prothon compare --ensembles wild_type.dcd mutant.dcd --topology topology.pdb

CBCN (reference: ensemble 0)
  ensemble 1: d = 0.2841 (floor 0.0472) — 34/76 residues differ

Two numbers, and the second is the one that decides whether the first means anything. Split one ensemble in half at random and compare the halves: the answer is not zero, because a finite sample never reproduces a continuous distribution exactly. That self-distance is the noise floor — the smallest difference this much sampling can resolve.

Here 0.2841 clears a floor of 0.0472 by six times. At 0.05 it would not have, and Prothon would say so rather than report a small difference.

Install

conda create -n prothon -c conda-forge prothon
conda activate prothon

prothon info lists what is installed and what version:

prothon info

What you can compare

It withholds rather than overstates. Trajectory frames are not independent draws, and a test that assumes they are calls 99% of residues different when nothing differs. Prothon estimates the correlation time from your data, permutes contiguous blocks of it, and holds a false-positive rate between 1.7% and 2.3% across correlation times spanning a factor of fifty. Where a trajectory holds too few independent blocks to build a null from, it reports the floor and prints no p-value at all. Every default is a measurement, not a choice.

One import, and everything is reachable from it

from prothon import Prothon

prothon = Prothon(["wild_type.dcd", "mutant.dcd"], "topology.pdb", "cbcn",
                  random_state=0)

prothon.compare()                       # where do they differ
prothon.distinguishability()            # differences *between* residues
prothon.coverage_and_fidelity()         # missed states, or invented ones
prothon.rank()                          # several against a reference
prothon.validate("rg", [2.71], [0.08])  # against experiment
prothon.save_config()                   # writes prothon.yml

The order parameter is a property of the study, so it is named once. Any method takes one to override it for a single call.

Three ways to run the same study, and none is a subset of another — flags, file and API are generated from one schema:

Any command line can be written out as a file with --save-config, and any file can be run from the command line.

Documentation

Start here — Install · Your first comparison · Order parameters · Worked examples

Going further — The statistics · Different molecules · Distances · Scoring against experiment · Ranking several ensembles

Measurements — Calibration · Circular parameters · Convergence · Ubiquitin · Performance

Reference — CLI · Configuration · Python API

Citation

Aina, A.; Hsueh, S. C. C.; Plotkin, S. S. PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles. J. Chem. Inf. Model. 2023, 63, 3453–3461. DOI: 10.1021/acs.jcim.3c00145

@article{aina2023prothon,
  author  = {Aina, Adekunle and Hsueh, Shawn C. C. and Plotkin, Steven S.},
  title   = {PROTHON: A Local Order Parameter-Based Method for Efficient
             Comparison of Protein Ensembles},
  journal = {Journal of Chemical Information and Modeling},
  volume  = {63},
  number  = {11},
  pages   = {3453--3461},
  year    = {2023},
  doi     = {10.1021/acs.jcim.3c00145},
}

Contributing

Contributions welcome — see CONTRIBUTING.md.

License

MIT. See LICENSE.