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Metainference: A Bayesian inference method for heterogeneous systems
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DOI：10.1126/sciadv.1501177.png)
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摘要
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Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called metainference, that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.
Keyword:
PROTEIN-STRUCTURE
CHEMICAL-SHIFTS
RECOGNITION
PREDICTION
MECHANISM
DYNAMICS
MODELS
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期刊
IF:
12.5
论文数:
2.1W
被引数:
18.1W
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