Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions

Pathogens pose a major threat to human and animal welfare. Understanding the interspecies host–pathogen protein–protein interactions could lead to the development of novel strategies to combat infectious diseases through the rapid development of new therapeutics.

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Document classification for mining host pathogen protein–protein interactions

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2010.04.003 ◽

2010 ◽

Vol 49 (3) ◽

pp. 155-160 ◽

Cited By ~ 13

Author(s):

Lanlan Yin ◽

Guixian Xu ◽

Manabu Torii ◽

Zhendong Niu ◽

Jose M. Maisog ◽

...

Keyword(s):

Protein Interactions ◽

Document Classification ◽

Protein Protein Interactions ◽

Host Pathogen ◽

Pathogen Protein

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Issues in performance evaluation for host–pathogen protein interaction prediction

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500116 ◽

2016 ◽

Vol 14 (03) ◽

pp. 1650011 ◽

Cited By ~ 9

Author(s):

Wajid Arshad Abbasi ◽

Fayyaz Ul Amir Afsar Minhas

Keyword(s):

Machine Learning ◽

Protein Interactions ◽

Cross Validation ◽

Protein Protein Interactions ◽

Evaluation Scheme ◽

Host Pathogen ◽

Pathogen Protein ◽

Protein Interaction Prediction ◽

Underlying Mechanisms ◽

Fold Cross Validation

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.

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GreeningDB: A Database of Host–Pathogen Protein–Protein Interactions and Annotation Features of the Bacteria Causing Huanglongbing HLB Disease

International Journal of Molecular Sciences ◽

10.3390/ijms221910897 ◽

2021 ◽

Vol 22 (19) ◽

pp. 10897

Author(s):

Cristian D. Loaiza ◽

Naveen Duhan ◽

Rakesh Kaundal

Keyword(s):

Protein Interactions ◽

Network Visualization ◽

High Yield ◽

Protein Protein Interactions ◽

Citrus Industry ◽

Citrus Greening Disease ◽

Host Pathogen ◽

Pathogen Protein ◽

Central Resource ◽

User Friendly

The Citrus genus comprises some of the most important and commonly cultivated fruit plants. Within the last decade, citrus greening disease (also known as huanglongbing or HLB) has emerged as the biggest threat for the citrus industry. This disease does not have a cure yet and, thus, many efforts have been made to find a solution to this devastating condition. There are challenges in the generation of high-yield resistant cultivars, in part due to the limited and sparse knowledge about the mechanisms that are used by the Liberibacter bacteria to proliferate the infection in Citrus plants. Here, we present GreeningDB, a database implemented to provide the annotation of Liberibacter proteomes, as well as the host–pathogen comparactomics tool, a novel platform to compare the predicted interactomes of two HLB host–pathogen systems. GreeningDB is built to deliver a user-friendly interface, including network visualization and links to other resources. We hope that by providing these characteristics, GreeningDB can become a central resource to retrieve HLB-related protein annotations, and thus, aid the community that is pursuing the development of molecular-based strategies to mitigate this disease’s impact. The database is freely available at http://bioinfo.usu.edu/GreeningDB/.

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The structural evolution of host-pathogen protein interactions: an integrative approach

10.1101/581637 ◽

2019 ◽

Author(s):

Anderson F. Brito ◽

John W. Pinney

Keyword(s):

Protein Interactions ◽

Homology Modelling ◽

Structural Evolution ◽

Integrative Approach ◽

Binding Affinities ◽

Protein Protein Interactions ◽

Host Pathogen ◽

Cell Membrane Proteins ◽

Pathogen Protein ◽

History Of

ABSTRACTThe evolution of protein-protein interactions (PPIs) is directly influenced by the evolutionary histories of the genes and the species encoding the interacting proteins. When it comes to PPIs of host-pathogen systems, the complexity of their evolution is much higher, as two independent, but biologically associated entities, are involved. In this work, an integrative approach combining phylogenetics, tree reconciliations, ancestral sequence reconstructions, and homology modelling is proposed for studying the evolution of host-pathogen PPIs. As a case study, we analysed the evolution of interactions between herpesviral glycoproteins gD/gG and the cell membrane proteins nectins. By modelling the structures of more than 12,000 ancestral states of these virus-host complexes it was found that in early times of their evolution, these proteins were unable to interact, most probably due to electrostatic incompatibilities between their interfaces. After the event of gene duplication that gave rise to a paralog of gD (known as gG), both protein lineages evolved following distinct functional constraints, with most gD reaching high binding affinities towards nectins, while gG lost such ability, most probably due to a process of neofunctionalization. Based on their favourable interaction energies (negative ΔG), it is possible to hypothesize that apart from nectins 1 and 2, some alphaherpesviruses might also use nectins 3 and 4 as cell receptors. These findings show that the proposed integrative method is suitable for modelling the evolution of host-pathogen protein interactions, and useful for raising new hypotheses that broaden our understanding about the evolutionary history of PPIs, and their molecular functioning.

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