A new framework for host-pathogen interaction research.

Area of Science:

• Molecular biology
• Systems biology
• Computational biology

Background:

• COVID-19 outcomes vary due to complex host-pathogen interactions at molecular and cellular levels.
• Understanding these interactions is crucial for disease management and therapeutic development.

Purpose of the Study:

• To propose a systematic framework for understanding molecular host-pathogen interactions (HPIs).
• To develop a computational approach for analyzing HPIs and their relation to disease outcomes.
• To demonstrate the framework's application in COVID-19 research for drug/vaccine design.

Main Methods:

• Formulation of four foundational HPI postulates.
• Development of the HPI Postulate and Ontology (HPIPO) framework using interoperable ontologies.
• Application of HPIPO and CIDO to COVID-19 data, including prediction of host-coronavirus protein-protein interactions (PPIs).

Main Results:

• The HPI postulates provide a basis for understanding HPI dynamics, roles, and critical checkpoints.
• The HPIPO framework enables AI-ready data standardization, sharing, and analysis of HPIs.
• A novel approach for designing drug/vaccine cocktails targeting critical host-coronavirus interaction checkpoints was demonstrated for COVID-19.

Conclusions:

• The proposed postulates and HPIPO framework offer a systematic approach to studying complex host-pathogen interactions.
• This framework facilitates AI-driven analysis and supports the rational design of therapeutics for infectious diseases like COVID-19.
• Further exploration of host-pathogen PPIs using this framework can advance our understanding and treatment strategies.