Evaluating Protein-protein Interaction (PPI)

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Evaluating Protein-protein Interaction (PPI) Networks for Diseases, Pathway, Target Discovery, and Drug-design Using ‘In silico Pharmacology’ Chiranjib Chakraborty1,2,#, George Priya Doss. C3,#, Luonan Chen4,5 and Hailong Zhu1,* 1

Department of Computer Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong; 2Department of Bio-informatics, School of Computer and Information Sciences, Galgotias University, Uttar Pradesh, India; 3 Medical Biotechnology Division, School of Biosciences and Technology, VIT University, Vellore 632014, Tamil Nadu 632014, India; 4Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, China; 5Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan Abstract: In silico pharmacology is a promising field in the current state-of drug discovery. This area exploits “proteinprotein Interaction (PPI) network analysis for drug discovery using the drug “target class”. To document the current status, we have discussed in this article how this an integrated system of PPI networks contribute to understand the disease pathways, present state-of-the-art drug target discovery and drug discovery process. This review article enhances our knowledge on conventional drug discovery and current drug discovery using in silico techniques, best “target class”, universal architecture of PPI networks, the present scenario of disease pathways and protein-protein interaction networks as well as the method to comprehend the PPI networks. Taken all together, ultimately a snapshot has been discussed to be familiar with how PPI network architecture can used to validate a drug target. At the conclusion, we have illustrated the future directions of PPI in target discovery and drug-design.

Keywords: ????????????????????????????????. 1. INTRODUCTION Since the last decade, the average human lifespan has improved due to the interventions of novel therapeutics. Scientists have been searching for novel drugs for the management of severe fatal diseases, as well as management of a better life [1-3]. Researchers are trying to discover and develop life-saving drugs [4]. Drug discovery through conventional approachs is extremely difficult, time consuming and costly. The conventional route of pharmacological drug discovery method involves the automated high throughput screening (HTS) technique. In fact, several biochemical, genetic or pharmacological tests can be performed using HTS technique; thereby, researchers can identify the active compounds quickly [5-8]. To develop one candidate drug through the conventional approach takes nearly about 12 to 15 years, and the total cost is more than 700 million dollars. Only 10% of the drug candidates enter into clinical trials, finally get registered and enter into the market [9, 10]. Presently the drug discovery process is performed faster with the intervention of in silico techniques [11]. For instance, to understand the cholesterol biosynthesis pathway *Address correspondence to this author at the Department of Computer Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong, Tel: +852 3411 7636; Fax: +852 3411 7892; Email: [email protected] # Equally contributed

1389-2037/14 $58.00+.00

and develop statin drugs, it required nearly 40 years using conventional route pharmacological drug discovery processes [12, 13]. Conversely, to understand the role of epidermal growth factor receptor 2 (HER-2) protein in breast cancer, the molecular basis of HER-2 receptor based breast cancer pathway and developing the chemotherapeutic agent called Herceptin® required merely three years [14]. During the development of Herceptin®, researcheres utilizes in silico methods which resulted in faster development of this chemotherapeutic agent. The above mentioned is only one example for drug discovery that utilizes in silico techniques. In today’s high tech world, there are several examples of success which supports the in silico route of drug discovery. Recent involvement of computers allows us to identify new drugs efficiently. Using in silico pharmacology, we can analyze and incorporate the biological and medical data from various sources. Several biocomputing based new technologies have been included such as quantitative structure-activity relationships (QSAR), similarity searching, pharmacophores, homology modeling, other molecular modeling methods, docking technique, machine learning, data mining, and network analysis [15, 16]. Another evolution of “in silico pharmacology” is ProteinProtein networks for the development of biochemical pathways [17]. We can utilize the protein-protein networks for drug target discovery, as well as drug discovery and drugdesign. This route of drug discovery is called the “reverse pharmacology” [18]. © 2014 Bentham Science Publishers

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In this review, firstly, we focus on conventional drug discovery and present state-of drug discovery using in silico techniques and also discuss about the high-quality “target class”. Secondly, we provide an overview which illustrates how “protein-protein interaction (PPI)” networks are developed through cellular interactome networks. Thirdly, a generalized architecture of protein-protein interaction (PPI) networks was provided. Fourthly, we discussed the present scenario of disease pathways and protein-protein interaction networks as well as the methods to study the PPI networks. Finally, an overview was presented to understand how PPInetworks architecture can be used to validate a drug target. 2. CONVENTIONAL DRUG DISCOVERY AND PRESENT STATE-OF DRUG DISCOVERY USING IN SILICO TECHNIQUES Conventional drug discovery was initiated with a proposed drug candidate to study in a model organism in order to show the effects on disease symptoms. Previously, conventional method of drug discovery was used, and remained as the only method for drug discovery. Using this conventional method various drugs were developed from nature. Nature is the single most significant source of drugs. During 1970–1980, natural products were the main source of novel human therapeutics for Western pharmaceutical industry. Among the years of 1981 to 2007, approximately 877 smallmolecules were launched as New Chemical Entities (NCEs). It has to be noted that from 1994 half of the drugs approved are from the natural products and semi synthetic natural product analogues or synthetic compound. These NCEs were developed by conventional method of drug discovery [19, 20]. Another 13 natural product-driven drugs were approved from 2005 to 2007 and conventional methods were used to discover these molecules [21]. Although several drugs were developed using the conventional methods of drug discovery, there are several disadvantages in it. The numbers of in vivo assays involved in the conventional methods to discover the drug candidate molecule as well as to understand its efficacy, toxicity, and metabolilsm [22, 23]. This approach is used to understand the mechanistic action of the candidate drug molecules. A few years ago the US-FDA-approved drugs developed by the researchers without knowledge of the molecular mechanisms action responsible for the diseases [24]. Understanding the molecular mechanisms of action is not the important criteria for conventional drug discovery. However, several steps are involved in the drug discovery process which are disease selection, target discovery and development, lead compound selection and discovery, lead optimization, finally clinical trials. In the conventional drug discovery process, these steps were performed sequentially [25], Therefore, developing a new drug from the unique idea and launching of a complete can take about 15 years and estimated cost is about 700 million US dollars [26]. Using computational biology, a different route of drug discovery was developed termed “reverse pharmacology” or “systems pharmacology” drug-design strategy initiated to fast track drug discovery process and development [27]. This drug-design approach helps to reduce the side effects [2830]. During this genomic era, advancement in genomics, proteomics, and metabolomics has ended up in the discovery of many new drug-like entities. This scenario helps to dis-

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cover the drug-like activities from those drugs-like entities [31]. Systems pharmacology, also termed ‘In silico pharmacology’, is a promising area that helps to understand drug action across manifold level of complexity ranging from all the levels such as cellular and molecular, as well as tissue and organism level along with experimental methods. This comprehensive approach has several advantages. i) This can easily offer an understanding of both the therapeutic as well as side effects of drugs. ii) It provides a view to understand how drugs act in several interacting pathways as well as different tissues and cell types. Iii) This approach helps to identify new drug targets and improves the safety and efficacy of existing drugs. Now, the modern “reverse pharmacology” route of drug discovery is in the limelight, in which, the first step is the discovery of the “drug targets”. Reverse pharmacology drug discovery is based on a “one-disease, one-target and one-drug” approach. This new approach is of immense importance today because of its route which transforms the disease pathway into biological networks and therefore, this new approach will be more effective in handling complex diseases [32-34]. This new approach pursues target discovery based on disease mechanism or “Target class” discovery from the disease mechanism. The target class is also called as “druggable target” which may also be potentially useful in discovering the target molecule. Therefore, this approach is more well-organized than the conventional approach [35, 36]. However, there are some disadvantages that exist in the “in silico pharmacology” route. One disadvantage has been noted during the process of model development. During this process, we calculate the drug target flexibility or protein flexibility, molecule conformation and promiscuity which are present in accurate predictions may not be always true in real life. 3. WHAT MAKES A HIGH-QUALITY “TARGET CLASS”? Currently using computational biology, the most crucial focus of the researchers is to discover the “Target class” [37]. It has been noted that ‘Proteins and enzymes’ target class represents potential drug target for different diseases [7, 38]. The proteins and enzymes have active sites and substrate binding site which makes them high-quality “target class”. We can clone these proteins easily and can understand 3D structure properly [39]. This “in silico pharmacology” helps us to known the active site and substrate binding site using computational methods to design the drugs. After completion of the human genome project, it has been noted that 266 proteins can be used as drug targets that are targeted by pharmacological agents [40]. The pharmaceutical industry actually rely on particular ‘proteins, and enzymes’ target class from diseases related biochemical pathways (Table 1), against which researchers are attempting to develop active compounds with preferred actions [41-44]. Majority of the drugs are functioning through the binding to a specific proteins or enzymes, thereby changing their biochemical action which may alter the diseases related biochemical pathway. Cell contains several potential enzymes which have several key activities [45, 46]. Active compounds which act as an inhibitor can modulate the function of enzymes; therefore, they are often used as drugs [47, 48]. Enzymes like proteases perform proteolytic activities. These potential proteins

Evaluating Protein-protein Interaction (PPI) Networks

Table 1.

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Biochemical pathways, related diseases and their target proteins of potential drug development. Biochemical Pathway

Diseases

Drug Target

References

Wnt signaling pathway/ Wnt/-catenin pathway

Cancer, Schizophrenia, Rheumatoid arthritis, Osteoarthritis, familial exudative vitreoretinopathy

glycogen synthase kinase 3, frizzled homolog-1

[156-158]

NMDA receptor pathways

Stroke and Alzheimer's disease, Schizophrenia

NR1 homologs such asNR1-1a, NR1-1b

[159,160]

Renin-angiotensin pathway or Renin–angiotensin–aldosterone pathway

Hypertension, Heart failure

AGTR1, ACE

[161,162]

Phosphoinositide 3-kinase pathway

Cancer

PI3K isoforms

[163]

Apolipo-protein metabolism pathway

Alzheimer's disease, HDL cholesterol (HDL-C) deficiency

Apolipo-protein E, LDL receptor, ABCA1 Transporter Protein

[164,165]

Lipid homeostasis pathway

Hyperlipidemia, Obesity, Coronary Heart disease

Sterol regulatory element binding proteins (SREBPs), HMG-COA reductase, SCAP, Orm1, Orm2

[166,167]

Cyclic nucleotide metabolism pathway

Asthma, Cancer

PDE2, PDE3, PDE4A, PDE4B, PDE5A

[168,169]

Insulin Signaling pathway

Diabetes, Obesity, Cancer

Insulin, IGF, IGF-1R

[170,171]

(enzyme or receptor) are found at different locations of the biological pathway and are inter-concerned in approximately each process in the cell. Deformity in biological pathway and inter-concerned in proteins cause various diseases. One notable example in which the process of proteolysis is associated with various diseases such as inflammation, arteriosclerosis, and neuro-degeneration. Therefore, “the protease class” is an important target class for drug discovery and development. It is an extra advantage that this protease class is well studied and therefore, it has a well-defined structural biology as well as well-known chemistry [49, 50]. Another common example of enzyme inhibition is aspirin. Presently, this molecule is used as a drug which inhibits the COX-1 and COX-2 enzymes that produce the inflammation messenger prostaglandin, thereby suppressing the pain and inflammation [51, 52]. To understand this enzyme class, we can isolate the protein and develop an assay in vitro [53]. Outside the cell, proteins may not function after isolation. Conversely, proteins act as part of highly interconnected cellular networks inside the cell [54 -57]. So, this protein network shows the relationship between the proteins in a biological pathway such as signaling pathway. The cell communicates with the help of signaling pathways and allows receiving, processing, and responding to the information through the pathway components –protein. The cross-talk between the pathway components results in signaling networks [58]. These signaling networks provide a global view on the relationships between the proteins thereby which act as network components. The network between proteins is defined as interactome networks [59]. On the other hand, network biology is paying more attention towards drug–target interaction network which helps us to understand the connection between drugs with their known targets [60]. Actually, a drug–target network consists of several sub networks and it is one of the cluster of associated drug targets. These two types of

networks (interactome network and drug–target network) also provide an understanding about the target profiling and interactome for the discovery of new drugs. 4. GLOBAL VIEW OF BIOCHEMICAL NETWORKS BETWEEN THE CELL COMPONENTS–FROM JOURNEY FROM CELLULAR INTERACTOME NETWORKS TO “PROTEIN-PROTEIN NETWORK” Recently, the procedure for networking building of proteins was initiated by scientists, using a biochemical pathway. Actually, the networking building is the process of categorizing and joining together the pathway components, where communication and relationship between the protein components is an essential criterion [61, 62]. The networking building process is a knowledge base process associated with annotations and is populating very fast [63]. The global set of relationship between protein–protein interactions can be easily understood through interactome network. Actually, interactome is the total set of inter-molecular or intramolecular association in a particular cell which may be systematic mapping of protein-protein, protein-DNA, proteinRNA, and protein-metabolite interactions [64-66]. From the system biology point of view, we can state it is a theoretical framework according to which dynamic complex systems created through interacting macromolecules and cellular behavior can be motivated through networks [67]. The beginning endeavor was to explore the proteome-scale interactome network mapping in the mid-1990s [68, 69]. Presently, protein-protein interactions are extensively used for large-scale mapping, and one example is binary interactome mapping. Proteome can support us to infer on interactome networks spanning from a particular protein functional categorization to glimpse on local and global systems resources [66, 70]. Here, we use interactome to understand the protein –protein interaction in a cell. These “interactome” or cellular networks offer comprehensive perspective of the relationships

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between nodes. Actually, this discipline is trying to simplify a complex network system as well as to sum up of components (nodes) and interactions (edges) between them [71]. Therefore, the network properties can be calculated based on a node’s connections, relationship in the network degree of distribution as well as nodal dynamics [72-74]. In a protein – protein interaction network, the number of nodes that it interacts locally as well as globally is important. Various protein–protein interaction network studies are focused on the degree distribution of the global network and local network and often hubs, nodes which connect to relatively many other nodes [75, 76]. 5. THE ARCHITECTURE OF PROTEIN-PROTEIN INTERACTION (PPI) NETWORKS In protein-protein interactions, (PPIs) networks are composed of nodes which are connected by the edges. The edges contain protein – protein interaction or protein– compound interactome or network [77, 78]. Mainly, network can be divided in two ways that are- directed edges [79] and undirected edges [80]. It has been noted that direction in edges as well as replication of edges is important in influencing the strength of PPI. Biochemical signaling based proteomic studies have examined that signaling networks are directed [81, 82]. To establish a PPI networks during assembling a biochemical pathway, it requires the right set of proteins and their order of interactions. We should also understand the directionality of each edge. A directed network contains only directed edges and undirected network contain only undirected edges [83]. The attribute of the PPI network is hoarded in the edges. It was noted that one edge can be standardized by dividing with (M-1)/ (M-2), where M is the number of edges in the connected component. The edge betweenness is a parameter which can be defined as the number of shortest paths between two nodes [84]. However, it is essential that we should know about the simple as well as complex network parameters. Another important question is: how one node exerts influence to the other node. There are small numbers of nodes in PPI, where more dense connections are found than the average node. Some time, we find highly clustered connections between any two nodes. In this kind of networks, is entitled as “scale-free networks” [85, 86]. The portion of nodes is having ‘n’ edges and that may denote as p(n) [87]. Conversely, identification of the component in a PPI network is essential, and once components in a PPI network were identified, the role of a node can be naturally determined by how the node was located in its own component and with respect to other component [88]. 5.1. The Distance Between Two Nodes The distance between two nodes u and v can be designated as d (u, v) according to graph theory. The neighborhood of nodes can be defined as set of nodes and its neighbors nodes can be denoted as n. The connectivity of nodes (n) can be described by neighborhood which is related to kn, and it is the size of its neighborhood. Each number of nodes can provide information about the distribution of network density [89, 90].

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5.2. The Degree Centrality Node degree centrality refers to the number of associations with other nodes. In undirected networks, it is equivalent to the neighbors of the nodes. The more attached nodes have more influence or significance. More attached node have greater prospect because they have more choices [91, 92]. The degree centrality of a vertex V, we define a graph G for a given graph G=(V, E) with V vertices and its set of edges E, the relation can be is described as CD (V) = deg (V) 5.3. Clustering Coefficient of a Node According to Watts and Strogatz (1998) the clustering coefficient [92] is as follows: "Suppose that a vertex V has KV neighbours; then at most K V (K V - 1)/2 edges can exist between them (this occurs when every neighbour of v is connected to every other neighbor of v). Let CV denote the fraction of these allowable edges that actually exist. Define C as the average of CV over all V." In undirected networks, the clustering coefficient Cn of a node n is defined as follows: Cn = 2en/(kn(kn-1)), Where, kn is the number of neighbors of n and en is the number of connected pairs between all neighbors of n. In directed networks, the definition is a little diverse which is as follows: Cn = en/(kn(kn-1)) [89, 90, 92, 93]. However, for the undirected and directed networks, the clustering coefficient remains same for both the cases. It is a ratio which can be denoted as N / M, where N is the number of edges, and M is the maximum number of edges [93]. 5.4. Overlap Size of Nodes Each node i of a system is illustrated by a membership number mi, and this relationship can be denoted through the number of neighborhood node associated with it. Consecutively, communication between the two member nodes can be explained where  and  can share S-, - OV nodes, which is described by Palla et al. [94] as the overlie size between these member of the node of network area. Obviously, the network area also composes a network with the overlie being their relations. 5.5. Node Degree Distribution The degree distribution P(k) of a network can be defined to be the fraction of nodes in the network with degree k. In a network, if there are n nodes and nk of them have degree k, we have P(k) = nk/n. This node degree distribution represent the number of nodes having degree k, (where k = 0,1, …). We should understand about the neighborhood connectivity of a node n and also can be described as the average connectivity of all neighbors of n [95]. However, it has been recommended that over representation of vital proteins among high-degree nodes can be play

Evaluating Protein-protein Interaction (PPI) Networks

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an essential role into a hub of network, and this hub relates arbitrate connections among many of less connected proteins [96]. Other than the above parameters, there are some complex PPI network parameters exists which are – connectivity of neighborhood node, shortest paths between two nodes, topological coefficients stress, stress distribution between nodes, and closeness centrality of nodes etc. It is very important to understand these parameters properly in analyzing the architecture of protein-protein interaction (PPI) [97]. Yet, in particular question arises: what is the main topological determinant architecture of PPI of essentiality? However, we should understand the diseases, biochemical network of PPI properly, before answering this question.

6.2. PPI in Pathogen Related Disease

6. PRESENT SCENARIO OF DISEASE PATHWAY AND PROTEIN-PROTEIN INTERACTION NETWORKS

iii) Derived from RNA interference data [112, 115]

After the completion of Human Genome Project, major attention is shifted towards gene and proteins in mapping the networks of interactions. The biochemical or functional association between the proteins for diseases can be used for developing protein-protein interaction (PPI). This elucidation can help us to map a large scale network connections as well as protein networks which are increasingly serve as a tool to unravel the molecular basis of disease [98-100].

v) Co-localization of host and pathogen proteins [112, 113]

6.1. PPI Networks to Understand Disease PPI networks provide global views of the relationships between nodes to gain a basic understanding of disease. These networks can be used to identify diseases pathways. First, researchers understand the togetherness of a group of proteins to prepare protein interaction by means of graphlet or small networks. Using graphlet or small networks, they are able to develop the purposeful complex network and finally develop a pathway based network [101]. Then, researchers depict the defective portion of this pathway to understand the susceptible proteins related to disease or representative nodes to understand diseases network in terms of PPI network. Therefore, innovative techniques are being cultivated precisely to prepare the communication between sub-networks to capitulate pathway concept. PPI network is also useful to know different aspects of disease progression [102, 103]. PPI network can be developed using network motifs; small sub-graphs of three to five nodes which have patterns of connectivity over represented in the network [104]. Scientists developed network all the PPIs that take place in Huntington’s disease (HD) and discovered that the communication between the proteins such as HTT and GIT1 to understand disease process such as aggregation [105]. More advance work using these PPI network confirmed that GTI1 is possible as a drug target and this protein can be utilized as a therapeutic target to develop therapeutic agent for the management of HD [106, 107]. Another example is Von Hippel-Lindau syndrome (VHL) which occurs through protein misfolding. This protein misfolding results in disruption of PPI through angiogenic growth factors expression and formation of the blood vessels [108, 109]. Insulin signal transduction and insulin resistance was studied to understand the Type-2 Diabetes using PPI [17]. Another PPI network was developed to study breast cancer in order to understand the interaction networks [110].

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Infectious diseases are one of the foremost causes of death. Host-pathogen interactions are vital in defining the infection mechanism; build up superior treatment and prevention of infectious diseases. A number of computational methods are proposed to predict host-pathogen proteinprotein interactions. Several parameters are pursued to analyze host-pathogen PPIs. Some of the parameters are as follows: i) Based on functional information analysis in terms of gene ontology [111-113] ii) Based on gene expression data [111, 114] iv) Localization information analysis in terms of protein subcellular localization [111, 116] vi) Based on related experimental data analysis [112, 117, 118] Host-pathogen PPI networks are being studied time to time. Some examples are host-pathogen PPI networks related to pathogens such as H. pylori [119], P. falciparum [120, 121] etc. These PPI network is helpful to understand more about the host-pathogen interactions as well as to identify common proteins which are associated with PPI network. These findings can be more helpful in understanding the drug target and drug development. PPI network of viral pathogens has been studied [122, 123]. PPI in pathogen significantly notify us that these interactions are different from the PPI maps of other eukaryotes. 7. METHODS TO STUDY THE PPI NETWORKS To study PPI networks, ‘in silico pharmacology’ uses both experimental methods as well as computational methods. Several methods are available online to understand the PPI networks such as biological method, high-throughput and in silico predictions methods. 7.1. Biological Methods It is well established that protein interactions can be identified through different biochemical, bio-physical methods such as X-ray crystallography, NMR spectroscopy, fluorescence, atomic force microscopy [124]. To determine the protein structure and interactions, as well as their rates of interconversion, researchers utilize X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, cryoelectron microscopy and small-angle X-ray scattering provide atomic-resolution or near-atomic-resolution snapshots [125]. One notable example is the crystallographic characterization of cytochrome P450 enzymatic cycle [126]. Jointly, structure and kinetics can be predicted at the same time by using Laue X-ray diffraction [127]. Also, hydrogen– deuterium exchange can be studied by either mass spectrometry or NMR spectroscopy, which may provide a powerful way to find global or local unfolding of protein on timescales from milliseconds to longer time [128]. Several biological methods are available for studying the protein interactions and their dynamics.

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7.2. High-throughput Methods and In silico Methods Presently, different methods have been developed and applied to study large scale networks of PPI of different organisms. One such method is yeast-two-hybrid (Y2H) System [129]. Studies have been performed using Y2H system to check for the interactions among large sets of human proteins [130, 131]. Another method is co-immuno-precipitation (co-IP) which was applied to illustrate the understanding a number of proteins. One such study uses interactors of 338 proteins which were chosen based on their association with the disease [132]. Another method is clustering algorithms which allow proteins to be prepared into functional modules such as protein complexes or signaling pathways [133, 134]. A high-confidence weighted network, another method was developed to calculate new annotations for proteins, such as protein function, localization, and other PPI network information [135, 136]. Clustering of phylogenetic is used to outline the context of metabolic networks which can identify the evolutionary conserved functional entities [137]. In silico methods are also available which provides a fast balance in investigational procedures. These in silico methods can be used to validate experimental data as well as to help select potential targets for further experimental screening [138]. Some other in silico methods are available which utilizes investigational data. The examples are to use the relative frequency of interacting domains [139], co-expression [140], or network properties [141, 142]. 7.3. Graph and Graphlet-based Method Graph theory can be successfully and usefully applied to study PPI networks. To date the study of global graph uniqueness, basic graph algorithms are employed to portray local interconnectivity and more detailed associations between nodes. Such graph methods can facilitate addressing fundamental PPI, such as signal transduction pathways [143]. Graphlets with 2–5, nodes are suitable to measure PPI network [144]; the nodes belonging to the same orbit are of the same shade within a graphlet [145]. It has been noted that graphlets are different from network motifs; because they must be induced subgraphs (motifs are fractional subgraphs). A subgraph of a graph G is having nodes and edges belong to G on a subset S. A subgraph S of graph G is depicted, if S includes every edges that appear in G over the same subset of nodes. Therefore, as a summary, we can say that a graphlet is a small, connected and make subgraph of a larger network [146]. 7.4. Database Several databases have been developed to study the interacting proteins and PPI network. This database contains thousands of interactions. Some examples are DIP [147], BioGRID [148], STRING [149], and ConsensusPathDB [150] etc. 8. DOES THE PROTEIN-PROTEIN INTERACTION (PPI) NETWORKS ARCHITECTURE CAN USE TO VALIDATE A DRUG TARGET? PPI network of biological pathway of a disease and their drug target needs thorough understanding. The information processing PPI networks of biological pathway have the ca-

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pability to validate drug targets. In this context, several biological pathways and related database are available for drug target identification (Table 2). One such example is SMPDB is a pathway database. Currently SMPD comprises of more than 450 highly pathways specific to humans which describes the small molecule metabolism or small molecule processes [151]. This database can help us to identify a metabolic disease; ascertain whether a certain protein found in pathogens could be a drug target or not. However, from PPI network we can understand that less connected nodes would comprise susceptible points of the disease related network and these nodes are better candidates for drug targets. Presently, several PPI networks are available for a different kind of disease such as type-2 diabetes [152] and neurodegenerative disease [153] which can be used to validate the drug target. After developing the PPI-Networks, drug targets can be identified based on the chemical similarity of their ligands [127]. These target nodes are shown to have important topological attributes as well as distinct from the attributes of networks based on structural similarity of the proteins. Some properties are intrinsically important such as attributes of a gene, the identified protein or set of proteins that are chosen from the PPI network topology. Using such studies may initiate rapid identification of drug targets (Fig. 1). Therefore, PPI network of disease pathway has the potential to identify good drug targets, “proteins or a particular protein” from that PPI network which can interact with a drug to provide a therapeutic response. Some steps have been recommended by scientists to understand how a protein-protein interaction (PPI) network architecture can be used to validate a drug target. The steps include- First, identification of possible targets based on support of significant biological function in the clinical condition and derived from evidence that the function of the target can be effectively controlled. Second, a physical and functional network should be generated of relations for the target obtains from existing data. This network is a PPI network. Third, verification should be done of the previous history of mutational PPI disturbance involving the target. Fourth, development of a biased plan to identify exact versus nonspecific inhibition of the chosen protein, PPI as well as control pathway. Fifth, it is necessary to exploit unexpected findings (Golemis et al. 2002). However, the outlined above strategies should be thoroughly studied to validate the drug target from the protein-protein interaction (PPI) networks architecture. CONCLUSION The drug discovery research domain has experienced a fundamental shift from the conventional approach to “target class” based drug discovery using “protein class” over the past several years. After the post-genomic era, the drug discovery research spotlights more on drug target oriented platform which focuses mainly on the identification of specific biological targets using enzymes or proteins. So, PPI network plays a significant role in this research domain. Researchers are using genomics and proteomics and other experimental assays using hundreds or thousands of gene or protein at a time to understand the molecular mechanisms of normal biological processes as well as the disease states. Using computational biology, PPI network can aid in the drug development

Evaluating Protein-protein Interaction (PPI) Networks

Table 2.

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List of computational server pathway related databases.

Pathway

Access

Remark

References

KEGG PATHWAY Database

www.genome.jp/kegg/pathway.html

This data base is a collection of manually drawn pathway maps from Kanehisa Laboratories, Japan

[172]

BioModels Database

www.ebi.ac.uk/biomodels/

It is a database for storing, exchanging and retrieving published quantitative models of biological interest from European Bioinformatics Institute

[173]

KinG Database

http://hodgkin.mbu.iisc.ernet.in/~king

Protein kinases database

[174]

GPCRDB

http://www.gpcr.org/7tm/

GPCR database

[175]

Path Finder

http://bibiserv.techfak.uni-bielefeld.de/pathfinder/

Web server for biochemical pathways reconstruction and dynamic visualization

[176]

GSCope

http://gscope.gsc.riken.go.jp/

Modeling and analyzing biological pathways

[177]

Reactome

http://www.reactome.org/

Pathway database with more advanced query features

[178]

SMPDB

http://www.smpdb.ca/

Pathway database with disease, drug and metabolic pathways for humans

[179]

Wikipathways

http://www.wikipathways.org

Community annotated pathway database for model organisms, human pathways, drug or disease pathways

[180]

MPEA

http://ekhidna.biocenter.helsinki.fi/poxo/mpea/.

Metabolite pathway analysis , visualization

[181]

Fig. (1). A Schematic illustration of “drug target discovery” from protein-protein interaction (PPI) network. First, protein-protein interaction (PPI) network is developed from a biochemical pathway; then diseases module is selected from the PPI network and finally, target proteins have been selected from the diseases module.

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process which follows the route of target identification, validation and lead identification. However, PPI network software systems related to target discovery and drug-target interaction are needed immediately. Therefore, efficient and speedy computational techniques can fulfill the objective of the drug discovery research. CONFLICT OF INTEREST

Chakraborty et al. [16]

[17]

[18]

The authors confirm that this article content has no conflict of interest.

[19]

ACKNOWLEDGEMENTS

[20]

The authors thank the management of VIT University as well as Galgotias University for providing the facilities to carry out this work. This work was supported by the Research Grants Council of Hong Kong [212111] and Faculty Research Grant of Hong Kong Baptist University [3011299].

[21] [22] [23]

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