Host–pathogen protein interactions predicted by comparative modeling

Abstract

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.

Shared Molecular Strategies of the Malaria Parasite P. falciparum and the Human Virus HIV-1

“…In addition, we used 691 computationally determined protein interactions from protein structures of the human host and the parasite (23).…”

“…Randomizing such sets of expressed genes and accessible parasite proteins, we found that the emergence of such a set of conserved interactions is a highly nonrandom event as well (p Ͻ 10 Ϫ3 ). Finally, we augmented our set of conserved interactions with experimentally determined host-parasite interactions (21) and interactions that were computationally inferred from protein structure data (23). In total, we assembled 1385 interactions between 379 parasite and 823 host proteins, where the majority of interactions were provided by computationally predicted interactions, followed by experimental observations and conserved interactions ( Fig.…”

“…Furthermore, Dyer et al compared experimentally known interactions of different viruses with the human host (20). The first small map of experimentally determined protein interactions between the human host and the malaria parasite P. falciparum was released recently (21), and was preceded by computational predictions of host-parasite interactions (22,23).…”

“…Combining these interactions with experimentally determined (21) and previously predicted interactions based on protein structural features (22,23), we pooled a large set of host-parasite interactions. Although completely different in their biology and evolution, we find that both HIV-1 and P. falciparum use their protein repertoires in a combinatorial manner.…”

Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction

“…Developing computational methods that identify which PPIs enable a pathogen to infect a host has significant implications in identifying potential therapeutical targets. Davis et al [16] studied ten host-pathogen protein-protein interactions using structural information with a comparative model: the host/pathogen protein pairs that share similarity to protein complexes with known structures are used to build 3-D structural models of putative complexes, and the modelled pairs are then filtered by functional and genomic experimental information. The technique was applied to ten pathogens and assessed by three independent computational procedures.…”