• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction
【24h】

Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction

机译：脯氨酸顺反异构化预测的随机子空间学习

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.
机译：脯氨酸残基是折叠期间动力学并发症的常见来源。 X-Pro肽键是唯一具有相当的顺式和反式构象稳定性的肽键。 X-Pro肽键的顺反异构化（CTI）是一个广为人知的限速因子，它不仅可以诱导蛋白质折叠中的其他慢相，而且可以改变蛋白质的毫秒级和亚毫秒级动力学。脯氨酸CTI的准确计算预测对于理解蛋白质在人体和动物中的折叠，剪接，细胞信号传导和跨膜活性转运至关重要。在我们的早期工作中，我们使用具有强大的金属识别技术的新颖的基于树的共识模型，成功开发了具有生物物理动机的脯氨酸CTI预测因子，并获得了28.58％的Q2准确度和0.74 Mcc的结果，比结果要好（70-73％ Q2精度）在文献中引用的参考基准数据集很高。在本文中，我们描述了使用新型随机子空间学习和Bootstrap播种技术的实验，作为对我们早期工作的扩展，共识模型以及基于熵的学习方法，旨在通过针对脯氨酸CTI的精确而强大的学习方案来获得更好的准确性预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号