Computational tools for guided discovery and engineering of metabolic pathways

Area of Science:

• Biotechnology and Synthetic Biology
• Computational Chemistry and Cheminformatics

Background:

• Increasing demand for diverse and sustainably synthesized chemicals drives industry interest in biosynthesis.
• Current biosynthesis primarily uses native metabolites or heterologous expression of known pathways.
• A gap exists for producing chemicals with no known biosynthetic routes.

Purpose of the Study:

• To review computational approaches for predicting novel metabolic pathways.
• To address the need for de novo biosynthesis of chemicals.
• To provide accessible tools for scientists without extensive computational experience.

Main Methods:

• Review of computational algorithms for metabolic pathway prediction.
• Approaches utilizing biochemical databases to assemble known reactions.
• Methods employing generalized biochemical rules to predict novel enzymatic reactions.

Main Results:

• Identification of diverse computational strategies for novel pathway discovery.
• Demonstration of feasibility in predicting unobserved enzymatic reactions.
• Highlighting the availability of user-friendly, freely accessible software tools.

Conclusions:

• Computational algorithms are essential for designing biosynthetic routes for novel chemicals.
• Accessible tools empower scientists to explore de novo biosynthesis.
• This facilitates sustainable chemical production and innovation in the chemical industry.