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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic
1Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, Karnataka, India.
Abstract:
Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases, DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 -Escherichia coli and phage lambda -E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions.
Insights
Predicting host-pathogen protein-protein interactions aids disease research. This study introduces a computational method using homology detection to identify novel interactions, including those involving hypothetical proteins, accelerating molecular understanding of diseases.
Area of Science:
- Computational Biology
- Infectious Disease Research
- Protein Interaction Networks
Background:
- Understanding host-pathogen interactions is crucial for deciphering disease mechanisms.
- Experimental methods for large-scale interaction detection are limited, hindering progress.
- Computational approaches are needed to predict these interactions efficiently.
Purpose of the Study:
- To develop and apply a simple method for predicting protein-protein interactions between host and pathogen organisms.
- To identify novel host-pathogen interactions relevant to disease processes.
- To explore the potential functions of hypothetical proteins through predicted interactions.
Main Methods:
- Utilized homology detection approaches.
- Cross-referenced against protein-protein interaction databases (DIP and iPfam).
- Applied the method to test cases (phage T4-E. coli, phage lambda-E. coli) and human-pathogen pairs (E. coli, S. enterica typhimurium, Y. pestis).
Main Results:
- Successfully recognized previously known interactions in test cases.
- Identified several novel interactions between human proteins and pathogen proteins.
- Discovered numerous predicted interactions involving hypothetical proteins, suggesting potential functions.
Conclusions:
- The developed computational method is effective for predicting host-pathogen protein-protein interactions.
- The identified novel interactions, especially those with hypothetical proteins, offer valuable insights into disease mechanisms.
- This approach can accelerate molecular understanding of diseases by revealing previously unknown functional relationships.
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