Open AccessPyMiner: A method for metabolic pathway design based on the uniform similarity of substrate-product pairs and conditional search
Author(s) -
Xiaoning Song,
Mingyu Dong,
Min Liu
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0266783
Subject(s) - metabolic pathway , metabolic network , similarity (geometry) , product (mathematics) , computer science , natural product , metabolic engineering , substrate (aquarium) , computational biology , chemical similarity , biochemical engineering , biological system , bioinformatics , artificial intelligence , chemistry , biology , mathematics , biochemistry , engineering , structural similarity , enzyme , ecology , geometry , image (mathematics)
Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.