Evaluating mixture models for building RNA knowledge-based potentials.
Evaluating mixture models for building RNA knowledge-based potentials.
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DOI：
10.1142/s0219720012410107
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发表时间：
2012-04
影响因子：
1
通讯作者：
Bernauer J
中科院分区：
文献类型：
--
作者：
Sim AY;Schwander O;Levitt M;Bernauer J
核糖核酸（RNA）分子在多种生物过程中发挥重要作用。为了正常发挥功能，RNA分子通常必须折叠成特定的结构，因此理解RNA结构对于理解RNA如何发挥功能至关重要。理解和预测生物分子结构的一种方法是使用从实验确定的结构构建的基于知识的势。这些类型的潜力已被证明是有效的预测蛋白质和RNA的结构，但它们的实用性是有限的，其显着崎岖的性质。这种坚固性（以及因此潜在的有用性）在很大程度上取决于对结构信息（例如距离）进行分类的bin宽度的选择，但适当的bin宽度并不是先验已知的。为了避免分箱问题，我们比较了基于知识的潜力，建立在RNA结构中的原子间距离使用不同的混合模型（核密度估计，期望最小化和Dirichlet过程）。我们表明，从狄利克雷过程中建立的平滑的知识为基础的潜力是成功的，在选择本地样RNA模型，从不同的结构诱饵具有可比的效力，以潜在的开发样条拟合-一种常用的方法-分箱距离直方图。我们的潜力不那么坚固，这表明它适用于各种类型的结构建模。
Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting — a commonly taken approach — to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.