Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

Emine Guven-Maiorov, Chung Jung Tsai, Buyong Ma, Ruth Nussinov*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)—along with their structural details at atomic resolution—may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi’s sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages19
StatePublished - 2019

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745


  • Host-pathogen interaction prediction
  • Interface mimicry
  • Molecular mimicry
  • Protein–protein interaction
  • Structural network
  • Superorganism network


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