Combining Experimental Data and Computational Methods for the Non-Computer Specialist
Abstract
:1. Introduction
2. Basic Strategies to Integrate Experiments and Computational Methods
3. Nuclear Magnetic Resonance
4. Small Angle X-Ray Scattering
5. Cryo Electron Microscopy
6. Mass Spectrometry
7. Förster Resonance Energy Transfer
8. Electron Paramagnetic Resonance
9. Fluorescence, UV–Vis and Infrared Spectroscopies
10. Other Techniques
11. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Computational Term | Brief Description |
---|---|
Molecular dynamics simulation | Sampling method. New conformations are generated by using Newton’s equations (Force field) [9]. |
Monte Carlo simulation | Sampling method. New conformations are generated by random perturbations, then the conformation is accepted or rejected based on some fixed criteria [9]. |
Docking methods | Computational method to predict complex formation. It consists of two steps the simulation, where different binding poses are sampled and the scoring, where the best binding pose is selected based on predefined rules [9]. |
Selection based on maximum entropy | This method selects the larger number of conformer (maximum entropy) that match experimental data [12]. |
Selection based on maximum parsimony | This method selects the minimum number of conformers (maximum parsimony) that can explain the experimental data [12]. |
Selection based on Bayesian | This methods combines the use of prior information and new evidence in the selection process [12]. |
Program | Accepted Experimental Data | Functionality | Availability | Ref |
---|---|---|---|---|
CHARMM | Distance */Cryo-EM | Molecular Dynamics simulations software. | www.charmm.org | [15] |
GROMACS | NMR/Distance * | Molecular Dynamics simulations software. | www.gromacs.org | [16] |
Xplor-NIH | NMR/SAXS/Cryo-EM Distance * | Structure determination software. | nmr.cit.nih.gov/xplor-nih | [17] |
Phaistos | NMR/SAXS | Monte Carlo simulations software. | sourceforge.net/projects/phaistos/ | [18] |
Flexible-meccano | NMR /SAXS | Generate randomly conformers ensembles | www.ibs.fr/research/scientific-output/software | [20] |
HADDOCK | XL-MS/HDX-MS/Cryo-EM NMR | Information-driven flexible docking approach | bianca.science.uu.nl/haddock2.4/ | [24] |
iDOCK | Distances * | Docking. Included on IMP | integrativemodeling.org | [25] |
pyDockSAXS | SAXS | Docking with SAXS profile | life.bsc.es/pid/pydocksaxs | [26] |
ENSEMBLE | SAXS/NMR | Ensemble selection software. | abragam.med.utoronto.ca/~JFKlab/# | [28] |
X-EISD | NMR/SAXS/FRET | Ensemble selection software. | github.com/THGLab/X-EISD | [29] |
BME | Distances */SAXS/NMR | Entropy ensemble selection software. | github.com/KULL-Centre/BME | [30] |
MESMER | DEER/SAXS/ NMR/Other | Minimal ensemble Solutions to Multiple Experimental Restraints | github.com/steelsnowflake/mesmer | [19] |
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Cárdenas, R.; Martínez-Seoane, J.; Amero, C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020, 25, 4783. https://doi.org/10.3390/molecules25204783
Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules. 2020; 25(20):4783. https://doi.org/10.3390/molecules25204783
Chicago/Turabian StyleCárdenas, Reinier, Javier Martínez-Seoane, and Carlos Amero. 2020. "Combining Experimental Data and Computational Methods for the Non-Computer Specialist" Molecules 25, no. 20: 4783. https://doi.org/10.3390/molecules25204783