ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen...

Area of Science:

• Computational biology
• Metabolic engineering
• Bioinformatics

Background:

• Metabolic pathway prediction is crucial for understanding host-pathogen interactions and identifying potential therapeutic targets.
• Existing methods often rely on extensive reaction databases, limiting predictions for novel or uncharacterized pathways.

Purpose of the Study:

• To introduce a novel algorithm, ASAPP (Architectural Similarity-based Automated Pathway Prediction), for predicting metabolic pathways.
• To enable prediction of metabolic transformations based on metabolite structural similarity, independent of known reaction data.
• To analyze the impact of pathogen-released toxins on host metabolic pathways.

Main Methods:

• ASAPP utilizes the 2D structure and molecular weight of metabolites as input.
• The algorithm identifies probable metabolic transformations by assessing structural similarities between metabolites.
• It was applied to predict host pathway disruptions caused by pathogen toxins.

Main Results:

• ASAPP achieved an accuracy of 85.09% in predicting metabolic pathways.
• The study identified potential breakpoints in host Glycolysis and TCA cycle pathways due to toxin exposure.
• The ASAPP tool is publicly available for use.

Conclusions:

• ASAPP offers a novel, structure-based approach for metabolic pathway prediction, expanding capabilities beyond existing database-dependent methods.
• The algorithm provides insights into host-pathogen metabolic interplay and potential points of pathway disruption.
• This work facilitates the prediction of unknown metabolic transformations and the consequences of toxic insults.