synth-saxs 0.1.3

Education-focused SAXS profile simulation from protein coordinates

synth-saxs

synth-saxs is a lightweight Python library for simulating Small-Angle X-ray Scattering (SAXS) profiles from protein coordinates.

Extracted from the synth-pdb ecosystem, it provides a physically grounded, education-focused engine for reciprocal space simulation.

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🧪 For Structural Biologists

• Guinier & Kratky Analysis: Built-in plots to determine Radius of Gyration ($R_g$), forward scattering $I(0)$, and assess protein compactness.
• Hydration Modeling: Physically accurate solvent displacement model based on Pavlov & Svergun (1997).

🤖 For Machine Learning Researchers

• Debye Back-Calculation: O(N²) scattering intensity from atomic coordinates, suitable as a differentiable-style loss signal for structure validation.
• Educational Clarity: Explicit, well-commented implementation of form factors and solvent contrast — easy to audit and extend.

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Features

• Debye Formula: O(N²) calculation of scattering intensity.
• Solvent Displacement: Physically accurate solvent contrast model based on Pavlov & Svergun (1997).
• Atomic Form Factors: Standard Waasmaier & Kirfel (1995) coefficients.
• Visualization: Built-in support for Kratky and Guinier plots.

Installation

# Basic installation
pip install synth-saxs

# Installation with visualization support
pip install "synth-saxs[viz]"

Command-Line Interface (CLI)

synth-saxs provides a CLI for rapid simulation and plotting:

# Basic simulation
synth-saxs protein.pdb --output profile.dat

# Plotting with Kratky analysis
synth-saxs protein.pdb --plot report.png --plot-type kratky

# Advanced modeling (Hydration Shell + P(r) distribution)
synth-saxs protein.pdb --shell-density 0.03 --p-dist pr.png --p-dist-dat pr.dat

CLI Arguments

• input: Path to PDB/mmCIF file.
• --output: Save $I(q)$ data to a .dat file.
• --plot: Path to save a SAXS report image.
• --shell-density: Excess hydration shell density (default: 0.0).
• --p-dist: Save a plot of the $P(r)$ distribution.
• --p-dist-dat: Save raw $P(r)$ data to a .dat file.

Quick Start

1. Single Structure Simulation

import biotite.structure.io.pdb as pdb_io
from synth_saxs import calculate_saxs_profile, add_noise

# Load a structure
struct = pdb_io.PDBFile.read("protein.pdb").get_structure(model=1)

# Calculate I(q) and add realistic noise
q, I = calculate_saxs_profile(struct)
I_noisy = add_noise(I, noise_level=0.02)

2. Ensemble Averaging

The SaxsSimulator can handle both Biotite stacks and standard Python lists of structures.

from synth_saxs import SaxsSimulator

# List of different conformation models
models = [model1, model2, model3]

sim = SaxsSimulator(q_max=0.3)
avg_intensity = sim.simulate(models)

3. Pair Distance Distribution P(r)

from synth_saxs import calculate_p_dist, plot_p_dist

r, pr = calculate_p_dist(struct)
plot_p_dist(r, pr, output_path="p_dist.png")

4. Visualization

from synth_saxs import plot_saxs_results
plot_saxs_results(q, I, plot_type="all", output_path="saxs_report.png")

Scientific Rationale

The engine is designed for numerical stability and educational clarity. It correctly handles the delicate balance between atomic contrast and solvent displacement decay to ensure monotonic scattering curves in the Guinier regime.

References

• Waasmaier, D. & Kirfel, A. (1995). Acta Cryst. A51, 416-431.
• Pavlov, M.Y. & Svergun, D.I. (1997). J. Appl. Cryst. 30, 712-717.
• Svergun, D., et al. (1995). J. Appl. Cryst. 28, 768-773.

Related Projects

This library is part of the synth-pdb ecosystem for synthetic biophysics data generation:

• synth-pdb — Core protein structure generator (CLI + API)
• synth-nmr — NMR observables simulator
• synth-cryo-em — Cryo-EM density map simulator
• synth-dynamics — ANM/Langevin dynamics engine
• diff-biophys — Differentiable JAX biophysics kernels
• diff-ensemble — IDP structural ensemble generator

Contributing

Contributions are welcome! Please open an issue or pull request on GitHub. The project uses ruff for linting/formatting and mypy for type checking — run pre-commit run --all-files before submitting.

License

MIT License — see LICENSE for details.

Citation

If you use synth-saxs in your research, please cite:

@software{synth_saxs,
  author  = {Elkins, George},
  title   = {synth-saxs: SAXS profile simulation from protein coordinates},
  year    = {2026},
  url     = {https://github.com/elkins/synth-saxs},
  version = {0.1.0}
}