Predicting and clustering plant CLE genes with a new method developed specifically for short amino acid sequences

BMC Genomics

Melis Kucukoglu¹  Zhe Zhang²  Lei Liu²  Robert M. Larkin²  Bo Zheng²  Xueping Shi²  Dongdong Tian² 

[1] Institute of Biotechnology, Helsinki Institute of Life Science (HILIFE), University of Helsinki, 00014, Helsinki, Finland;Viikki Plant Science Centre, University of Helsinki, 00014, Helsinki, Finland;Key Laboratory of Horticultural Plant Biology of Ministry of Education, Huazhong Agricultural University, 430070, Wuhan, China;College of Horticulture and Forestry Sciences, Huazhong Agricultural University, 430070, Wuhan, China;

关键词: Peptide hormone;    CLE;    Machine learning;    Euclidean distance;    Gene prediction;    Gene clustering;    Evolution;   

DOI  :  10.1186/s12864-020-07114-8

来源: Springer

【 摘 要 】

BackgroundThe CLV3/ESR-RELATED (CLE) gene family encodes small secreted peptides (SSPs) and plays vital roles in plant growth and development by promoting cell-to-cell communication. The prediction and classification of CLE genes is challenging because of their low sequence similarity.ResultsWe developed a machine learning-aided method for predicting CLE genes by using a CLE motif-specific residual score matrix and a novel clustering method based on the Euclidean distance of 12 amino acid residues from the CLE motif in a site-weight dependent manner. In total, 2156 CLE candidates—including 627 novel candidates—were predicted from 69 plant species. The results from our CLE motif-based clustering are consistent with previous reports using the entire pre-propeptide. Characterization of CLE candidates provided systematic statistics on protein lengths, signal peptides, relative motif positions, amino acid compositions of different parts of the CLE precursor proteins, and decisive factors of CLE prediction. The approach taken here provides information on the evolution of the CLE gene family and provides evidence that the CLE and IDA/IDL genes share a common ancestor.ConclusionsOur new approach is applicable to SSPs or other proteins with short conserved domains and hence, provides a useful tool for gene prediction, classification and evolutionary analysis.

【 授权许可】

CC BY