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ProteinConformers: large-scale and energetically profiled descriptions of protein conformational landscapes

Zhou, Y.; Wei, C.; Sun, M.; Wang, L.; Song, J.; Xu, F.; Li, Y.; Zheng, W.; Zhang, Y.

2026-02-20 bioinformatics
10.64898/2026.02.20.707011 bioRxiv
Show abstract

Modeling protein conformational landscapes is essential for understanding dynamics, allostery, and drug discovery, yet existing resources lack diverse conformational coverage, energetic annotations, or benchmarking standards. ProteinConformers (https://zhanggroup.org/ProteinConformers) provides 2.7 million geometry-optimized conformations generated with a multi-seed molecular dynamics strategy, paired with 13.7 million energy evaluations and 5.5 million similarity annotations. It delivers continuous landscapes from non-native to near-native states, benchmarking framework for multi-conformation generators, and an interactive analysis platform.

Matching journals

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

1
Nature Communications
○ 5641 papers in training set
Top 7%
21.8%
Similar papers in this journal
2
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 0.4%
13.0%
Similar papers in this journal
3
Bioinformatics
◐ 1204 papers in training set
Top 3%
6.7%
Similar papers in this journal
4
Communications Biology
○ 993 papers in training set
Top 2%
5.5%
Similar papers in this journal
5
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
5.5%
Similar papers in this journal
50% of probability mass above
6
Protein Science
○ 246 papers in training set
Top 0.8%
5.1%
7
Journal of Chemical Theory and Computation
● 140 papers in training set
Top 0.4%
4.3%
8
Nature Methods
○ 385 papers in training set
Top 2%
4.3%
9
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.2%
10
Scientific Data
○ 209 papers in training set
Top 0.8%
3.2%
11
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.4%
12
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 24%
2.1%
13
Nature Computational Science
○ 55 papers in training set
Top 0.5%
2.0%
14
Scientific Reports
○ 3612 papers in training set
Top 49%
1.9%
15
Structure
○ 193 papers in training set
Top 1%
1.7%
16
Proteins: Structure, Function, and Bioinformatics
○ 88 papers in training set
Top 0.8%
1.5%
17
Journal of Molecular Biology
○ 232 papers in training set
Top 2%
1.3%
18
Communications Chemistry
○ 48 papers in training set
Top 0.8%
1.1%
19
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.1%
20
eLife
● 5828 papers in training set
Top 63%
1.0%
21
The Journal of Physical Chemistry B
● 167 papers in training set
Top 2%
1.0%
22
Nucleic Acids Research
◐ 1281 papers in training set
Top 15%
0.6%
23
Journal of Cheminformatics
○ 29 papers in training set
Top 0.8%
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.