Adhikari Badri, Cheng Jianlin
Department of Computer Science, University of Missouri, 201 Engineering Building West, Columbia, MO, 65211, USA.
Methods Mol Biol. 2016;1415:463-76. doi: 10.1007/978-1-4939-3572-7_24.
In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated.
在计算结构蛋白质组学领域，接触预测为解决从头开始预测蛋白质结构这一长期存在的问题展现了新的前景。在过去几年中，深度学习算法的应用以及大型蛋白质序列数据库的可得性，再加上从多序列比对中推导接触的方法的改进，使得接触预测的精度大幅提高。此外，这些预测的接触还被用于从头构建三维模型。在本章中，我们简要讨论蛋白质残基-残基接触的诸多要素以及可用于预测的方法，重点介绍一种先进的接触预测工具DNcon。通过一个案例研究进行说明，我们描述了如何使用DNcon对给定的蛋白质序列进行从头接触预测，并讨论如何对预测的接触进行分析和评估。
