Structure & Docking#
Tutorials for the omicverse.mol module — the bridge from an omics target
protein to its 3D structure and drug context.
A typical omicverse analysis ends with a target: a differential gene from
ov.bulk, a marker or driver from ov.single, a variant from ov.genetics.
The natural next questions are structural and pharmacological — what does the
protein look like in 3D, where are its confident regions, does it have a
druggable pocket, are there known drugs against it, and can a candidate
molecule bind it? ov.mol answers them, with interactive 3D
visualization (py3Dmol) that renders inline in Jupyter and persists in the
exported HTML docs.
The three notebooks follow the natural arc see the target → assess it → test a drug, and each one follows the analysis workflow the field actually uses — with the discipline (model-confidence assessment, redocking validation) a structural biologist or computational chemist would apply. All three run one real target, EGFR, framed as a hit handed over by an upstream omics analysis.
Notebook |
What you learn |
|---|---|
Structure |
Fetch / predict and interactively visualize a target’s structure — and assess model confidence (pLDDT, PAE) before trusting it. |
Druggability |
Detect binding pockets, score druggability, and check what drugs are already known — structure-based target prioritization. |
Docking |
Validate a docking protocol by redocking, then dock a candidate molecule and inspect the binding pose. |
Installation#
The structural-biology stack is optional. The core layer — structure acquisition, interactive visualization and known-drug lookup:
pip install 'omicverse[mol]'
The docking layer (AutoDock Vina + receptor / ligand preparation):
pip install 'omicverse[mol-dock]'
Binding-pocket detection uses rust-fpocket, a pip-installable Rust port of
fpocket:
pip install fpocket-rs