Predicting algorithm of attC site based on combination optimization strategy. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Liu, Zhendong; Chen, Xi; Li, Dongyan; Lv, Xinrong; Qin, Mengying; Bai, Ke; He, Zhiqiang; Yang, Yurong; Li, Xiaofeng; Dai, Qionghai
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 1895
• Page End: 1912
• Publication Date: 2022-12-31
• Subjects: attC site -- site-specific recombination -- machine learning -- combination optimization -- synthetic biology; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2086217
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Site-specific recombination systems are widely used as bioengineering tools. However, the traditional site-specific recombination system requires a consensus sequence for the specific site. Such sequence-level constraints limit effective recombination between sites. Therefore, in order to develop an efficient site-specific recombination system, we investigated the attC site of the bacterial integration subsystem and built a predictive model to infer the important features that contribute to recombination. Here, we design an attC site prediction algorithm based on a combination optimisation strategy. Based on the structural features of attC sites, the prediction algorithm realises the high-precision prediction of the recombination frequencies between sites and the screening of the top 20 important features that play a role in recombination, which are effective for improving the design method of attC sites. The algorithm has better portability and higher prediction accuracy compared with the existing advanced algorithms, among which the Pearson correlation coefficient is 0.87, explained variance score is 0.73, root mean square error is 0.006 and mean absolute error is 0.041. This can not only provide ideas for the research of efficient recombination systems but also provide a theoretical basis for developing genetic engineering further.