Three-dimensional structure determination lies at the heart of modern drug discovery, mechanistic biology, and protein engineering. Understanding how a protein folds, where substrates and inhibitors bind, and how multi-protein complexes assemble provides the molecular basis for rational therapeutic design.
ICDMO's structural biology platform spans the complete toolkit — from atomic-resolution X-ray crystallography and Cryo-EM through solution-state NMR, rapid SAXS characterization, and fully integrated structure-based drug design. All techniques are delivered under one roof, enabling seamless integrative structural projects.
Each technique provides unique, complementary information. Our scientists guide you to the right approach for your specific target, timeline, and scientific question.
ICDMO delivers the computational half of structural biology. Data collection is performed by your facility or a partner beamline; we process, interpret and translate the results into structures and design decisions.
NMR answers questions crystallography cannot: how a protein moves, which residues a weak fragment touches, and what an intrinsically disordered region is doing in solution. We take acquired spectra and carry out the interpretation — processing, assignment, chemical shift perturbation mapping, relaxation analysis and restraint-based structure calculation — and we state which conclusions the data actually supports.
Interpretation quality is bounded by the data: incomplete assignment, spectral overlap in larger proteins and exchange broadening all constrain what can be concluded. We report unresolved regions rather than filling them with assumption. Sample preparation and data acquisition are performed by your laboratory or a partner facility.
Small-angle scattering is the fastest way to learn the solution-state size, shape and oligomeric behaviour of a protein, and the easiest technique to over-interpret. We process scattering curves, assess data quality honestly, derive the structural parameters that are genuinely supported, and fit atomic models or ensembles against the data where the system justifies it.
SAXS is low resolution and exquisitely sensitive to aggregation and polydispersity. A shape envelope derived from a poor sample is a confident-looking artefact, so data quality assessment comes before interpretation and we will report a dataset as uninterpretable when it is. Measurement is performed by your facility or a partner beamline.
Structure only creates value when it changes what the chemistry team makes next. Our SBDD service runs as a design loop: we analyse the structure and the observed SAR, design and prioritise compounds, apply free energy methods where a synthesis decision warrants the cost, and re-analyse once assay and structural results return. Cycles typically run two to four weeks.
Predictions prioritise hypotheses; they do not establish potency, selectivity or developability. Synthesis and assays are performed by your team or a qualified partner. Where a model repeatedly fails to explain observed SAR, we report that and diagnose the cause rather than retuning until the numbers agree.
Confirm druggability; identify binding pockets, allosteric sites and conformational flexibility for rational drug design.
Crystallographic screening of 500–1,000 fragment libraries to identify novel chemical starting points for your programme.
Iterative co-crystal structures guide SAR: visualize binding modes, optimize key interactions and improve selectivity.
Capture substrate, transition-state and product complexes to understand catalytic mechanisms and allosteric regulation.
Rational enzyme variant design, antibody humanization, and protein–protein interface engineering guided by atomic structure.
High-quality structures for IND applications, biosimilar comparability studies, and patent binding-mode novelty filings.
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.
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 ↗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 ↗The resolution and quality of the data set a hard ceiling on what can be concluded, and no processing recovers information the experiment did not capture. Regions with weak density, incomplete assignment or poor scattering are reported as unresolved rather than modelled with false confidence. Sample preparation and data collection are performed by your laboratory or a partner facility; ICDMO delivers the computational and interpretive work.
Note: Timelines are estimates for well-behaved samples. Challenging targets may require additional optimization. Contact us for a free feasibility assessment before project initiation.
Share your target details and our structural biologists will recommend the best technique and provide a no-obligation project plan within 24 hours.
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