Computational approaches in chemical space exploration for carbon fixation pathways

Area of Science:

• Biochemistry
• Computational Chemistry
• Synthetic Biology

Background:

• Chemical space exploration is crucial for discovering and optimizing metabolic pathways, synthetic metabolic functions, and understanding biochemical network evolution.
• Systematic searching of chemical spaces is essential for advancing fields like synthetic biology and metabolic engineering.

Purpose of the Study:

• To systematically search chemical spaces for novel metabolic pathways, focusing on carbon fixation.
• To identify and compare new varieties and recombinations of natural autocatalytic pathways.
• To highlight the utility of graph-based cheminformatics for artificial pathway design.

Main Methods:

• Utilized a graph-based computational approach implemented in MØD cheminformatics software.
• Integrated Integer Linear Programming (ILP) for systematic chemical space searching and optimization.
• Explored the chemical space of natural and artificial carbon fixation pathways based on enzyme reactions.

Main Results:

• Identified novel varieties and recombinations of natural autocatalytic pathways through targeted queries.
• Enabled flexible and targeted searches, including autocatalytic cycles and thermodynamic considerations.
• Demonstrated the discovery of new enzymatic cascades within the explored chemical space.

Conclusions:

• Graph-based cheminformatics, combined with ILP, offers a versatile framework for chemical space exploration and artificial pathway design.
• The developed approach can identify novel pathways with potential applications in carbon capture, agriculture, and chemical production.
• This research contributes to addressing global sustainability challenges through advanced metabolic engineering and pathway design.