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Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

  • Emine Guven-Maiorov
  • Chung-Jung Tsai
  • Buyong Ma
  • Ruth Nussinov
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1851)

Abstract

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.

Key words

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

Notes

Acknowledgments

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. This study utilized the high-performance computational capabilities of the Biowulf PC/Linux cluster at the National Institutes of Health (NIH), Bethesda, MD (http://biowulf.nih.gov).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Emine Guven-Maiorov
    • 1
  • Chung-Jung Tsai
    • 1
  • Buyong Ma
    • 1
  • Ruth Nussinov
    • 1
    • 2
  1. 1.Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer ResearchNational Cancer InstituteFrederickUSA
  2. 2.Department of Human Genetics and Molecular Medicine, Sackler Inst. of Molecular Medicine, Sackler School of MedicineTel Aviv UniversityTel AvivIsrael

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