RNA function is shaped by structure: a single strand can form helices, loops, pseudoknots, long-range contacts, and alternative states that control recognition and activity. We build structural ensembles and interaction hypotheses that help teams choose binding sites, partners, designs, and validation experiments.
Predict base-pairing, helices, loops, and pseudoknots from sequence and chemical-probing data.
Build atomistic 3D models using template-based, fragment-assembly, and deep-learning folding.
Model and rank RNA–protein complexes, from RBP motifs to ribonucleoprotein assembly.
Dock ligands and map pockets for riboswitch and RNA pocket-targeting programs.
Run MD simulations to test conformational stability and score interaction hypotheses.
These four related pages are listed in the provided website outline and connect structural modeling with a more focused docking or design objective.
Predict target-site accessibility along the transcript and model duplex geometry so your sequence engages the intended site with minimal off-target pairing.
Map the ligand pocket of a stable 3D fold and screen small molecules that bind the functional conformation rather than the unfolded ensemble.
Model how a long non-coding RNA scaffolds protein complexes and identify the interfaces where disruption could block a disease-relevant pathway.
The cycle moves from a defined biological state to a ranked ensemble, interaction hypothesis, and validation plan. New experimental evidence can be incorporated at each checkpoint.
Request a Project ScopeDefine the RNA, its functional state, and the specific decision the model must support.
Fold the RNA and validate against probing data, cryo-EM, or available PDB templates.
Dock protein or small-molecule partners and rank poses by physics-based scoring.
Run MD simulations to test stability, conformational changes, and interaction persistence.
Hand over structures, interactions, and a validation plan, then fold in new data for the next round.
RNA can occupy alternative folds and interaction states. We combine thermodynamic, deep-learning, template, docking, and experimental-constraint evidence, then preserve plausible alternatives when the data do not justify one definitive model.
A target-accessibility map, RNA–protein interface, and ligand-bound conformational study need different methods and sampling. Scope follows the decision, not a fixed package.
The most useful starting point is a well-defined construct and functional state, even if experimental structure data are limited.
We check whether the data support one fold, several states, or only local structural conclusions, and we report confidence at the region and interaction level.
The project answer, confidence level, key assumptions, and recommended next action in plain language.
Base-pair maps, coordinate files, alternative folds, construct notes, and region-level confidence.
RNA–protein or RNA–small molecule poses with interface contacts, pocket geometry, and evidence.
Conformational sampling, interaction persistence, clustering, and limitations when included in scope.
Annotated secondary structures, 3D views, interaction maps, and confidence overlays.
Target-site, partner, ligand, or construct recommendations linked to discriminating experiments.
The authors introduced a geometric deep-learning approach that scores RNA 3D structures with accuracy approaching experimentally derived maps, showing that learned energy landscapes can guide folding where thermodynamic models struggle.
The authors demonstrated end-to-end prediction of biomolecular complexes—including protein–RNA interfaces—from sequence, showing that joint structure prediction can recover interaction geometry without separate docking pipelines.
These answers clarify the evidence, inputs, uncertainty, and scope behind a decision-ready RNA modeling project.
Accuracy depends on RNA length, topology, conformational state, homologous structures, and experimental constraints. We report model ensembles and region-level confidence rather than presenting one predicted structure as certain.
Yes. Structural modeling can identify accessible target regions and assess local duplex geometry. These results complement sequence-level efficacy, off-target, chemistry, delivery, and experimental screening rather than replacing them.
No. Modeling can begin from sequence, but SHAPE or DMS probing, cryo-EM density, mutational data, known base pairs, and homologous structures can narrow the ensemble and increase confidence.
Yes. We can model RNA–protein complexes and RNA–small molecule binding, then compare poses, contacts, pocket geometry, and stability according to the available structural evidence.
Useful inputs include RNA sequence and construct boundaries, species and isoform, functional state, binding partner, modifications, available probing or structural data, and the biological or design decision the model must support.
We retain plausible secondary and tertiary structure ensembles when the data do not support one state, compare interaction hypotheses across states, and recommend experiments that can distinguish them.
Scope depends on RNA size and complexity, the number of states and partners, available constraints, required sampling, and the decision timeline. A milestone plan is provided before work begins.
Share the construct, biological state, interaction question, and available evidence. CD ComputaBio will propose a fit-for-purpose modeling scope with defined inputs, milestones, deliverables, and confidence reporting.
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