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Siglec-6 small-molecule binding

Computationally screen and dock candidate analogues against Siglec-6 to improve on the lab's existing weak (~75 µM) lead — promising hits get synthesised and validated wet-lab (SPR for K_d, CETSA in live cells).

  • Lead: Julien Lee Heberling, Huang Lab, Scripps Research
  • Theme: Virtual screening, immunology
  • Repo:

Team

Why this project

Siglec-6 (CD327, OB-BP1) is a poorly understood Siglec family member — expression is restricted to memory B cells, mast cells, and trophoblasts, and it’s upregulated in chronic lymphocytic leukaemia, acute myeloid leukaemia, and preeclampsia. The Huang lab has preliminary binding data on a small molecule that interacts with Siglec-6 but at a weak affinity (K_d ≈ 75 µM). The hackathon goal is to use computation to suggest analogues with markedly improved binding, so the wet-lab can prioritise synthesis and validation.

What a team could build

  • Set up an AutoDock Vina docking workflow against the AlphaFold model of Siglec-6.
  • Generate or curate a SMILES library of analogues around the current ~75 µM lead.
  • Score and rank candidates, then surface a short list of promising poses with rationale.

Minimum viable demo: a ranked list of analogue candidates with docking scores, pose visualisations, and a one-pager arguing why the top hits are worth synthesising.

Stretch directions

  • Pull a protein-language-model embedding for Siglec-6 (e.g. via OpenProteinSet on AWS) and use it as an additional feature for filtering candidates.
  • Generative analogue design (e.g. via fragment growing) rather than a static SMILES library.
  • Compare Vina against a free-energy or ML-based rescoring step.

Data

  • AlphaFold PDB of Siglec-6 (~50 kDa, unglycosylated).
  • SMILES strings of small-molecule candidates (~300–600 amu).

Pull the AlphaFold structure / UniProt annotations from UniProt on AWS, and the OpenProteinSet AWS mirror if teams want PLM embeddings as a representation-learning baseline.

Wet-lab follow-up

Promising hits are synthesised by the lab and validated:

  • SPR for K_d measurement.
  • CETSA for protein perturbation in live cells.

Skills you’ll build

Protein–ligand docking with AutoDock Vina; virtual screening from SMILES libraries; structure-based rational design of small-molecule analogues; reading docking poses against experimental binding-affinity data.