Buccaneer model building with neural network fragment selection.
Buccaneer model building with neural network fragment selection.
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DOI：
10.1107/s205979832300181x
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发表时间：
2023-04-01
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一个神经网络训练，以确定不利的片段，从而提高蛋白质模型的建设中的海盗软件。追踪骨架是蛋白质模型构建的关键步骤，因为不正确的追踪会导致蛋白质模型不佳。在这里，提出了一种神经网络训练，以识别不利的片段，并将它们从建模过程中删除，以提高骨干追踪。此外，一个决策树进行训练，以选择一个最佳的阈值，以消除不利的片段。该神经网络在结构基因组学联合中心（JCSG）的实验定相数据集、最近存放的实验定相数据集（2015年至2021年）和分子置换数据集上进行了测试。实验结果表明，在Buccaneer蛋白质模型构建软件中使用神经网络可以产生比单独使用Buccaneer构建的蛋白质模型更完整的蛋白质模型。特别是，Buccaneer使用神经网络构建的蛋白质模型的完整性分别为25%和50%的原始和截断分辨率JCSG实验定相数据集，28%的最近收集的实验定相数据集和43%的分子置换数据集。
A neural network trained to identify unfavourable fragments and therefore improve protein model building in the Buccaneer software is described. Tracing the backbone is a critical step in protein model building, as incorrect tracing leads to poor protein models. Here, a neural network trained to identify unfavourable fragments and remove them from the model-building process in order to improve backbone tracing is presented. Moreover, a decision tree was trained to select an optimal threshold to eliminate unfavourable fragments. The neural network was tested on experimental phasing data sets from the Joint Center for Structural Genomics (JCSG), recently deposited experimental phasing data sets (from 2015 to 2021) and molecular-replacement data sets. The experimental results show that using the neural network in the Buccaneer protein-model-building software can produce significantly more complete protein models than those built using Buccaneer alone. In particular, Buccaneer with the neural network built protein models with a completeness that was at least 5% higher for 25% and 50% of the original and truncated resolution JCSG experimental phasing data sets, respectively, for 28% of the recently collected experimental phasing data sets and for 43% of the molecular-replacement data sets.