Name
Abstract | ||
|---|---|---|
Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/e21090898 | ENTROPY |
Keywords | Field | DocType |
Bayesian methods,maximum entropy,chemical shifts,intrinsically disordered proteins,protein ensembles,structural modelling,NMR,molecular dynamics | Statistical physics,Mathematical optimization,Data type,Molecular dynamics,Intrinsically disordered proteins,Principle of maximum entropy,Chemical shift,Mathematics,Bayesian probability | Journal |
Volume | Issue | Citations |
21 | 9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramon Crehuet | 1 | 23 | 4.44 |
| Pedro J. Buigues | 2 | 0 | 0.34 |
| Xavier Salvatella | 3 | 0 | 0.68 |
| Kresten Lindorff-Larsen | 4 | 60 | 6.32 |