Makigaki Shuichiro, Ishida Takashi
Department of Computer Science, School of Computing, Tokyo Institute of Technology Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
Comput Struct Biotechnol J. 2020 Jul 25;18:2043-2050. doi: 10.1016/j.csbj.2020.07.012. eCollection 2020.
Protein tertiary structure is important information in various areas of biological research, however, the experimental cost associated with structure determination is high, and computational prediction methods have been developed to facilitate a more economical approach. Currently, template-based modeling methods are considered to be the most practical because the resulting predicted structures are often accurate, provided an appropriate template protein is available. During the first stage of template-based modeling, sensitive homology detection is essential for accurate structure prediction. However, sufficient structural models cannot always be obtained due to a lack of quality in the sequence alignment generated by a homology detection program. Therefore, an automated method that detects remote homologs accurately and generates appropriate alignments for accurate structure prediction is needed. In this paper, we propose an algorithm for suitable alignment generation using an intermediate sequence search for use with template-based modeling. We used intermediate sequence search for remote homology detection and intermediate sequences for alignment generation of remote homologs. We then evaluated the proposed method by comparing the sensitivity and selectivity of homology detection. Furthermore, based on the accuracy of the predicted structure model, we verify the accuracy of the alignments generated by our method. We demonstrate that our method generates more appropriate alignments for template-based modeling, especially for remote homologs. All source codes are available at https://github.com/shuichiro-makigaki/agora.
蛋白质三级结构在生物学研究的各个领域都是重要信息，然而，与结构测定相关的实验成本很高，因此已经开发了计算预测方法以促进采用更经济的方法。目前，基于模板的建模方法被认为是最实用的，因为只要有合适的模板蛋白，所得到的预测结构通常是准确的。在基于模板的建模的第一阶段，灵敏的同源性检测对于准确的结构预测至关重要。然而，由于同源性检测程序生成的序列比对质量欠佳，往往无法获得足够的结构模型。因此，需要一种能够准确检测远源同源物并生成合适比对以进行准确结构预测的自动化方法。在本文中，我们提出了一种算法，用于使用中间序列搜索来生成合适的比对，以用于基于模板的建模。我们使用中间序列搜索进行远源同源性检测，并使用中间序列来生成远源同源物的比对。然后，我们通过比较同源性检测的灵敏度和选择性来评估所提出的方法。此外，基于预测结构模型的准确性，我们验证了我们的方法生成的比对的准确性。我们证明，我们的方法为基于模板的建模生成了更合适的比对，特别是对于远源同源物。所有源代码可在https://github.com/shuichiro-makigaki/agora获取。
