Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network

Khorsand, Babak; Savadi, Abdorreza; Zahiri, Javad; Naghibzadeh, Mahmoud

doi:10.3934/mbe.2020176

Cited by 19 publications

(20 citation statements)

References 55 publications

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“…Random forest was the approach followed by both Yang [ 25 ] and Barman [ 26 ]. The approach followed by Leite [ 27 ], Zahiri [ 28 ], and Mei [ 29 ], were k-nearest neighbors, multilayer perceptron, and AdaBoost, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, researchers work on different viruses to predict their PPI networks. Zhao predicted HIV1-human PPI network [ 28 ], Khorsand uncovered Alphainfluenzavirus-human PPI network [ 29 ], Ray predicted HCV-human PPI network [ 30 ], Chan revealed west nile virus-human PPI network [ 31 ], and Duran worked on herpesvirus-human PPI network [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Features to learn a model is another view to look at predicting virus-host PPI network. Alguwaizani used repeating patterns and amino acids composition [ 33 ], Kösesoy used location based encoding of amino acids [ 34 ], Mir used structure similarity [ 35 ], Guven-Maiorov used interface similarity [ 36 ], and Khorsand used network topology and gene ontology [ 29 ] in their researches towards learning PPI networks.…”

Section: Introductionmentioning

confidence: 99%

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SARS-CoV-2-human protein-protein interaction network

Khorsand

Savadi

Naghibzadeh

2020

Informatics in Medicine Unlocked

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SARS-CoV-2-human protein-protein interaction network

Khorsand

Savadi

Naghibzadeh

2020

Informatics in Medicine Unlocked

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“…Billions of dollars are spent to reveal the way hosts are infected by pathogens and their presumptive victims. Host-Pathogen protein-protein interactions can be the best clue for initiating infection which have been studied in different pathogens [1][2][3][4][5][6][7][8]. Exploring molecular functions, biological processes, cellular compartment, common pathways and the other properties of host proteins targeted by pathogens can help us in infectious disease inhibition.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive host-pathogen protein-protein interaction network analysis

2020

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Background Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. Results As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins’ marker. Conclusions Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens’ favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

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“…Usually, machine learning predictors that are based on a diverse feature set achieve higher classification accuracy than traditional, solely sequence similarity based methods [ 9 , 10 ]. Several ML systems for PPI prediction have been developed, using a variety of learning methods and feature sets [ 5 , 11 – 18 ]. A specific case of a PPI is a cytokine–receptor interaction (CRI).…”

Section: Introductionmentioning

confidence: 99%

Improved cytokine–receptor interaction prediction by exploiting the negative sample space

Nath

Leier

2020

BMC Bioinformatics

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Background Cytokines act by binding to specific receptors in the plasma membrane of target cells. Knowledge of cytokine–receptor interaction (CRI) is very important for understanding the pathogenesis of various human diseases—notably autoimmune, inflammatory and infectious diseases—and identifying potential therapeutic targets. Recently, machine learning algorithms have been used to predict CRIs. “Gold Standard” negative datasets are still lacking and strong biases in negative datasets can significantly affect the training of learning algorithms and their evaluation. To mitigate the unrepresentativeness and bias inherent in the negative sample selection (non-interacting proteins), we propose a clustering-based approach for representative negative sample selection. Results We used deep autoencoders to investigate the effect of different sampling approaches for non-interacting pairs on the training and the performance of machine learning classifiers. By using the anomaly detection capabilities of deep autoencoders we deduced the effects of different categories of negative samples on the training of learning algorithms. Random sampling for selecting non-interacting pairs results in either over- or under-representation of hard or easy to classify instances. When K-means based sampling of negative datasets is applied to mitigate the inadequacies of random sampling, random forest (RF) together with the combined feature set of atomic composition, physicochemical-2grams and two different representations of evolutionary information performs best. Average model performances based on leave-one-out cross validation (loocv) over ten different negative sample sets that each model was trained with, show that RF models significantly outperform the previous best CRI predictor in terms of accuracy (+ 5.1%), specificity (+ 13%), mcc (+ 0.1) and g-means value (+ 5.1). Evaluations using tenfold cv and training/testing splits confirm the competitive performance. Conclusions A comparative analysis was performed to assess the effect of three different sampling methods (random, K-means and uniform sampling) on the training of learning algorithms using different evaluation methods. Models trained on K-means sampled datasets generally show a significantly improved performance compared to those trained on random selections—with RF seemingly benefiting most in our particular setting. Our findings on the sampling are highly relevant and apply to many applications of supervised learning approaches in bioinformatics.

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Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network

Cited by 19 publications

References 55 publications

SARS-CoV-2-human protein-protein interaction network

SARS-CoV-2-human protein-protein interaction network

Comprehensive host-pathogen protein-protein interaction network analysis

Improved cytokine–receptor interaction prediction by exploiting the negative sample space

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