FP Analyzer

An interactive Jupyter notebook for analyzing fluorescence polarization (FP) saturation binding experiments. Reads raw plate-reader output, applies instrument corrections (G-factor, blank subtraction, Q-factor), fits direct binding models, computes a Z′-factor for assay quality, and exports publication-ready tables and figures.

Quick start

Option A — try it online (no install). Click the Binder badge above. The first launch takes 1–3 min while the environment is built (subsequent launches are cached and start in seconds). The example data files are already in the examples/ folder of the Binder session.

Option B — run locally.

git clone https://github.com/intbio/fp_analyzer
cd REPO
pip install -r requirements.txt
jupyter notebook notebooks/fp_analyzer.ipynb

In either case, run the cells from top to bottom. The notebook is laid out as six linear steps: Setup → Load data → QC → Fit → Summary → Export. Each step has a short instruction block followed by a code cell that displays a widget panel.

What it does

• Parses a layout file (CSV/XLSX) and a data file (custom CSV or native BMG Labtech FP export).
• Computes the instrument G-factor from dedicated calibration wells with free fluorophore of known reference anisotropy.
• Subtracts buffer blanks and computes per-well anisotropy r from raw parallel and perpendicular intensities.
• Estimates the Q-factor (bound/free total fluorescence ratio) per condition from the titration extremes.
• Aggregates titration replicates into a saturation curve.
• Fits one of three direct-binding models:
  • fp_quadratic (default) — exact closed-form solution accounting for ligand depletion (Roehrl et al. 2004; Jarmoskaite et al. 2020).
  • one_site — simple hyperbolic, valid only when [L*] ≪ K_D.
  • hill — phenomenological Hill model with coefficient n_H.
• Reports K_D with confidence intervals and reduced χ².
• Computes Z′-factor (Zhang 1999) per condition for assay quality.
• Generates a Scatchard plot as a visual diagnostic.
• Bundles results into a downloadable ZIP (CSV tables + PNG figures).

What it does not do

• Does not fit competitive displacement experiments (only direct binding).
• Does not model non-specific binding explicitly — visual diagnosis via the Scatchard plot only.
• Does not verify that the binding reaction has reached equilibrium — this must be established by the experimenter prior to data collection (see Jarmoskaite et al. 2020 for guidance).

Repository layout

fp-analyzer/
├── README.md                       # this file
├── USER_GUIDE.md                   # full user guide with theory
├── requirements.txt                # Python dependencies (used by Binder)
├── notebooks/
│   └── fp_analyzer.ipynb           # the interactive notebook
├── examples/
│   ├── example_layout.csv          # synthetic 384-well layout
│   ├── example_data.csv            # raw intensities, custom CSV format
│   ├── example_data_bmg.csv        # same data in BMG Labtech export format
│   └── _generate.py                # script that built the synthetic data
└── docs/
    └── methods.docx                # ready-to-paste Methods section for a paper

Data format requirements

See USER_GUIDE.md §3 for the complete specification of the layout and data file formats, including a worked example.

In short:

• Layout file — CSV/XLSX with columns well, condition, sample_type, concentration, replicate. Each well is one of four sample types: titration, substrate, blank, fluorophore.
• Data file — either a custom CSV with columns well, parallel, perpendicular, or a native BMG Labtech FP export (autodetected from Raw Data (parallel) / Raw Data (perpendicular) block headers).

Try the synthetic example in examples/ first to see the expected structure (two conditions, K_D = 25 and 250 nM, full QC controls on a 384-well plate).

Theory

A short theoretical primer covering FP basics (Perrin equation, G-factor, anisotropy from raw intensities, Q-factor mixing, choice of binding model, Z′-factor) is in USER_GUIDE.md §2. A condensed Methods section ready for inclusion in a paper is in docs/methods.docx.

Key references:

• Lakowicz JR (2006). Principles of Fluorescence Spectroscopy, 3rd ed. Springer.
• Tranter GE (2010). Fluorescence polarization and anisotropy. In Encyclopedia of Spectroscopy and Spectrometry, 2nd ed., 625–627.
• Jameson DM, Croney JC (2003). Comb. Chem. High Throughput Screen. 6, 167.
• Moerke NJ (2009). Curr. Protoc. Chem. Biol. 1, 1.
• Roehrl MHA, Wang JY, Wagner G (2004). Biochemistry 43, 16056.
• Jarmoskaite I, AlSadhan I, Vaidyanathan PP, Herschlag D (2020). eLife 9, e57264.
• Zhang JH, Chung TDY, Oldenburg KR (1999). J. Biomol. Screen. 4, 67.
• Newville M, Stensitzki T, Allen DB, Ingargiola A (2014). LMFIT. doi:10.5281/zenodo.11813.

License

MIT License

Copyright (c) 2026 Integrative Biology Group

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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