TITLE:
Artificial Neural Network Modeling to Predict the Non-Linearity in Reaction Conditions of Cholesterol Oxidase from Streptomyces olivaceus MTCC 6820
AUTHORS:
Shraddha Sahu, Shailendra Singh Shera, Rathindra Mohan Banik
KEYWORDS:
Cholesterol Oxidase, Artificial Neural Network, Optimization, Streptomyces olivaceus, Prediction
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.7 No.4,
April
11,
2019
ABSTRACT: Cholesterol
oxidase (COX) is widely used enzyme for total cholesterol estimation in human
serum and for the fabrication of electro-chemical biosensors. COX is also used
for the bioconversion of cholesterol; for the production of precursors of
steroidal drugs and hormones. Enzyme activity depends decisively on defined conditions with respect to pH, temperature, ionic
strength of the buffer, substrate concentration, enzyme concentration,
reaction time. Standardization of these parameters is desirable to attain
optimum activity of the enzyme. The present work aims to build a neural network
model using five input parameters (pH,
cholesterol concentration, 4-aminoantipyrine concentration, crude COX
volume and horseradish peroxidase) and one output i.e., COX activity (U/ml) as a response. A feed forward back
propagation neural network with Levenberg-Marquardt training algorithm was used
to train the network. The network performance was assessed in terms of
regression (R2), Mean Squared Error (MSE) and Mean Absolute
Percentage Error (MAPE). A network topology of 5-10-1 was found to be optimum.
The MSE, MAPE and R2 values of the
neural model were 0.0075%, 0.12%
and 0.9792% respectively. The maximum predicted activity of COX was 1.073 U/ml, which was close to
the experimental value i.e., 1.1 U/ml at simulated optimum
assay conditions. MSE and MAPE depicted the precision in the prediction efficiency of the developed ANN
model. Higher R2 value showed a good
correlation between the experimental and ANN predicted values. This proved the
robustness of the ANN model to predict similar type of system (COX from other Streptomyces sp.) within the limits of
the trained data set. The COX activity was enhanced by 1.71 folds after
optimization of the reaction conditions.