Back

Molecular dynamics descriptors for 1,079 post-translationally modified protein systems

Liu, K.; Qian, Q.; Peng, J.; Ma, D.; Yao, Y.; Zhao, J.; Chi, Y.

2026-08-24 biophysics
10.64898/2026.08.23.746532 bioRxiv
Show abstract

Databases of post-translational modifications (PTMs) catalogue modified sites and increasingly add static structural context, but trajectory-derived descriptors remain scattered across specialised tools and general molecular dynamics archives. Dyna-MO PTM brings together 1,079 AlphaFold 3-seeded systems covering lysine acetylation, lysine and arginine monomethylation, and serine, threonine and tyrosine phosphorylation. Each system is linked to three completed 10 ns replicas generated with CHARMM36m and TIP3P, for 32.37 s of aggregate sampling. A 118-column table joins simulation and quality-control provenance with global relaxation measures, site solvent exposure, rotamers, secondary structure and ionic-contact proxies. Versioned identifiers connect the records to starting structures, trajectories, manifests and analysis scripts. Researchers can use the resource to filter PTM contexts, reproduce descriptors, prioritise longer simulations and evaluate trajectory-analysis or generative methods. The trajectories describe finite-window relaxation rather than equilibrium free energies, kinetics or matched PTM effects.

Matching journals

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

1
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 0.2%
12.6%
Similar papers in this journal
2
Protein Science
○ 246 papers in training set
Top 0.2%
11.9%
Similar papers in this journal
3
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 0.4%
11.9%
Similar papers in this journal
4
Nature Methods
○ 385 papers in training set
Top 1%
8.9%
Similar papers in this journal
5
Nature Communications
○ 5641 papers in training set
Top 22%
7.3%
Similar papers in this journal
50% of probability mass above
6
eLife
● 5828 papers in training set
Top 19%
6.2%
7
Scientific Data
○ 209 papers in training set
Top 0.6%
4.0%
8
Structure
○ 193 papers in training set
Top 0.7%
3.5%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 6%
2.7%
10
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.4%
11
Nature
○ 645 papers in training set
Top 6%
2.1%
12
Journal of Molecular Biology
○ 232 papers in training set
Top 2%
1.7%
13
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 30%
1.5%
14
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.5%
15
Communications Chemistry
○ 48 papers in training set
Top 0.6%
1.4%
16
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.3%
17
PLOS ONE
● 5266 papers in training set
Top 53%
1.1%
18
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.1%
19
Chemical Science
● 73 papers in training set
Top 2%
1.0%
20
Communications Biology
○ 993 papers in training set
Top 28%
0.9%
21
Scientific Reports
○ 3612 papers in training set
Top 72%
0.8%
22
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%
23
The Journal of Chemical Physics
● 56 papers in training set
Top 0.6%
0.6%
24
Biophysical Journal
○ 631 papers in training set
Top 5%
0.6%
25
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.