Classification and homology modelling of WRKY transcription factors in Zea maize by computational targeted genome mining

One of the chief concerns of climate change is an escalation in the frequency and severity of abiotic stresses. Low precipitation and high temperatures will result in more frequent and longer spells of droughts in the tropics.  Induction of tolerance to these and other abiotic stresses is the key to increase food production. Tolerance can be incorporated into varieties using conventional breeding methods, marker-assisted selection and transgenic approaches. Computational biology is an important tool to achieve these goals.  WRKY transcription factors so called because of their DNA binding domain has four amino acids tryptophan (W), arginine (R), lysine (K) and tyrosine (Y) are one of the largest families of transcriptional regulators in plants and they have been reported to be involved in response to various abiotic stresses specifically in multiple stressors induced by changing climate.  We hypothesize that it will be possible to associate candidate genes discovered in model species such as Arabidopsis thaliana, with corresponding loci in maize. Potential functional impact of intrinsic genetic information can be effectively predicted from in-depth whole genome data mining for gene sequences and expression. This method combined with comparative homology modelling for identification of potential DNA binding sites in protein products is one of most powerful tools we have for cis/ trans genic improvement of maize. In this study we took up a detailed analysis of the complete maize genome for the similarity/presence of either DNA, mRNA or protein product of At WRKY. We conducted i) BLASTN - nucleotide query to maize nucleotide database; ii) TBLASTX – translated nucleotide query sequence into protein sequences in all six reading frames compared to maize nucleotide database translated on all six reading frames; and iii) TBLASTN - protein query to translated six frames maize nucleotide database. WRKY like proteins were classified in maize genome and phylogenetic trees were constructed using the neighbour-joining method. A homology model was built according to the positive match protein sequence.

A comprehensive search for templates was done in all publicly available structure databases of pdb structures solved by X ray crystallography or NMR. InterPro scan was done with other known protein families, domains, regions, repeats, and sites for identifiable features found in known proteins to be applied to the target protein sequence. Coordinates of the template protein were transferred onto the target sequence to construct the model and DNA docking of the model (Fig.1) with a matrix of homologous interface contacts was done. The resultant putative WRKY was subjected to detailed bioinformatics analysis to ascertain structural and functional similarly to known WRKY. In silico genome mining, prediction and finding by algorithmic approaches in spite of its shortcoming offers an excellent quick start-up for laboratory approaches, thereby reducing time and resource greatly. Stand alone bioinformatics approaches such as the one attempted in this study is  bound to become more accurate in its results as  the size of the databases expand, and will evolve  in future as a  precise predictive science based on omics data mining. Our results with WRKY offer a jumpstart for validation, transformation and marker assisted selection experiments which could pave the way for cis/trans genic improvement of maize especially under the predicted climate change scenario.  

Homology model of a putative WRKY like protein in maize docked with DNA with a matrix of homologous interface contacts