# modelfree-protein15n Model-Free analysis framework for protein backbone amide 15N NMR spin relaxation rates.
This tool fits the relaxation data to a multi-Lorentzian spectral density function. One can choose the number of Lorentzians (dynamic modes) for the fit. Typically, one can perform 1, 2, and 3 dynamic mode MF analysis and see which model is most relevant for the data. IMPACT analysis is also possible by fixing the correlation times and fitting the amplitudes.
## Relevant litterature
Lipari & Szabo, Journal of the American Chemical Society (1982); Halle, The Journal of chemical physics (2009); Khan et al., Biophysical journal (2015)
# installation
$ pip install modelfree-protein15n
# usage
The program is able to generate random relaxation data with the command ‘modfree generate’. The program is able to fit relaxation data to a model with the command ‘modfree fit’. The program is able to plot the results with the command ‘modfree plot’.
## Data generation
To generate relaxation data, type the following command:
$ modfree generate
The following flags are available: -o (str): Output directory containing the generated data. -modes (int): number of dynamic modes used to generate the data -n (int): number of residues in the data -noise (float): between 0 and 1, indicates the proportion of noise to put in the data. (0.03 by default) -fields (list, int or float): Magnetic fields used for the rate generation in MHz. -rates: relaxation rates to generate. by default R1, R2, NOE, etaXY.
For example, you can type:
$ modfree generate -o Generated -modes 2 -n 70 -noise 0.05 -fields 600 700 850 950 1200 -rates R1 R2 NOE etaXY etaZ
## Data fitting
To fit relaxation data, you will need your data in a specific format akin to the generated data. Generate some data to see how it’s done. You will also need a directory file and a parameter file. type the following command to fit the data generated in the previous section:
$ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml
You can also fit only part of the data with the flag -r:
$ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml -r 10 11 12 13 14 15 16 17
Or
$ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml -r 15
## Data plotting
To plot the fitted data, just type:
$ modfree plot -o Generated_fit -p all
The following flags are available: -o (str): Output directory containing the data. -p: What to plot (all, relaxation, parameters, statistics, correlation) -format: Format of the plot files (pdf, png, jpg, svg…) -dpi (600 by default)
Metadata
Release files for modelfree-protein15n 0.0.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| modelfree-protein15n-0.0.7.tar.gz | 15.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| modelfree_protein15n-0.0.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 39.0 kB
Release files / modelfree-protein15n-0.0.7.tar.gz
| Download URL | modelfree-protein15n-0.0.7.tar.gz |
|---|---|
| Size | 15.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
76e1ded001ebcaab60ff28a017286ca33b4dbfd10fe00b724d208b3877e2d07e
|
|
BLAKE2b-256 checksum How to use checksums |
35e5d44ceb5f5c227510238c0311cf88e55589c3d57534c8bacaa9b03badf1ea
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.8
|
Release files / modelfree_protein15n-0.0.7-py3-none-any.whl
| Download URL | modelfree_protein15n-0.0.7-py3-none-any.whl |
|---|---|
| Size | 23.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ba493f45a140e4c3c6c796ddbf7a17da02a6adb6703637580a2fccc45a63c168
|
|
BLAKE2b-256 checksum How to use checksums |
efb80964a6135805e26f569e7e22361103156a748449765872227d0dca0fc5b2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.8
|
Security for modelfree-protein15n 0.0.7
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.