Computational approaches for prediction of pathogen-host protein-protein interactions

Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

doi:10.3389/fmicb.2015.00094

Cited by 102 publications

(90 citation statements)

References 67 publications

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“…More recently, attention has been turned to the problem of predicting PPIs between pathogenic microbes and the human host towards gaining a better understanding of infectious diseases [23, 24]. Comparative methods based on local sequence alignment [31–34] are bound to yield a significant proportion of false positives, or negatives, unless structural (domain-level) details about the template interactions are also properly taken into account.…”

Section: Discussionmentioning

confidence: 99%

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Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Mahajan

Mande

2017

BMC Bioinformatics

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BackgroundA comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved.ResultsWe propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions.ConclusionsOur method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1550-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…Computational methods can complement and aid experimental approaches by helping to predict, or prioritize, potential interactions which could guide wet lab studies. Indeed, bioinformatic, or in silico, prediction of human-microbial PPIs has emerged as an active area of research in recent years [23, 24]. …”

Section: Introductionmentioning

confidence: 99%

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Mahajan

Mande

2017

BMC Bioinformatics

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“…Even among highly connected proteins (degree k>5) in the human interactome, the degree of virus targets was twice as large as those of nontargeted proteins. Independent analyses found that viral proteins preferably target human host proteins that are involved in a large number of interactions [14,49,50,[54][55][56][57][58][59][60]. As the number of interactions is a local measure of centrality, other more global measures of centrality were considered as well.…”

Section: What Are the Protein Targets Of Human Viruses?mentioning

confidence: 99%

Protein-protein interactions of human viruses

Goodacre

Devkota

Bae

et al. 2020

Seminars in Cell & Developmental Biology

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Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

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“…Available computational tools to predict host-pathogen interactions have been recently reviewed by Nourani et al . [93]. Current methods mostly depend on global sequence and structure homology.…”

Section: Introductionmentioning

confidence: 99%

Structural host-microbiota interaction networks

2017

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Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

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Computational approaches for prediction of pathogen-host protein-protein interactions

Cited by 102 publications

References 67 publications

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Protein-protein interactions of human viruses

Structural host-microbiota interaction networks

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