Bioinformatics Center, University of Copenhagen, Department of Biology, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark.
J Magn Reson. 2011 Dec;213(1):182-6. doi: 10.1016/j.jmr.2011.08.039. Epub 2011 Sep 6.
Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inference. In this case, the forcefields are brought in as a prior distribution in the form of a Boltzmann factor. Due to high computational cost, the approach has been only sparsely applied in practice. Here, we demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure in structure determination from experimental data.
从 NMR 数据确定蛋白质结构的传统方法依赖于物理力场和实验数据的特殊组合，以及对自由参数（例如实验数据相对于物理力场的权重）的启发式确定。最近，开发了一种理论上严格的方法，将结构确定视为贝叶斯推断的问题。在这种情况下，力场以 Boltzmann 因子的形式作为先验分布引入。由于计算成本高，该方法在实践中仅得到了稀疏的应用。在这里，我们证明了在贝叶斯形式主义中使用生成概率模型而不是物理力场不仅在概念上具有吸引力，而且还可以提高精度和效率。我们的结果为在从实验数据确定结构中使用生物分子结构的复杂概率模型开辟了新的前景。
