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Moment-based prediction of DNA-binding proteins.

机译:基于瞬间的DNA结合蛋白预测。

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摘要

Net charge, electric dipole moment and quadrupole moment tensors were calculated for 78 amino acid sequences from 62 representative DNA-binding proteins with known structures. It was found that the magnitudes of the moments of electric charge distribution in these chains differ significantly from those of a non-binding control data set. Net charge, net dipole moment and quadrupole moment could each distinguish binding and non-binding proteins with 82.6%, 77.4% and 73.7% accuracy by single-variable predictors without cross-validation. Using hybrid predictors with information of charge and both moments, the best predictions were 85.6% without cross-validation and 83.9% for the cross-validated data sets. This level of prediction accuracy obtained with these simple descriptors competes with the results obtained using more complex models including many descriptors. The coarse graining of atomic charges onto C(alpha) atoms did not reduce the prediction accuracy significantly. This result suggests that we canuse C(alpha) coordinates derived from homology modeling to predict DNA-binding proteins. The speed and accuracy of this method, in combination with homology-based methods of structure prediction, should enhance genome-wide recognition of DNA-binding proteins.
机译:从具有已知结构的62个代表性DNA结合蛋白中计算了78个氨基酸序列的净电荷,电偶极矩和四极矩张量。发现这些链中电荷分布的矩的大小与非约束性控制数据集的显着不同。净电荷,净偶极矩和四极矩可以通过单变量预测变量分别区分结合蛋白和非结合蛋白,准确率分别为82.6%,77.4%和73.7%,而无需交叉验证。使用带有电荷和两个时刻信息的混合预测器,没有交叉验证的最佳预测是85.6%,而交叉验证的数据集的最佳预测是83.9%。用这些简单的描述符获得的这种预测准确性水平与使用包括许多描述符的更复杂的模型获得的结果相抗衡。原子电荷在Cα原子上的粗粒化并没有显着降低预测精度。该结果表明我们可以使用源自同源建模的Cα坐标来预测DNA结合蛋白。这种方法的速度和准确性,与基于同源性的结构预测方法相结合,应能增强DNA结合蛋白的全基因组识别能力。

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