Back

PySteMoDA: An Open Source Python Package for the Analysis of Steered Molecular Dynamics Simulations Data

Mesbah, I.; Klaus, C.; Sotomayor, M.; Sumbul, F.; Rico, F.

2026-01-28 biophysics
10.64898/2026.01.26.699872 bioRxiv
Show abstract

Molecular dynamics simulation is a powerful computational technique used for predicting and understanding the dynamic behavior of biomolecular systems. Steered molecular dynamics (SMD) simulations enable the study of force-induced processes in biomolecules, effectively mimicking single-molecule force spectroscopy experiments probing protein unfolding and receptor-ligand unbinding. Given the stochastic nature of these mechanical events, accurately exploring the dynamic behavior of biomolecules and extracting accurate physical information requires several in-silico experiments. This includes performing many pulling simulations at different velocities or force loading rates. The large amount of data obtained from these simulation sets requires efficient automated data processing tools. We present PySteMoDA, a novel Python package with a user-friendly graphical interface specifically designed for constant-velocity SMD data analysis. The automated force peak detection methods reduce user bias, improve accuracy, and accelerate data analysis. The package also allows identification of residues involved in mechanical events through computation of the time-dependent mechanical work and correlation factors between residue pairs. This package not only addresses automated data processing in SMD simulations and accurate parameter extraction, but also significantly enhances accessibility and usability. Through PySteMoDA, users can efficiently analyze simulation data without the barrier of coding, facilitating a wider range of investigations and insights in the field of computational biochemistry and biophysics.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Journal of Chemical Information and Modeling
207 papers in training set
Top 0.1%
28.4%
2
PLOS Computational Biology
1633 papers in training set
Top 3%
10.4%
3
Journal of Chemical Theory and Computation
126 papers in training set
Top 0.2%
5.0%
4
SoftwareX
15 papers in training set
Top 0.1%
4.1%
5
Bioinformatics
1061 papers in training set
Top 5%
4.1%
50% of probability mass above
6
PLOS ONE
4510 papers in training set
Top 35%
4.1%
7
Biophysical Journal
545 papers in training set
Top 2%
3.7%
8
Frontiers in Molecular Biosciences
100 papers in training set
Top 0.4%
3.7%
9
Scientific Reports
3102 papers in training set
Top 41%
3.2%
10
Journal of Molecular Biology
217 papers in training set
Top 0.9%
2.7%
11
Computational and Structural Biotechnology Journal
216 papers in training set
Top 3%
2.1%
12
The Journal of Chemical Physics
49 papers in training set
Top 0.2%
2.1%
13
Journal of Computational Chemistry
11 papers in training set
Top 0.1%
1.8%
14
Protein Science
221 papers in training set
Top 0.8%
1.7%
15
Bioinformatics Advances
184 papers in training set
Top 3%
1.7%
16
eLife
5422 papers in training set
Top 46%
1.4%
17
Nature Computational Science
50 papers in training set
Top 0.9%
1.3%
18
The Journal of Physical Chemistry Letters
58 papers in training set
Top 1%
1.3%
19
Physical Biology
43 papers in training set
Top 2%
1.0%
20
Briefings in Bioinformatics
326 papers in training set
Top 6%
0.9%
21
The Journal of Physical Chemistry B
158 papers in training set
Top 2%
0.8%
22
Biophysical Reports
36 papers in training set
Top 0.5%
0.8%
23
Communications Biology
886 papers in training set
Top 25%
0.7%
24
iScience
1063 papers in training set
Top 33%
0.7%
25
The European Physical Journal E
15 papers in training set
Top 0.1%
0.7%
26
Nucleic Acids Research
1128 papers in training set
Top 19%
0.7%
27
Nature Communications
4913 papers in training set
Top 67%
0.5%
28
ACS Omega
90 papers in training set
Top 5%
0.5%
29
BMC Bioinformatics
383 papers in training set
Top 8%
0.5%