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With the increasing numbers of known protein structures and greater accuracy of ultra-high resolution protein structures in structural biology, high fidelity conformational information is used to explore the validation of three-dimensional protein structures using Ramachandran plots. In proteins structural determination method, amino acid residues (as nodes) and the close contact between the residues (as edges) have been used to explore basic network properties to study protein folding, its structural stability and for prediction of catalytic sites. In this study, in silico analysis and Ramachandran plot analysis of NifA protein using nitrogen-fixing Azorhizobium caulinodans was carried out on the basis of 3Drefine results. The percentage distribution of amino acids in different regions was recorded in I-TASSER and Raptor X models using Ramachandran plot analysis. In I-TASSER model, 79.9% of amino acid residues resided in most favoured red region and only 15.7% amino acid residues were found in the allowed (yellow region), out of total 523 amino acids analyzed in this model. On the other hand, 90.5% amino acid residues resided in most favoured (red) region in Raptor X model out of total 433 amino acids analyzed in this model. Only 6.7% amino acid residues were found in additional allowed (yellow) region, whereas only 1.2% residues were observed in generously allowed region. Thus, the number of amino acid residues belonging to “outlier, allowed, and favored” regions in Ramachandran plot analysis represents best quality metrics of experimental structure models before structure deposition.

Ramachandran plot, NifA protein, Nitrogen fixation, I-TASSER model, Raptor X model, Amino acids, Structural stability

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Divya Sindhu, S.K. Yadav, "Ramachandran plot analysis: Conformational validation of NifA protein structure in nitrogen-fixing Azorhizobium caulinodans," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.116-121, 2020.

This paper focuses on drought forecasting, using Artificial Neural Network (ANN) and predicts the values of drought condition derived using Rainfall data of Indore (M.P). We have used the Rainfall data as input data of ANN model for drought forecasting, and determine Standardized Precipitation Index (SPI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

Artificial Neural Networks (ANNs), SPI.

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Rajesh Kumar Sharma, Mayank Rajput, Rahul Sharma, "Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Drought Condition Derived using Rainfall Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.6, pp.122-125, 2020.