scholarly journals Techniques for transferring host-pathogen protein interactions knowledge to new tasks

2015 ◽  
Vol 6 ◽  
Author(s):  
Meghana Kshirsagar ◽  
Sylvia Schleker ◽  
Jaime Carbonell ◽  
Judith Klein-Seetharaman
Keyword(s):  
Protein Interactions ◽  
Host Pathogen ◽  
Pathogen Protein

scholarly journals Training host-pathogen protein–protein interaction predictors

2018 ◽  
Vol 16 (04) ◽  
pp. 1850014 ◽  
Author(s):  
Abdul Hannan Basit ◽  
Wajid Arshad Abbasi ◽  
Amina Asif ◽  
Sadaf Gull ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Vital Role ◽  
New Drugs ◽  
Generalization Performance ◽  
Detection Techniques ◽  
Sample Weights ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Training Examples

Detection of protein–protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host–pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor .

scholarly journals Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

2015 ◽  
Vol 90 (4) ◽  
pp. 1973-1987 ◽  
Author(s):  
Stacy L. DeBlasio ◽  
Juan D. Chavez ◽  
Mariko M. Alexander ◽  
John Ramsey ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Host Protein ◽  
Host Proteins ◽  
Content Type ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Binding Interfaces

ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.IMPORTANCEThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

High-throughput proteomics and the fight against pathogens

2016 ◽  
Vol 12 (8) ◽  
pp. 2373-2384 ◽  
Author(s):  
Anita Horvatić ◽  
Josipa Kuleš ◽  
Nicolas Guillemin ◽  
Asier Galan ◽  
Vladimir Mrljak ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Animal Welfare ◽  
High Throughput ◽  
Protein Interactions ◽  
Rapid Development ◽  
Protein Protein Interactions ◽  
Major Threat ◽  
Host Pathogen ◽  
Pathogen Protein

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.

scholarly journals Document classification for mining host pathogen protein–protein interactions

2010 ◽  
Vol 49 (3) ◽  
pp. 155-160 ◽  
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

scholarly journals Issues in performance evaluation for host–pathogen protein interaction prediction

2016 ◽  
Vol 14 (03) ◽  
pp. 1650011 ◽  
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.

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

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/.

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

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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