Statistical methods and its applications in Whey protein binding (Bos Taurus)

Abstract

Milk is made up of two proteins, such as casein (80%) and whey (20%). These proteins, which include membrane globular proteins, glycoproteins, and lipoproteins, are mostly found in the outer layer of milk fat globule membranes. WP can be separated from the casein in milk or formed as a byproduct of cheese making and contains all nine essential amino acids with a low lactose content. Whey protein has many health benefits, including improving strength and body composition, promoting weight loss, boosting metabolism, stifling hunger, maintaining good health, avoiding nourishing the bones, reducing inflammation, promoting quick wound healing, lowering blood pressure, repairing and preserving muscle tissues etc. Many dairy and food-based industries eagerly explore the accurate retention time for the formation of health related new byproducts. As of date, there are very few studies available in the world literature citation database and too many micro and macro studies on WP are currently being undertaken globally, they provide insight into the binding of particular amino acids (Seq. peptides) and their accurate lag time of binding to the formation of "helixes and sheaths. In this paradigm, statistical tool are very important to determine average time and WP algorithms for analysis of seq amino acids and the metabolic pathway's action. Overall, the present research attempts to extrapolate the Seq peptide's binding capacity and its retention time for amino acids in order to fill the aforementioned research gap. For the model demonstration we used the commonly cited whey protein peptide (Seq) NCBS, which was considered to prove the hypothesis. The Markova two state random and Monte Carlo simulation (superimposed artificial neural network) models were formulated to extract transient probabilities and retention times of Seq peptides. As per the results, the binding capacity was found to be excellent with a good retention time (5 to 11.50 minutes) in cross-linked and unlinked cases (10 to 13.50 minutes). Bioinformatics, the dairy industry, and life scientists will find these more recent discoveries to be very helpful for measuring likelihoods and transient probabilities of binding.