinases or phosphatases
2Finding a tractable site when the orthosteric pocket is shallow, charged or undruggable
3Designing activators or positive modulators, which orthosteric approaches rarely deliver
4Overcoming resistance mutations located in the orthosteric site
5Modulating rather than abolishing function where complete inhibition carries toxicity risk
Methods and Techniques Applied
1Extended molecular dynamics with pocket-opening and mixed-solvent analysis to expose transient sites
2Allosteric site prediction using structural, dynamic and coevolutionary signals in combination
3Communication pathway analysis through dynamic network and correlated motion methods
4Conservation analysis across the family to assess selectivity potential at each candidate site
5Virtual screening and structure-based design against validated allosteric pockets
6Simulation of designed complexes to test conformational effect and the direction of modulation
What We Need From You
1Target structure, ideally in more than one conformational state
2Functional data indicating which states are active and any known allosteric effectors
3Family members against which selectivity is required
4Whether inhibition, activation or fine modulation is the therapeutic goal
What You Receive
1Ranked allosteric site candidates with dynamic and structural evidence for each
2Communication pathway analysis linking each candidate site to the functional centre
3Selectivity assessment across the family at each site
4Designed or screened modulator candidates with poses and predicted profiles
5Mechanistic hypothesis for each design, with the experiment that would test it
What We Compute and Report
1Pocket opening frequency and occupancy for transient sites detected in simulation
2Communication pathway analysis linking candidate sites to the functional centre
3Conservation divergence between the target and family members at each candidate site
4Predicted conformational effect of each designed modulator, with direction of modulation
5Binding poses and interaction analysis for designed or screened allosteric ligands
Questions We Are Asked Before Scoping
How do you know a predicted site is genuinely allosteric?
▸We do not know it computationally. Pathway analysis and simulation build a mechanistic case; confirmation requires mutagenesis or a modulator that demonstrably alters function, and we design those experiments with you.
Are allosteric programmes riskier?
▸Generally yes. Site validation is harder, hit rates are lower and mechanisms are more complex. The compensating advantages are selectivity and modulation, which is why the trade-off is worth stating explicitly at the outset.
Can you find allosteric sites without a structure?
▸A structure or a reliable model is required. Sequence-based coevolution analysis can suggest coupled residues, but designing a modulator needs three-dimensional information.
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
Selectivity within a conserved family cannot be achieved at the active site
Modulation rather than complete inhibition is the therapeutic goal
Resistance mutations in the orthosteric site have undermined an inhibitor series
Consider another service when
The orthosteric site is tractable and selectivity is achievable there
High-Throughput Virtual Screening You only need to know whether any pocket exists
Binding Site & Druggability Assessment 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.
A surface pocket mistaken for an allosteric site
A communication pathway to the functional centre must be demonstrable before a site is recommended for design.
Transient pockets reported from insufficient sampling
Opening frequency and occupancy are quantified across replicates, and single-trajectory observations are labelled as such.
Designing a modulator without deciding which state to stabilise
Direction of modulation is fixed at the design stage, because activators and inhibitors require different structural strategies.
A Worked Example
A selective site on a conserved kinase family member
The situation
Every compound the team made inhibited four family members equally.
What the work involved
Multi-microsecond simulation with mixed-solvent mapping exposed transient pockets, dynamic network analysis traced coupling to the active site, and family conservation was computed per candidate site.
What came back
One pocket coupled to the activation loop and divergent at five positions across the family. Virtual screening against it produced a starting series with measurable selectivity in the first assay round.
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.
Structure prediction now exceeds classical docking on independent benchmarks
On a held-out protein–ligand benchmark, deep-learning complex prediction placed roughly three quarters of ligands within 2 Å of the crystallographic pose, outperforming established docking programs on the same set. For design programmes this changes what can be attempted before a co-crystal structure exists — while leaving binding-mode certainty, and therefore the most consequential chemistry decisions, dependent on experimental structures.
Abramson et al., Nature, 2024 ↗Free energy methods can resolve the differences that decide a synthesis
Published benchmarks on congeneric series report errors near 1 kcal/mol, sufficient to rank analogues that differ by around tenfold in potency. In an integrated design loop this is the calculation reserved for decisions where making the wrong compound costs more than the computation.
Protocol and benchmark studies, Scientific Reports, 2019 ↗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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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