Document Type : Original Research Article

Authors

1 Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran

2 Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran,

10.22034/HBB.2023.03

Abstract

This article studied applicabilities of Artificial Neural Networks (ANN), aiming at partitioning of 
Asparaginase (ASP). The first step investigated the partitioning of ASP using ATPS, consisting of 
polyethylene glycol and different inorganic salts. The studies of ASP partition were conducted by 
measuring the amounts of ASP in the upper and lower phase, followed by calculating the recovery. 
PEG of 6000 and (NH4)2SO4 provided the best performance with recovery yield of 86 %. The 
partitioning behavior of the approach was investigated in the presence of an impurity kind of 
amylase. Related partition coefficient of 0.21 and recovery yield of ASP of 86 % were calculated. 
A Circular Dichroism (CD) spectroscopy compared the structure of standard and partitioned ASP, 
which remained stable. The relative activity was determined to be 94 %. A feed-forward neural 
network was applied to predict the best model to partition of ASP.

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