New version of DynaMine
Since February 2022 Dynamine is using the B2B Tools as backend, in this new version:
- Email notifications are not longer supported.
- The former JSON API was replaced by a REST API while the beta command line tool was replaced by a Python package. For further technical information, please click here.
Try DynaMine out, submit your sequence here!
DynaMine is a fast predictor of protein backbone dynamics using only sequence information as input. Given
a protein sequence, DynaMine predicts backbone flexibility at the residue-level in the form of backbone N-H
S2 order parameter values. These S2 values represent how restricted the movement of the
atomic bond vector is with respect to the molecular reference frame. A value of 1 means complete order (stable
conformation), while a value of 0 means fully random bond vector movement (highly dynamic).
DynaMine has been trained on backbone N-H S2 order parameter values that have been directly estimated from experimentally determined NMR chemical shifts. Although it is based on a simple linear regression approach, DynaMine is able to accurately distinguish regions of different structural organization within proteins, such as folded domains and disordered linkers of different sizes. Additionally, it can identify disordered regions within proteins with an accuracy comparable to the most sophisticated existing disorder predictors. Remarkably, DynaMine achieves this high performance without depending on prior disorder knowledge or three-dimensional structural information, which makes it a unique approach on the field as well as providing independent proof of the relationship between dynamics and structural disorder in protein regions.
Finally, the predicted values have a clear physical meaning: they are relevant on an absolute scale and go beyond the binary classification of the predicted residues as ordered or disordered, so allowing for direct dynamics comparisons between protein regions.
DynaMine has been trained on backbone N-H S2 order parameter values that have been directly estimated from experimentally determined NMR chemical shifts. Although it is based on a simple linear regression approach, DynaMine is able to accurately distinguish regions of different structural organization within proteins, such as folded domains and disordered linkers of different sizes. Additionally, it can identify disordered regions within proteins with an accuracy comparable to the most sophisticated existing disorder predictors. Remarkably, DynaMine achieves this high performance without depending on prior disorder knowledge or three-dimensional structural information, which makes it a unique approach on the field as well as providing independent proof of the relationship between dynamics and structural disorder in protein regions.
Finally, the predicted values have a clear physical meaning: they are relevant on an absolute scale and go beyond the binary classification of the predicted residues as ordered or disordered, so allowing for direct dynamics comparisons between protein regions.
How to cite DynaMine
-
The method
Elisa Cilia, Rita Pancsa, Peter Tompa, Tom Lenaerts, and Wim Vranken
From protein sequence to dynamics and disorder with DynaMine
Nature Communications 4:2741 doi: 10.1038/ncomms3741 (2013) -
The web-server
Elisa Cilia, Rita Pancsa, Peter Tompa, Tom Lenaerts, and Wim Vranken
The DynaMine webserver: predicting protein dynamics from sequence
Nucleic Acid Research doi: 10.1093/nar/gku270 (2014)






