Automatic modeling of protein backbones in electron-density maps via prediction of Cα coordinates
Automatic modeling of protein backbones in electron-density maps via prediction of Cα coordinates
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DOI：
10.1107/s0907444902016724
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发表时间：
2002-12-01
期刊：
影响因子：
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通讯作者：
Sacchettini, JC
中科院分区：
文献类型：
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作者：
Ioerger, TR;Sacchettini, JC
今天，大多数晶体学家通过首先构建尽可能多的蛋白质骨架，然后模拟侧链来解决蛋白质结构。通过基于计算机的电子密度解释自动确定骨架坐标将提高速度并可能提高结构-溶液过程的准确性。在本文中，描述了一种新的计算程序，称为CAPRA，预测C（α）原子在密度图中的坐标，并输出代表蛋白质骨架的C（α）原子链。该结果构成了超越追踪密度的重要一步，因为在CAPRA输出的链中预测的原子与真实结构中的C（alpha）原子之间存在理想的一一对应关系（精细模型）。CAPRA基于模式识别技术，包括提取旋转不变的数字特征来表示密度模式，以及使用神经网络来预测迹线中哪些伪原子最接近真实的C（alpha）原子。用几个2.4-2.8埃分辨率的MAD和MIR电子密度图进行的实验表明，CAPRA能够构建类似于蛋白质分子90%的主链，具有r.m.s. C（alpha）坐标的误差约为0.9埃。
Most crystallographers today solve protein structures by first building as much of the protein backbone as possible and then modeling the side chains. Automating the determination of backbone coordinates by computer-based interpretation of the electron density would enhance the speed and possibly improve the accuracy of the structure-solution process. In this paper, a new computational procedure called CAPRA is described that predicts coordinates of C(alpha) atoms in density maps and outputs chains of C(alpha) atoms representing the backbone of the protein. The result constitutes a significant step beyond tracing the density, because there is ideally a one-to-one correspondence between atoms predicted in the chains output by CAPRA and C(alpha) atoms in the true structure (refined model). CAPRA is based on pattern-recognition techniques, including extraction of rotation-invariant numeric features to represent patterns in the density and use of a neural network to predict which pseudo-atoms in the trace are closest to true C(alpha) atoms. Experiments with several MAD and MIR electron-density maps of 2.4-2.8 Angstrom resolution reveal that CAPRA is capable of building similar to90% of the backbone of a protein molecule, with an r.m.s. error for C(alpha) coordinates of around 0.9 Angstrom.