Reweighting ensemble probabilities with experimental histogram data constraints using a maximum entropy principle.
Reweighting ensemble probabilities with experimental histogram data constraints using a maximum entropy principle.
复制标题
使用最大熵原理通过实验直方图数据约束重新加权集合概率。
DOI:
10.1063/1.5050926
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发表时间:
2018
期刊:
影响因子:
--
通讯作者:
R. Cukier
中科院分区:
文献类型:
--
作者:
Hongfeng Lou;R. Cukier
Entropy maximization methods that update a probability distribution P 0(x) to a new distribution P(x) with the use of externally known, averaged constraints find use in diverse areas. Jaynes developed a Maximum Entropy Procedure (MEP) that is an objective approach to incorporate external data to update P 0(x) to P(x). In this work, we consider the MEP in the context of external data known from a probability distribution versus that from a mean and a few higher moments. An immediate problem is that the conventional iterative Lagrange multiplier method, which relies on inverting a certain covariance matrix, is not applicable here because the covariance matrix is not invertible. We introduce an indicator function method that does not suffer from this problem. It leads to an analytic solution to this version of a MEP. As an example, a previously generated ensemble of peptide conformations used to characterize an intrinsically disordered protein is analyzed. The external constraint is on the radius of gyration probability distribution, p(RG), of this peptide. Ensemble observables such as geometric, shape characteristics, the residue end-to-end distance distribution, the all atom-pair distribution function related to the scattering intensity, the polyproline II content, and NMR 3JHNHα three bond couplings are evaluated with the initial and updated ensembles. Some observables are found to be insensitive and others sensitive to the external information. An example of a 24-residue peptide, histatin 5, where an experimentally derived p(RG) is available, is also analyzed.
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DOI:
10.1021/acs.jpcb.6b08678
发表时间:
2016
期刊:
The journal of physical chemistry. B
影响因子:
--
作者:
Reppert,Mike;Roy,AnishR;Tempkin,JeremyOB;Dinner,AaronR;Tokmakoff,Andrei
通讯作者:
Tokmakoff,Andrei
影响因子:
6.8
作者:
Hub, Jochen S.
通讯作者:
Hub, Jochen S.
影响因子:
12.4
作者:
Nussinov R;Tsai CJ;Ma B
通讯作者:
Ma B
影响因子:
3.3
作者:
Roux, Benoit;Islam, Shahidul M.
通讯作者:
Islam, Shahidul M.
影响因子:
3.4
作者:
Marinelli, Fabrizio;Faraldo-Gomez, Jose D.
通讯作者:
Faraldo-Gomez, Jose D.