Back

Simulation atomic force microscopy to predict correlated conformational dynamics in proteins from topographic imaging

Flechsig, H.

2021-10-16 biophysics
10.1101/2021.10.15.464530 bioRxiv
Show abstract

Atomic force microscopy (AFM) of proteins can detect only changes within the scanned molecular surface, missing all motions in other regions and thus information about functionally relevant conformational couplings. We show that simulation AFM can overcome this drawback by reconstruction of 3D molecular structures from topographic AFM images. A proof of principle demonstration is provided for an in-silico AFM experiment visualizing the conformational dynamics of a membrane transporter. The application shows that the alternating access mechanism underlying its operation can be retrieved from only AFM imaging of one membrane side. Simulation AFM is implemented in the freely available BioAFMviewer software platform, providing the convenient applicability to better understand experimental AFM observations.

Matching journals

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

1
Nano Letters
● 71 papers in training set
Top 0.1%
15.4%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 2%
13.4%
Similar papers in this journal
3
eLife
● 5828 papers in training set
Top 10%
10.0%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 20%
8.0%
Similar papers in this journal
5
Biophysical Journal
○ 631 papers in training set
Top 2%
4.4%
Similar papers in this journal
50% of probability mass above
6
Nature Methods
○ 385 papers in training set
Top 2%
4.4%
7
Communications Biology
○ 993 papers in training set
Top 4%
3.6%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 19%
2.7%
9
Biophysical Reports
○ 37 papers in training set
Top 0.1%
2.4%
10
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.2%
11
The Journal of Physical Chemistry Letters
● 63 papers in training set
Top 0.4%
1.9%
12
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 0.6%
1.9%
13
Nanoscale
● 42 papers in training set
Top 0.3%
1.8%
14
ACS Nano
● 113 papers in training set
Top 1%
1.8%
15
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.7%
16
Journal of Chemical Information and Modeling
● 238 papers in training set
Top 2%
1.7%
17
iScience
○ 1154 papers in training set
Top 22%
1.5%
18
The Journal of Physical Chemistry B
● 167 papers in training set
Top 1%
1.1%
19
International Journal of Molecular Sciences
○ 494 papers in training set
Top 11%
1.1%
20
Analytical Chemistry
● 218 papers in training set
Top 2%
1.1%
21
Life Science Alliance
● 285 papers in training set
Top 7%
0.9%
22
Structure
○ 193 papers in training set
Top 2%
0.9%
23
Chemical Science
● 73 papers in training set
Top 2%
0.9%
24
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.6%
25
Nucleic Acids Research
◐ 1281 papers in training set
Top 14%
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.