In-Silico Approaches for Studying the MAP Kinase Signaling Pathways Involved in Resistance Against Alternaria Blight in Brassica

Abstract

The most devastating fungal disease of Brassica is Black spot or Alternaria blight caused by Alternaria brassicae. Alternaria brassicae causes the disease through the production of host specific toxin viz. chlorotic and necrotic toxins, which are responsible for the formation of chlorotic, necrotic and green islands like disease symptoms in susceptible Brassica leaves. Signal transduction is the means by which cells respond to extracellular information. Mitogen- activated protein kinase (MAPK) cascades are important signaling modules in eukaryotic cells. MAPK are important intermediates in signal transduction pathways that are initiated by many types of cell surface receptors. They function Downstream of sensors/receptors and regulate cellular responses to external and endogenous stimuli. Mitogen activated kinase, ensures their specificity of action by interacting with its different substrates through docking domains. Specific docking interactions between MAPKs and its substrate are crucial for efficient and accurate signal transmission.

Recent studies have shown that disease resistance against any pathogen in plant is governed by combinatorial interaction between proteins. Therefore, now a day’s much effort has gone into finding the complete set of interacting protein. Genome wide protein-protein interaction network have been realized by using high throughput methods like yeast two hybrid system and affinity immunochip based system. Some groups studied the partner of MAPK by using phosphoproteomics approach. However these methods are cost effective, require good infrastructure and contain high false positive result.

The need of in silico methods for protein-protein interaction prediction is being driven by above listed limitation and also by the generation of sequences at a rate far beyond our ability to carry out experimental functional analysis.

In this chapter we are trying to give insight about different in silico approaches e.g. Machine Learning Approaches viz., Hidden Markov Model (HMM), Neural Network (NN), Support Vector Machine (SVM) etc., Direct Sequence Analysis approach that takes into account surface information for predicting protein-protein interaction, Docking studies which attempts to use geometric and steric considerations to fit two proteins of known structure into a bound complex,(iv) through Phylogenetic profiling. By using some of these methods we studied the MAPK interaction with upstream or downstream proteins viz WRKY protein, Myb protein, bZIP, NAC, MAPKK, Phosphatases and some other defence proteins. The main advantages of these in-silico approaches are less expensive, provide result in less time and the precision and accuracy are also much better than the conventional wet lab methods.