afrc – the Analytical Flory Random Coil

Version 0.4.3 (released September 2026)

afrc is a Python package that computes the polymer properties of an unfolded polypeptide using an analytical implementation of the Flory Random Coil (the AFRC).

The AFRC reproduces the dimensions of a polypeptide in a theta solvent: an ideal chain with a scaling exponent of exactly 0.5 and no finite-size effects. It is a reference model, not a predictor. It tells you how an IDR of a given sequence would behave if chain-chain and chain-solvent interactions exactly cancelled out, which makes it a useful null model for simulations and experiments alike.

The model is pre-parameterized against numerical simulations, so the only input is an amino acid sequence. From that, the AFRC instantly returns:

  1. The mean end-to-end distance and its distribution.

  2. The mean radius of gyration and its distribution.

  3. The mean hydrodynamic radius.

  4. Every inter-residue mean distance and distance distribution (distance maps and internal scaling profiles).

  5. Inter-residue contact fractions (contact maps).

  6. Expected paramagnetic relaxation enhancement (PRE) profiles.

  7. Explicit 3D ensembles (one bead per residue), written as PDB/XTC files - from Python or with the afrc-ensemble command-line tool, which also checks the ensemble against the model.

The package also implements several other analytical polymer models: the worm-like chain [Zhou2004] [Brien2009], the self-avoiding walk [Brien2009], a SAW with a tunable scaling exponent [Zheng2018], the freely jointed chain and the freely rotating chain. They share a common interface with the AFRC - including generating 3D ensembles - so one sequence can be compared against several reference models. See Polymer Models (Theory) for the theory behind each and Polymer Models (Application) for usage.

Contents:

Indices and tables

References

[Brien2009] (1,2)

O’Brien, E. P., Morrison, G., Brooks, B. R., & Thirumalai, D. (2009). How accurate are polymer models in the analysis of Forster resonance energy transfer experiments on proteins? The Journal of Chemical Physics, 130(12), 124903.

[Zheng2018]

Zheng, W., Zerze, G. H., Borgia, A., Mittal, J., Schuler, B., & Best, R. B. (2018). Inferring properties of disordered chains from FRET transfer efficiencies. The Journal of Chemical Physics, 148(12), 123329.