Salinity stress adversely affects the plant growth and is a major constraint to agriculture. In the present study, we studied the role of plant growth promoting rhizobacterium (PGPR) Enterobacter cloacae SBP-8 possessing ACC deaminase activity on proteome profile of wheat (Triticum aestivum L.) under high salinity (200 mM NaCl) stress. The aim of study was to investigate the differential expressed protein in selected three (T-1, T-2, T-3) treatments and absolute quantification (MS/MS analysis) was used to detect statistically significant expressed proteins. In this study, we investigated the adaptation mechanisms of wheat seedlings exposed to high concentration of NaCl treatment (200 mM) for 15 days in response to bacterial inoculation based on proteomic data. The identified proteins were distributed in different cellular, biological and molecular functions. Under salt stress, proteins related to ion-transport, metabolic pathway, protein synthesis and defense responsive were increased to a certain extent. A broader comparison of the proteome of wheat plant under different treatments revealed that changes in some of the metabolic pathway may be involved in stress adaption in response to PGPR inoculation. Hierarchical cluster analysis identified the various up-regulated/down-regulated proteins into tested three treatments. Our results suggest that bacterial inoculation enhanced the ability of wheat plant to combat salt stress via regulation of transcription factors, promoting antioxidative activity, induction of defense enzymes, lignin biosynthesis, and acceleration of protein synthesis.

Functional characterization of proteins being one of the major issues in molecular biology is still unsolved due to several resource and technical limitations of experimental structure determination methods. A suitable methodology for accurate prediction of protein confirmations simply from sequence is therefore emerging as the primary modeling goal of researchers today. Global blind protein structure prediction summit, entitled Critical Assessment of Structure Prediction (CASP), critically assesses the modeling methodologies, to track our algorithmic path development. But our success is still impeded by incompetent modeling methodologies and several key technical lacunas. There is still a great need to focus some key issues for bridging the major though considered trivial gaps, in the upcoming CASP to pave and demarcate our correct way of developing a consistently accurate prediction methodology in the near future. Major problems resulting in divergence of our predicted models from their actual native states are thus highlighted with suggested more stringent and reliable assessment considerations in the CASP test.

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.

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