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Activity prediction and molecular mechanism of bovine blood derived angiotensin I-converting enzyme inhibitory peptides.

Zhang T, Nie S, Liu B, Yu Y, Zhang Y, Liu J - PLoS ONE (2015)

Bottom Line: To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion.The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM.The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nutrition and Functional Food, Jilin University, Changchun, Jilin, China.

ABSTRACT
Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking.

No MeSH data available.


Related in: MedlinePlus

The coefficient of determination (square of pearson correlation coefficient) of different structure of BPNN models.
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pone.0119598.g002: The coefficient of determination (square of pearson correlation coefficient) of different structure of BPNN models.

Mentions: There were three layers in the BPNN model (input layer, hidden layer and output layer), while the number of nodes in hidden layer was not certain. So that we trained BPNN models with different number of nodes in hidden layer and different transfer functions to obtain the suitable model structure (Fig. 1, Fig. 2 and S1 Table, S2 Table). For the log-sigmoid & purelin transfer function, the highest MSE appears when the number of hidden layers is twelve. However, in terms of the tan-sigmoid & tan-sigmoid transfer function, the MSE is relatively low when the number of hidden layers is between four and fifteen, and the lowest MSE (0.0587 ± 0.0351) is obtained when it reaches seven. Besides, seven hidden layers with the log-sigmoid & purelin transfer function bring the highest determination coefficient (square of R, 0.3819 ± 0.2781). Therefore, we decided to select 10–7–1 as the topological structure of BPNN and the tan-sigmoid transfer function as the transfer function between both input layer & hidden layer and hidden & output layer. To improve the accuracy of the final model, we set the aim as R > 0.9 and trained 10–7–1 BPNN with tan-sigmoid transfer function & tan-sigmoid transfer function several times. The MSE and correlation coefficients of this model were in acceptable ranges as shown in Table 4. These correlation coefficients indicate that there is a strong correlation between the predicted and experimental result and the MSE (0.162) is acceptable. The plots of experimental versus predicted values (Fig. 3) confirmed the discussed results. Hence, we chose this one as the final model. To our knowledge, it was the first time applying BPNN to predict the IC50 of ACE of pentapeptide. In our previous work [58], a BPNN model was built to predict the IC50 of ACE of tripeptide. The MSE of the tripeptide model (0.2148) is higher than this pentapeptide model (0.0162). Meanwhile, the R of the tripeptide model (0.854) was less than this new model (0.9176). These differences possibly resulted from the difference of descriptors.


Activity prediction and molecular mechanism of bovine blood derived angiotensin I-converting enzyme inhibitory peptides.

Zhang T, Nie S, Liu B, Yu Y, Zhang Y, Liu J - PLoS ONE (2015)

The coefficient of determination (square of pearson correlation coefficient) of different structure of BPNN models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358945&req=5

pone.0119598.g002: The coefficient of determination (square of pearson correlation coefficient) of different structure of BPNN models.
Mentions: There were three layers in the BPNN model (input layer, hidden layer and output layer), while the number of nodes in hidden layer was not certain. So that we trained BPNN models with different number of nodes in hidden layer and different transfer functions to obtain the suitable model structure (Fig. 1, Fig. 2 and S1 Table, S2 Table). For the log-sigmoid & purelin transfer function, the highest MSE appears when the number of hidden layers is twelve. However, in terms of the tan-sigmoid & tan-sigmoid transfer function, the MSE is relatively low when the number of hidden layers is between four and fifteen, and the lowest MSE (0.0587 ± 0.0351) is obtained when it reaches seven. Besides, seven hidden layers with the log-sigmoid & purelin transfer function bring the highest determination coefficient (square of R, 0.3819 ± 0.2781). Therefore, we decided to select 10–7–1 as the topological structure of BPNN and the tan-sigmoid transfer function as the transfer function between both input layer & hidden layer and hidden & output layer. To improve the accuracy of the final model, we set the aim as R > 0.9 and trained 10–7–1 BPNN with tan-sigmoid transfer function & tan-sigmoid transfer function several times. The MSE and correlation coefficients of this model were in acceptable ranges as shown in Table 4. These correlation coefficients indicate that there is a strong correlation between the predicted and experimental result and the MSE (0.162) is acceptable. The plots of experimental versus predicted values (Fig. 3) confirmed the discussed results. Hence, we chose this one as the final model. To our knowledge, it was the first time applying BPNN to predict the IC50 of ACE of pentapeptide. In our previous work [58], a BPNN model was built to predict the IC50 of ACE of tripeptide. The MSE of the tripeptide model (0.2148) is higher than this pentapeptide model (0.0162). Meanwhile, the R of the tripeptide model (0.854) was less than this new model (0.9176). These differences possibly resulted from the difference of descriptors.

Bottom Line: To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion.The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM.The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nutrition and Functional Food, Jilin University, Changchun, Jilin, China.

ABSTRACT
Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking.

No MeSH data available.


Related in: MedlinePlus