Comprehensive host-pathogen protein-protein interaction network analysis

Khorsand et al. BMC Bioinformatics (2020) 21:400 https://doi.org/10.1186/s12859-020-03706-z RESEARCH ARTICLE Open Access Comprehensive host-pathogen proteinprotein interaction network analysis Babak Khorsand1, Abdorreza Savadi1,2* and Mahmoud Naghibzadeh2 * Correspondence: 1 Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 2 Ferdowsi University of Mashhad, Azadi Square, Mashhad 9177948974, Iran Abstract 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. Keywords: Pathogen-host protein interaction network, Network analysis, Bipartite network, Centrality, Essential proteins © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Khorsand et al. BMC Bioinformatics (2020) 21:400 Background Killing millions of humans, infectious diseases are the most brutal enemies of the entire history. 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 [1–8]. 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. Investigating common targets of different pathogens could help us to design multipurpose drugs. Moreover, investigating protein-protein interactions between human proteins (HPPIs) could help us to find viruses’ potential HPs victims. To do that, HPPI network is analyzed by different centrality measures. In network analysis, centrality is the main concept of identifying gravity of each node in the network. Centrality measures can be used to find most important HPs in HPPI network (HPPIN) to identify new drug targets [9–15]. Each centrality measure defines nodes’ weight from a different perspective. HPPIN has been analyzed by different centralities such as Degree Centrality [16], Closeness [17], Lobby Index [18], Betweenness [19], Clustering Coefficient [20], Leader Rank [21], Topological Coefficient [22], Module Centrality [23], Eigenvector Centrality [24], Neighborhood Connectivity [25], Normalized Alpha Centrality [26], Average Shortest Path Length [27], Subgraph Centrality [28], Radiality [29], Range limited Centrality [30] and Eccentricity [31]. Essential genes are minimal gene sets which are indispensable for a living cell and their functions are the foundation of life [32]. As disruption of these genes can lead to cell death, we investigate human virus protein-protein interaction network (HVPPIN) to see whether the product of essential genes (essential proteins) are main targets of virus proteins (VPS) and which human proteins (HPs) are targeted by more VPs. In this paper, we focus on human as host and integrate experimentally proteinprotein interactions (PPIs) between human proteins and virus proteins (HVPPIs) from different databases. Exploring the HVPPIN shows that human proteins which are targeted by different virus strains either have a lot of interactors in intra HPPIN or bridge between two large cliques. Additionally, network analysis is performed on HPPIN by eight different centralities. Results demonstrate that centrality scores of HPs targeted by different virus strains are significantly higher than the other HPs. Besides, it reveals that centrality scores of essential proteins (EPs) are significantly higher than non-essential proteins. HVPPIN is a special type of network called bipartite in which interactions are interspecies in contrast with HPPIN which is a unipartite network meaning interactions are intraspecies. As most of the common centralities are designed for unipartite network and HVPPIN is a bipartite network, seven novel bipartite centralities including CHTV (connectivity of human proteins targeted by same virus p (...truncated)