AUTHOR=Li Yang , Yapa Madhura M. , Hua Zhihua 

TITLE=A Machine Learning Approach to Prioritizing Functionally Active F-box Members in Arabidopsis thaliana

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.639253

DOI=10.3389/fpls.2021.639253

ISSN=1664-462X

ABSTRACT=Protein degradation through the Ubiquitin (Ub) – 26S Proteasome System (UPS) is a major gene expression regulatory pathway in plants.  In the UPS, a small protein called ubiquitin (only 76 amino acids long) ligates onto a large array of target proteins with the help of three enzymes (E1 activating, E2 conjugating and E3 ligating enzymes) and directs them for turnover in the 26S proteasome complex.  The S-Phase Kinase-Associated Protein 1 (Skp1), CUL1, F-box (FBX) protein (SCF) complexes have been identified as the largest E3 ligase group in plants due to the dramatic number increase of the FBX genes in plant genomes.  Since it is the FBX proteins that recognize and determine the specificity of SCF substrates, much effort has been done to characterize their genomic, physiological and biochemical roles in the past over two decades of functional genomic studies.  However, their large number and high sequence diversities requires innovative approaches to uncover new functions in this group.  To help develop such new tools, we first summarized 82 known FBX genes according to their functions that have been characterized up to date in Arabidopsis thaliana.  Comparing the genomic structure, evolutionary selection, expression patterns, domain compositions, and functional activities between known and unknown FBX gene groups, we developed a neural network machine learning approach to predict active members that are yet unknown in Arabidopsis, thereby facilitating their future functional characterization.