Secondary structure prediction involves the application of bioinformatic techniques on the primary structures of proteins and nucleic acids to predict their secondary structure. In the case of protein, it refers to alpha helices, beta sheets and beta turns, while for nucleic acids, it refers to helices and stem-loops.

We performed protein secondary structure prediction using the PSIPred 3.2 server which has average Q3 score of 81.6%. and utilizes neural networks to determine the folding of amino acid based on outputs generated by PSI-Blast.

For proper functionality of a protein, it needs to take up a final and stable 3D structure. This structure, predicted using the primary structure sequence of the protein, is known as the Tertiary Structure.

We performed this using homology modelling approach through the Iterative Threading ASSEmbly Refinement or the I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/).