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Host–pathogen protein interactions predicted by comparative modeling

¹ Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, California 94158, USA
² Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California 94158, USA
³ California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California 94158, USA
⁴ Graduate Group in Biological and Medical Informatics, University of California at San Francisco, San Francisco, California 94158, USA
⁵ Department of Pathology and Sandler Center for Basic Research in Parasitic Diseases, University of California at San Francisco, San Francisco, California 94158, USA

(RECEIVED September 6, 2007; FINAL REVISION September 20, 2007; ACCEPTED September 21, 2007)

Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host–pathogen interactions involve protein–protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host–pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host–pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host–pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host–pathogen protein interactions.

Keywords: host–pathogen interactions; protein–protein interactions; comparative modeling; protein interaction prediction; neglected tropical diseases

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