Predicting Protein–Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches

Zheng, Cunjian; Liu, Yuan; Sun, Fangnan; Zhao, Lingxia; Zhang, Lida

doi:10.3389/fpls.2021.690124

Cited by 11 publications

(9 citation statements)

References 53 publications

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“…The limitation of Ma et al.’s work was that the developed machine learning model was not tested with an independent dataset and other host–pathogen systems. Zheng et al. (2021) presented a computer methodology for structurally based plant–pathogen PPI prediction in rice and fungus.…”

Section: Resultsmentioning

confidence: 99%

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

et al. 2023

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IntroductionPlant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.MethodsIn this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.Results and DiscussionA total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

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Section: Resultsmentioning

confidence: 99%

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

et al. 2023

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“…For instance, Zheng et al. predicted PPI between rice and blast fungus based on interolog mapping of tertiary protein structures [80]. Yang et al.…”

Section: Computational Prediction Of Ppismentioning

confidence: 99%

“…al. predicted PPI between rice and blast fungus based on interolog mapping of tertiary protein structures[80]. Yang et al developed PlaPPISite for 13 plant interactomes including PPI predictions from interolog mapping and homology modelling[81].…”

mentioning

confidence: 99%

Protein‐protein interaction networks as miners of biological discovery

Wang

Lü

et al. 2022

Proteomics

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Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and coimmunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.

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“…Another approach for host–pathogen prediction uses a series of filters such as sequence alignment, domain–domain interaction and biological context or functional annotation ( Huo et al , 2015 ). A similar method for plant-pathogen PPIs prediction also extracts features from sequence comparison and domain–domain interactions, but also structural features such as TM-Score ( Zhang and Skolnick, 2005 ), RMSD (Root Mean Square Distance) among interacting residues, and fraction and several residue contacts preserved in the interaction models ( Zheng et al , 2021 ). All these methods do not provide strategies to generate new potential PPIs for a desired organism.…”

Section: Introductionmentioning

confidence: 99%

PPIntegrator: semantic integrative system for protein–protein interaction and application for host–pathogen datasets

Martins

Ziviani²,

Costa

et al. 2023

Bioinformatics Advances

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Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential Host-Pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare Host-Pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. Availability: https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin

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Predicting Protein–Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches

Cited by 11 publications

References 53 publications

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

Protein‐protein interaction networks as miners of biological discovery

PPIntegrator: semantic integrative system for protein–protein interaction and application for host–pathogen datasets

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