The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S2 to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite complex and lengthy. In this paper, we present a machine learning approach for predicting backbone 1H-15N order parameters based on protein NMR structure ensemble. A random forest model is used to learn the relationship between order parameters and structural features. Our method achieves high accuracy in predicting backbone 1H-15N order parameters for a test dataset of 10 proteins, with a Pearson correlation coefficient of 0.817 and a root-mean-square error of 0.131.
[NMR paper] Prediction of order parameters based on protein NMR structure ensemble and machine learning
Prediction of order parameters based on protein NMR structure ensemble and machine learning
The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S² to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite...
...
[NMR paper] Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Related Articles Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Comput Struct Biotechnol J. 2020;18:603-611
Authors: Fino R, Byrne R, Softley CA, Sattler M, Schneider G, Popowicz GM
Abstract
NMR-based screening, especially fragment-based drug discovery is a...
[NMR paper] Interpretation of seemingly contradictory data: low NMR S2 order parameters observed in helices and high NMR S2 order parameters in disordered loops of the protein hGH at low pH.
Interpretation of seemingly contradictory data: low NMR S2 order parameters observed in helices and high NMR S2 order parameters in disordered loops of the protein hGH at low pH.
Related Articles Interpretation of seemingly contradictory data: low NMR S2 order parameters observed in helices and high NMR S2 order parameters in disordered loops of the protein hGH at low pH.
Chemistry. 2017 May 15;:
Authors: Smith LJ, Athill R, van Gunsteren WF, Hansen N
Abstract
At low pH human growth hormone (hGH) adopts a partially folded state...
nmrlearner
Journal club
0
05-16-2017 10:27 PM
[NMR paper] Contact model for the prediction of NMR N-H order parameters in globular proteins.
Contact model for the prediction of NMR N-H order parameters in globular proteins.
Related Articles Contact model for the prediction of NMR N-H order parameters in globular proteins.
J Am Chem Soc. 2002 Oct 30;124(43):12654-5
Authors: Zhang F, Brüschweiler R
An analytical relationship is presented for the estimation of NMR S2 order parameters of N-HN vectors of the protein backbone from high-resolution protein structures. The relationship solely depends on close contacts of the peptide plane to the rest of the protein. Application of the...
Prediction of order parameters based on protein NMR structure ensemble and machine learning
Linear Mode
