Artificial intelligence-assisted analysis reveals amino acid effects and interactions on Limosilactobacillus fermentum growth.

Abstract

To understand the growth of lactic acid bacteria (LAB), Limosilactobacillus fermentum, in response to medium compositions, a deep neural network (DNN) was designed using amino acids (AAs) as explanatory variables and LAB growth as the objective variable. Sixty-four different patterns of free AAs were set using an orthogonal array. The best DNN model had high accuracy with low mean square errors and predicted that Asp would affect LAB growth. Bayesian optimization (BO) using this model recommended an optimal growth media comprising maximum amounts of Asn, Asp, Lys, Thr, and Tyr and minimum amounts of Gln, Pro, and Ser. Furthermore, this proposed media was empirically validated to promote LAB growth. The absence of Gln, Ser, and Pro indicates that the different growth trends among the DNN-BO-optimized media were likely caused by the interactions among the AAs and the other components.