• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Genomics >iBitter-SCM: Identi fication and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides
【24h】

iBitter-SCM: Identi fication and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides

机译：Ibitter-SCM：使用诸如二肽的倾向分数的评分卡方法鉴定和表征苦肽

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

In general, hydrolyzed proteins, plant-derived alkaloids and toxins displays unpleasant bitter taste. Thus, the perception of bitter taste plays a crucial role in protecting animals from poisonous plants and environmental toxins. Therapeutic peptides have attracted great attention as a new drug class. The successful identification and characterization of bitter peptides are essential for drug development and nutritional research. Owing to the large volume of peptides generated in the post-genomic era, there is an urgent need to develop computational methods for rapidly and effectively discriminating bitter peptides from non-bitter peptides. To the best of our knowledge, there is yet no computational model for predicting and analyzing bitter peptides using sequence information. In this study, we present for the first time a computational model called the iBitter-SCM that can predict the bitterness of peptides directly from their amino acid sequence without any dependence on their functional domain or structural information. iBitter-SCM is a simple and effective method that was built using the scoring card method (SCM) with estimated propensity scores of amino acids and dipeptides. Our benchmarking results demonstrated that iBitter-SCM achieved an accuracy and Matthews coefficient correlation of 84.38% and 0.688, respectively, on the independent dataset. Rigorous independent test indicated that iBitterSCM was superior to those of other widely used machine-learning classifiers (e.g. k-nearest neighbor, naive Bayes, decision tree and random forest) owing to its simplicity, interpretability and implementation. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide a better understanding of the biophysical and biochemical properties of bitter peptides. For the convenience of experimental scientists, a web server is provided publicly at http://camt.pythonanywhere.com/ iBitter-SCM. It is anticipated that iBitter-SCM can serve as an important tool to facilitate the high-throughput prediction and de novo design of bitter peptides.
机译：通常，水解蛋白质，植物衍生的生物碱和毒素显示出令人不愉快的苦味。因此，苦味的感知在保护来自有毒植物和环境毒素的动物中起着至关重要的作用。治疗性肽作为一种新的药物课吸引了极大的关注。苦肽的成功鉴定和表征对于药物开发和营养研究至关重要。由于在后基因组时代产生的大量肽，迫切需要开发用于从非苦肽的快速和有效地区分苦肽的计算方法。据我们所知，尚未使用序列信息预测和分析苦肽的计算模型。在这项研究中，我们首次出现称称为Ibitter-SCM的计算模型，其可以直接从其氨基酸序列预测肽的苦味，而不对其功能域或结构信息的任何依赖性。 Ibitter-SCM是一种简单有效的方法，采用得分卡方法（SCM）建造，具有估计的氨基酸和二肽的倾向分数。我们的基准测试结果表明，Ibitter-SCM在独立数据集中分别实现了84.38％和0.688的准确性和马太福德系数。严格的独立测试表明，由于其简单，可解释性和实施，Ibitterscm优于其他广泛使用的机器学习分类器（例如K-Counteld Neight，Naive Bay，决策树和随机林）。此外，进行估计氨基酸和二肽的估计倾斜分数的分析，以更好地了解苦肽的生物物理和生化特性。为了方便实验科学家，网站服务器在http://camt.pythonanywherewher.com/ ibitter-scm上公开提供。预计Ibitter-SCM可以作为促进苦味肽的高通量预测和De Novo设计的重要工具。

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