Back

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

Tessmer, M. H.; Stoll, S.

2022-12-24 biophysics
10.1101/2022.12.23.521725 bioRxiv
Show abstract

Here we introduce chiLife, a Python package for site-directed spin label (SDSL) modeling for electron paramagnetic resonance (EPR) spectroscopy, in particular double electron-electron resonance (DEER). It is based on in silico attachment of rotamer ensemble representations of spin labels to protein structures. chiLife enables the development of custom protein analysis and modeling pipelines using SDSL EPR experimental data. It allows the user to add custom spin labels, scoring functions and spin label modeling methods. chiLife is designed with integration into third-party software in mind, to take advantage of the diverse and rapidly expanding set of molecular modeling tools available with a Python interface. This article describes the main design principles of chiLife and presents a series of examples. Author summaryThanks to modern modeling methods like AlphaFold2, RosettaFold, and ESMFold, high-resolution structural models of proteins are widely available. While these models offer insight into the structure and function of biomedically and technologically significant proteins, most of them are not experimentally validated. Furthermore, many proteins exhibit functionally important conformational flexibility that is not captured by these models. Site-directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy is a powerful tool for probing protein structure and conformational heterogeneity, making it ideal for validating, refining, and expanding protein models. To extract quantitative protein backbone information from experimental SDSL EPR data, accurate modeling methods are needed. For this purpose, we introduce chiLife, an open-source Python package for SDSL modeling designed to be extensible and integrable with other Python-based protein modeling packages. With chiLife, appropriate SDSL EPR experiments for protein model validation can be designed, and protein models can be refined using experimental SDSL EPR data as constraints.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Protein Science
221 papers in training set
Top 0.1%
23.1%
2
Bioinformatics Advances
184 papers in training set
Top 0.3%
7.0%
3
PLOS ONE
4510 papers in training set
Top 27%
6.5%
4
Structure
175 papers in training set
Top 0.5%
5.0%
5
SoftwareX
15 papers in training set
Top 0.1%
4.4%
6
Journal of Chemical Information and Modeling
207 papers in training set
Top 1%
4.4%
50% of probability mass above
7
Journal of Molecular Biology
217 papers in training set
Top 0.4%
4.1%
8
Bioinformatics
1061 papers in training set
Top 5%
3.8%
9
Biophysical Journal
545 papers in training set
Top 2%
3.7%
10
PLOS Computational Biology
1633 papers in training set
Top 12%
2.7%
11
Frontiers in Molecular Biosciences
100 papers in training set
Top 1.0%
2.1%
12
The Journal of Physical Chemistry Letters
58 papers in training set
Top 0.7%
1.9%
13
Acta Crystallographica Section D Structural Biology
54 papers in training set
Top 0.2%
1.7%
14
Scientific Reports
3102 papers in training set
Top 57%
1.7%
15
ACS Omega
90 papers in training set
Top 2%
1.5%
16
Nucleic Acids Research
1128 papers in training set
Top 12%
1.4%
17
Journal of Applied Crystallography
14 papers in training set
Top 0.1%
1.4%
18
Computational and Structural Biotechnology Journal
216 papers in training set
Top 5%
1.4%
19
Journal of Chemical Theory and Computation
126 papers in training set
Top 0.6%
1.3%
20
International Journal of Molecular Sciences
453 papers in training set
Top 12%
1.0%
21
Molecules
37 papers in training set
Top 1%
0.9%
22
Journal of Computational Chemistry
11 papers in training set
Top 0.2%
0.8%
23
Biochemistry and Biophysics Reports
28 papers in training set
Top 1%
0.8%
24
BMC Bioinformatics
383 papers in training set
Top 6%
0.8%
25
Nature Communications
4913 papers in training set
Top 62%
0.8%
26
Biology Methods and Protocols
53 papers in training set
Top 2%
0.8%
27
Journal of Structural Biology: X
15 papers in training set
Top 0.2%
0.7%
28
IUCrJ
29 papers in training set
Top 0.4%
0.7%
29
Communications Biology
886 papers in training set
Top 28%
0.7%
30
Frontiers in Genetics
197 papers in training set
Top 12%
0.5%