Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Zhou, Hufeng; Rezaei, J.; Hugo, Willy; Gao, Shumei; Jin, Jingjing; Fan, Mengyuan; Yong, Chern Han; Woźniak, Michał; Wong, Limsoon

doi:10.1186/1752-0509-7-s6-s6

Cited by 33 publications

(35 citation statements)

References 36 publications

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“…As this homology based approach is tied to the template interaction network, it is confined to the set of reported physical PPIs and thus also inherits the set false positives from the template network. Therefore, an interesting complementary approach would be the investigation of an approach based on domain–domain interactions ( Zhou et al, 2013b ). This would eliminate the necessity of homology for the predicted interactors, as it only requires the presence of the interacting domains.…”

Section: Discussionmentioning

confidence: 99%

Integrated inference and evaluation of host–fungi interaction networks

et al. 2015

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Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data.

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Section: Discussionmentioning

confidence: 99%

Integrated inference and evaluation of host–fungi interaction networks

et al. 2015

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“…To reduce false predictions, we counter-checked all our data by domain-domain interactions (DDI). DDI are often used as an approach independent from sequence homology-based methods to predict protein-protein interaction networks and thus strongly reduce the number of false positives 7,26,27 . Generally, some of the PPIs are achieved via interactions between short motifs that are often transient interactions 28 .…”

Section: Resultsmentioning

confidence: 99%

Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs

Gupta

Srivastava²,

Osmanoğlu³

et al. 2020

Sci Rep

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Apart from some model organisms, the interactome of most organisms is largely unidentified. Highthroughput experimental techniques to determine protein-protein interactions (ppis) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.understanding of PPIs in various organisms [11][12][13][14] . Here we used domain information, subcellular localization and isoform information to filter the preliminary global PPI network of C. floridanus reconstructed on stringent interolog based criteria. We focus on interactions predicted with high confidence to reduce noise. This conservative approach rejects 79.1% of the preliminary predicted interactions. We then explored the topologically important and evolutionary conserved proteins by analyzing the reconstructed interactome regarding cellular functions. Scientific RepoRtS |(2020) 10:2334 | https://doi.Assigning the confidence score. In fact, the preliminary network is filtered successively as mentioned above to reconstruct the final network, in this way the final network is already of high-confidence as many network biologists working on PPI networks have used DDIs and subcellular localization either to increase confidence or validate the interacting pairs. Here additionally we used topology-based method CAPPIC (cluster-based assessment of protein-protein interaction confidence) to assign the interaction confidence score 31,69 in the filtered network. In brief, CAPPIC calculations are based on the assumption that the proteins existing in the same network module are expected to have a higher number of common neighbours (neighbourhood interconnectedness 70 ), and a short path length inbetween 71 . For scoring the confidence level, CAPPIC first performs the clustering of the network using a robust clustering algorithm, Markov Cluster (MCL) 72 and then scores the interactions according to their level of compliance with the basic assumptions of topology-based methods. For the clustering we used an MCL inflation value of 1.5. Scores were classified to three subsets; low confidence score between 0 to 0.3, medium confidence score betwe...

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“…For example, the joint similarity metric could be calculated based on only those positions in the sequence alignment that correspond to the directly interacting residues in the template complex (as was done in [70], for example). This of course implicitly assumes that the interfacial residues mediating a domain-domain interaction in different protein structures all line up when the corresponding sequences are aligned, and thus, only these residues would be relevant for making comparisons.…”

Section: Discussionmentioning

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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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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Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Cited by 33 publications

References 36 publications

Integrated inference and evaluation of host–fungi interaction networks

Integrated inference and evaluation of host–fungi interaction networks

Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs

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

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