论文标题
蛋白质接触预测的无监督和监督的结构学习
Unsupervised and Supervised Structure Learning for Protein Contact Prediction
论文作者
论文摘要
蛋白质接触为理解蛋白质结构和功能提供了关键信息，因此序列的接触预测是一个重要的问题。最近的研究表明，一些正确预测的远程接触可以帮助拓扑级的结构建模。因此，接触预测和接触辅助蛋白折叠也证明了此问题的重要性。在本论文中，我将简要介绍现存的相关工作，然后展示如何通过无监督的图形模型建立与拓扑约束的图形模型。此外，我将解释如何使用监督的深度学习方法进一步提高接触预测的准确性。最后，我将提出一个称为多样性评分的评分系统，以衡量接触预测的新颖性，以及一种预测有关新评分系统接触的算法。
Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range contacts could help topology-level structure modeling. Thus, contact prediction and contact-assisted protein folding also proves the importance of this problem. In this thesis, I will briefly introduce the extant related work, then show how to establish the contact prediction through unsupervised graphical models with topology constraints. Further, I will explain how to use the supervised deep learning methods to further boost the accuracy of contact prediction. Finally, I will propose a scoring system called diversity score to measure the novelty of contact predictions, as well as an algorithm that predicts contacts with respect to the new scoring system.
