# Comparative Genome and Network Centrality Analysis to Identify Drug Targets ofMycobacterium tuberculosis H37Rv

Authors: Tilahun Melak; Sunita Gakkhar
Journal: BioMed Research International
 (2015)

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## Abstract

Potential drug targets ofMycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.


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## Body

## 1. Introduction

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis (TB), is the second main cause of death and infection for human among infectious diseases next to Human Immunodeficiency Virus (HIV) [1] andMycobacterium tuberculosis H37Rv is the most studied strain. According to WHO global tuberculosis report of 2013, there were an estimated 8.6 million new cases and 1.3 million TB deaths in 2012 [2]. The estimate also showed that 3.6% of the new and 20.2% of previously treated cases are multidrug-resistancetuberculosis (MDR-TB) cases. Even though the current frontline anti-Mycobacterium drugs are mainly responsible for controlling and treatment of the disease to the extent that is being existing today, they have several shortcomings [3]. The main of them is the emergence of MDR-TB and extensively drug-resistant tuberculosis (XDR-TB) which could be able to render even these frontline drugs inactive. Some of the drugs like rifampicin have adverse side effects which lead to patient compliance. Most of these drugs are not also effective in acting on the latent forms ofBacillus. The need for careful consideration of vicious interactions between TB and HIV during drug discovery process forMtb extends the challenge further [4].The stated challenges and limitations of the existing frontline antibiotics forMtb led to exhaustive computational and experimental methods to identify potential new drug targets for the pathogen. The stream which focuses on identifying the essential genes for the survival and growth of the pathogen is one of them. There are three main findings which proposed the lists of essential genes for the survival and growth ofMycobacterium tuberculosis H37Rv [5–7]. These findings were compiled and stored in Database of Essential Genes (DEG) for the intended users [8–10]. The database has been used to propose potential drug targets ofMycobacterium tuberculosis H37Rv [11]. In the study, the complete genome ofMycobacterium tuberculosis H37Rv was blasted against DEG to identify essential genes and the resulting dataset was further analyzed for similarity search against human genome to identify genes which are not similar with human to avoid host toxicity. Since two of the main findings about the essential genes were published after this study, it is possible to hypothesise that a comprehensive set of potential drug targets ofMycobacterium tuberculosis H37Rv could be obtained through a systematic computational analysis on the integrated dataset from DEG which incorporates those recent findings.Generally, computational methods identify a larger number of potential drug targets which could be difficult to experimentally validate all of the targets due to time and cost constraints. Our main objective in this study is to identify and prioritize the potential drug targets ofMycobacterium tuberculosis H37Rv by integrating the analysis of comparative genome and network centrality measures of protein-protein interaction network of the pathogen. The stated limitation with respect to the global network centrality measures is that they are mainly based on only shortest paths [12]. Even though nonshortest paths could be important while spreading information in the cellular network, the shortest paths yield a higher coverage than observed directly neighbours locally from protein interaction data. It has also been hypothesised that shortest paths are the most feasible paths that can be taken by proteins to communicate with each other [13].In this paper, a list of 137 potential drug targets ofMycobacterium tuberculosis H37Rv has been identified. These proteins are essential for the growth and survival of the pathogen, nonhomologous with human and prioritized based on their network centrality measure values where all of them are found within the close neighbourhood of the centre of gravity of protein-protein interaction network. It has been found out that almost half of these proteins have been already reported as potential drug targets of the pathogen by other methods. The structural assessment showed that 28 out of the 137 (20.44%) proteins have solved structure.


## 2. Materials and Method

### 2.1. Comparative Analysis

The complete genome sequence dataset ofMycobacterium tuberculosis H37Rv was retrieved from Tuberculosis Database which is an integrated platform providing access to genome sequence, expression data, and literature curation for tuberculosis research [1, 14]. BLAST search of the retrieved protein coding genes was carried out against DEG to identify essential genes. The corresponding protein sequences obtained after DEG search were subjected to a BLASTp against the nonredundant database with an e-value threshold cut-off set to 0.005 [15]. The search was also restricted toH. sapiens because the objective was to find only those proteins, which do not have detectable human homologues to prevent host toxicity.


### 2.2. Network Analysis

#### 2.2.1. Statistical Network Properties

The protein-protein interaction network ofMycobacterium tuberculosis H37Rv was retrieved from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database [16]. The interactome network could contain false positives and false negatives which might affect the quality of the dataset and have an impact on the result. Interactions labeled with only “medium confidence” and “high confidence” scores were considered to minimize this impact. The statistical properties of the generated proteome network were characterized by different measures such as degree distribution, characteristic path length, and clustering coefficient to understand the general functional organization of interacting proteins.The degree or connectivityk of a given network is equal to the number of connected neighbours or adjacent nodes. The degree distribution p(k), which has become one of the most prominent characteristics of network topology, is the measure of the proportion of nodes in the network having degree k [17].For any two nodesni and nj in a network with n vertices, the distance dij between them is defined as the length of theshortest path between the vertices, that is, the minimal number of edges that need to be traversed to travel from vertex ni to nj. The path between two nodes does not necessarily have to be unique since there could be several alternative paths with the same path length. The characteristic path length is defined as the average shortest path of overall pairs of nodes in the network with n vertices [17].Another important property of the network which shows local cohesiveness is theclustering coefficient C [17]. It is a measure of the probability that two nodes with a common neighbour to be connected. It is an indicator of the internal structure of the network. In undirected network, for a given node ni with ki neighbours, there exist Emax=ki(ki-1)/2 possible edges between the neighbors. Clustering coefficient ci of vertex ni is then given as the ratio of the actual number of edges Ei between the neighbors to the maximal number Emax:(1)ci=2Ekiki-1.The global or mean clustering coefficient Ci of the network is the average cluster coefficient of all vertices.


#### 2.2.2. Network Centrality

The resulting lists of proteins were further prioritized based on the four network centrality measures, namely, degree, closeness, betweenness, and eigenvector. The goal of these network centrality measures is to numerically characterize the importance of proteins in the biological system since centrality indices are used to quantify the nodes or edges that are more central than others.For undirected Graph G having adjacency matrixA=(Aij), the degree centrality ki of its ith node is given by(2)ki=∑j=1nAij.Closeness centrality of a node s is calculated as the inverse of the sum of distances from all other t nodes:(3)cclos=1∑dists,t.The betweenness centrality is a measure of the total number of shortest paths between two nodes passing through the specified node.Letσst be the number of shortest paths from s to t and σst(p) denotes the number of shortest paths from node s to node t passing through p; then betweenness centrality B(p) of the node p is given by (4)Bp=∑s≠p≠tσstpσst.LetX=(x1,x2,…,xn) be an eigenvector of the adjacency matrix A with eigenvalue λ:(5)λX=AX.The eigenvector centrality is given by(6)Xi=λ-1∑j=1nAijxj.By Perron-Frobenius theorem, there is only one eigenvector x with all centrality values nonnegative and this is the unique eigenvector that corresponds to the largest eigenvalue λ [18].Degree centrality is the most simple but also the most basic centrality measure which is used to identify an important node involved in a large number of interactions. It is a local centrality measure since determined by the number of its neighbors. It has been widely used for the analysis of biological networks [17]. Proteins with high degree centrality values are more likely to be essential for the survival and growth of the organism than proteins with low degree centrality values. In closeness centrality the specified nodes closeness to all other nodes of the network is quantified. An important node is typically close which means it can communicate quickly with the other nodes of the network. The betweenness centrality measure is a means to quantify the influence of a node in the interaction network. It shows that an important node lies on a high proportion of paths between other nodes in the interaction network. The eigenvector centrality of a node is directly dependent on the centrality values of its connected neighbors which means eigenvector centrality of each node is assigned a centrality value based on not only the quantity of its connections, but also their qualities. A high centrality value of the neighbors should result in a high centrality for the node under consideration. So the main idea in eigenvector centrality measure is that an important node is the one which is connected to important neighbors. The progression of the experiments in this study has been shown in Figure 1.Figure 1
Progression of experiments. Different aspects indicated in this diagram are identification of essential genes, comparative analysis, construction of protein-protein interaction network, and network centrality analysis and validation.


## 2.1. Comparative Analysis

The complete genome sequence dataset ofMycobacterium tuberculosis H37Rv was retrieved from Tuberculosis Database which is an integrated platform providing access to genome sequence, expression data, and literature curation for tuberculosis research [1, 14]. BLAST search of the retrieved protein coding genes was carried out against DEG to identify essential genes. The corresponding protein sequences obtained after DEG search were subjected to a BLASTp against the nonredundant database with an e-value threshold cut-off set to 0.005 [15]. The search was also restricted toH. sapiens because the objective was to find only those proteins, which do not have detectable human homologues to prevent host toxicity.


## 2.2. Network Analysis

### 2.2.1. Statistical Network Properties

The protein-protein interaction network ofMycobacterium tuberculosis H37Rv was retrieved from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database [16]. The interactome network could contain false positives and false negatives which might affect the quality of the dataset and have an impact on the result. Interactions labeled with only “medium confidence” and “high confidence” scores were considered to minimize this impact. The statistical properties of the generated proteome network were characterized by different measures such as degree distribution, characteristic path length, and clustering coefficient to understand the general functional organization of interacting proteins.The degree or connectivityk of a given network is equal to the number of connected neighbours or adjacent nodes. The degree distribution p(k), which has become one of the most prominent characteristics of network topology, is the measure of the proportion of nodes in the network having degree k [17].For any two nodesni and nj in a network with n vertices, the distance dij between them is defined as the length of theshortest path between the vertices, that is, the minimal number of edges that need to be traversed to travel from vertex ni to nj. The path between two nodes does not necessarily have to be unique since there could be several alternative paths with the same path length. The characteristic path length is defined as the average shortest path of overall pairs of nodes in the network with n vertices [17].Another important property of the network which shows local cohesiveness is theclustering coefficient C [17]. It is a measure of the probability that two nodes with a common neighbour to be connected. It is an indicator of the internal structure of the network. In undirected network, for a given node ni with ki neighbours, there exist Emax=ki(ki-1)/2 possible edges between the neighbors. Clustering coefficient ci of vertex ni is then given as the ratio of the actual number of edges Ei between the neighbors to the maximal number Emax:(1)ci=2Ekiki-1.The global or mean clustering coefficient Ci of the network is the average cluster coefficient of all vertices.


### 2.2.2. Network Centrality

The resulting lists of proteins were further prioritized based on the four network centrality measures, namely, degree, closeness, betweenness, and eigenvector. The goal of these network centrality measures is to numerically characterize the importance of proteins in the biological system since centrality indices are used to quantify the nodes or edges that are more central than others.For undirected Graph G having adjacency matrixA=(Aij), the degree centrality ki of its ith node is given by(2)ki=∑j=1nAij.Closeness centrality of a node s is calculated as the inverse of the sum of distances from all other t nodes:(3)cclos=1∑dists,t.The betweenness centrality is a measure of the total number of shortest paths between two nodes passing through the specified node.Letσst be the number of shortest paths from s to t and σst(p) denotes the number of shortest paths from node s to node t passing through p; then betweenness centrality B(p) of the node p is given by (4)Bp=∑s≠p≠tσstpσst.LetX=(x1,x2,…,xn) be an eigenvector of the adjacency matrix A with eigenvalue λ:(5)λX=AX.The eigenvector centrality is given by(6)Xi=λ-1∑j=1nAijxj.By Perron-Frobenius theorem, there is only one eigenvector x with all centrality values nonnegative and this is the unique eigenvector that corresponds to the largest eigenvalue λ [18].Degree centrality is the most simple but also the most basic centrality measure which is used to identify an important node involved in a large number of interactions. It is a local centrality measure since determined by the number of its neighbors. It has been widely used for the analysis of biological networks [17]. Proteins with high degree centrality values are more likely to be essential for the survival and growth of the organism than proteins with low degree centrality values. In closeness centrality the specified nodes closeness to all other nodes of the network is quantified. An important node is typically close which means it can communicate quickly with the other nodes of the network. The betweenness centrality measure is a means to quantify the influence of a node in the interaction network. It shows that an important node lies on a high proportion of paths between other nodes in the interaction network. The eigenvector centrality of a node is directly dependent on the centrality values of its connected neighbors which means eigenvector centrality of each node is assigned a centrality value based on not only the quantity of its connections, but also their qualities. A high centrality value of the neighbors should result in a high centrality for the node under consideration. So the main idea in eigenvector centrality measure is that an important node is the one which is connected to important neighbors. The progression of the experiments in this study has been shown in Figure 1.Figure 1
Progression of experiments. Different aspects indicated in this diagram are identification of essential genes, comparative analysis, construction of protein-protein interaction network, and network centrality analysis and validation.


## 2.2.1. Statistical Network Properties

The protein-protein interaction network ofMycobacterium tuberculosis H37Rv was retrieved from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database [16]. The interactome network could contain false positives and false negatives which might affect the quality of the dataset and have an impact on the result. Interactions labeled with only “medium confidence” and “high confidence” scores were considered to minimize this impact. The statistical properties of the generated proteome network were characterized by different measures such as degree distribution, characteristic path length, and clustering coefficient to understand the general functional organization of interacting proteins.The degree or connectivityk of a given network is equal to the number of connected neighbours or adjacent nodes. The degree distribution p(k), which has become one of the most prominent characteristics of network topology, is the measure of the proportion of nodes in the network having degree k [17].For any two nodesni and nj in a network with n vertices, the distance dij between them is defined as the length of theshortest path between the vertices, that is, the minimal number of edges that need to be traversed to travel from vertex ni to nj. The path between two nodes does not necessarily have to be unique since there could be several alternative paths with the same path length. The characteristic path length is defined as the average shortest path of overall pairs of nodes in the network with n vertices [17].Another important property of the network which shows local cohesiveness is theclustering coefficient C [17]. It is a measure of the probability that two nodes with a common neighbour to be connected. It is an indicator of the internal structure of the network. In undirected network, for a given node ni with ki neighbours, there exist Emax=ki(ki-1)/2 possible edges between the neighbors. Clustering coefficient ci of vertex ni is then given as the ratio of the actual number of edges Ei between the neighbors to the maximal number Emax:(1)ci=2Ekiki-1.The global or mean clustering coefficient Ci of the network is the average cluster coefficient of all vertices.


## 2.2.2. Network Centrality

The resulting lists of proteins were further prioritized based on the four network centrality measures, namely, degree, closeness, betweenness, and eigenvector. The goal of these network centrality measures is to numerically characterize the importance of proteins in the biological system since centrality indices are used to quantify the nodes or edges that are more central than others.For undirected Graph G having adjacency matrixA=(Aij), the degree centrality ki of its ith node is given by(2)ki=∑j=1nAij.Closeness centrality of a node s is calculated as the inverse of the sum of distances from all other t nodes:(3)cclos=1∑dists,t.The betweenness centrality is a measure of the total number of shortest paths between two nodes passing through the specified node.Letσst be the number of shortest paths from s to t and σst(p) denotes the number of shortest paths from node s to node t passing through p; then betweenness centrality B(p) of the node p is given by (4)Bp=∑s≠p≠tσstpσst.LetX=(x1,x2,…,xn) be an eigenvector of the adjacency matrix A with eigenvalue λ:(5)λX=AX.The eigenvector centrality is given by(6)Xi=λ-1∑j=1nAijxj.By Perron-Frobenius theorem, there is only one eigenvector x with all centrality values nonnegative and this is the unique eigenvector that corresponds to the largest eigenvalue λ [18].Degree centrality is the most simple but also the most basic centrality measure which is used to identify an important node involved in a large number of interactions. It is a local centrality measure since determined by the number of its neighbors. It has been widely used for the analysis of biological networks [17]. Proteins with high degree centrality values are more likely to be essential for the survival and growth of the organism than proteins with low degree centrality values. In closeness centrality the specified nodes closeness to all other nodes of the network is quantified. An important node is typically close which means it can communicate quickly with the other nodes of the network. The betweenness centrality measure is a means to quantify the influence of a node in the interaction network. It shows that an important node lies on a high proportion of paths between other nodes in the interaction network. The eigenvector centrality of a node is directly dependent on the centrality values of its connected neighbors which means eigenvector centrality of each node is assigned a centrality value based on not only the quantity of its connections, but also their qualities. A high centrality value of the neighbors should result in a high centrality for the node under consideration. So the main idea in eigenvector centrality measure is that an important node is the one which is connected to important neighbors. The progression of the experiments in this study has been shown in Figure 1.Figure 1
Progression of experiments. Different aspects indicated in this diagram are identification of essential genes, comparative analysis, construction of protein-protein interaction network, and network centrality analysis and validation.


## 3. Results and Discussion

### 3.1. Comparative Analysis for Identifying Nonhomologous Essential Genes

The retrieved complete genome sequence dataset ofMycobacterium tuberculosis H37Rv consists of sequences of 3958 protein coding genes. These genes were then blasted against DEG to obtain essential genes. These genes are those which are indispensable for the survival and growth of the pathogen. As a result their functions are, therefore, considered a foundation of life. Defining these protein coding genes which are essential for the bacterial growth and its survival is believed to be important in identifying both key biological processes and potential targets for rational drug development [7]. A total of 1091 genes were identified as essential genes from the analysis.One of the important questions that needs to be addressed while choosing potential drug targets for pathogens likeMycobacterium tuberculosis H37Rv is validating whether the potential proteins to be targeted are all absent in the hostH. sapiens and therefore unique to the pathogen. Identifying those enzymes from the pathogen which does not share a similarity with the host proteins ensures that the targets have nothing in common with the host proteins, thereby eliminating undesired host protein-drug interactions. We have performed a comparative analysis of the hostHomo sapiens and the pathogenMycobacterium tuberculosis for the identified 1091 essential genes. We have adopted a stringent measure of listing out only those enzymes which have no similarity or negligible similarity (above the e-value threshold of 0.005) to the host proteins. With the aid of this approach 572 out of 1091 proteins are absent in the hostH. sapiens and therefore they are unique toMycobacterium tuberculosis H37Rv.


### 3.2. Interactome Analysis for Prioritizing Nonhomologous Essential Genes

#### 3.2.1. General View ofMycobacterium tuberculosis H37Rv Proteome Network

A proteome-scale interaction network of proteins inMycobacterium tuberculosis H37Rv was generated from STRING database which is claimed to be a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions [16]. The interactions are weighted and integrated from various sources like experimental repositories, computational prediction methods, and public text collections which makes it an acting comprehensive metadatabase that maps all interaction evidence onto a common set of genomes and proteins. In this database, a combined score has been assigned for each protein-protein linkage based on the evidence from various sources. A higher score is assigned for interactions which are supported by several types of evidence. Generally, these scores are broadly classified into three, namely, “low-scores” for value less than 0.4, “medium-scores” for values between 0.4 and 0.7, and “high-scores” for those associations whose values are greater than 0.7. The existence of false positives and false negatives is widely anticipated in the networks of these types which are being constructed by using the currently available methods [13]. A recent comprehensive study has also indicated that the protein-protein interaction networks generated from STRING database are of low quality consisting of a significant amount of false positives and false negatives [19]. All interactions with value of “low-scores” have been removed from this study to minimize the impact of the problem. The resulting network contains 64,428 interactions among 3,958 proteins. Of the total 64,428 interactions, 22,395 were labeled as “high-score” and 42,033 as “medium-score.” Despite of its shortcomings, this network provides a good framework for navigation through the proteome and it also allows for refinement of the network upon the availability of new experimental data.Statistical properties of the generated network have been shown in Table1 to describe its essential properties. The characteristic path length of the network, which is the average distance between all pairs of nodes, is smaller than log⁡(n). This implies that theMycobacterium tuberculosis H37Rv proteome interaction network has “small world property” [20]. This property provides an idea about the network’s navigability by indicating how fast information can be communicated in the system irrespective of the number of nodes. Thus, from this small world property of the network, we can understand that the network is efficient in the communication of biological information. This means one protein can have an influence on another with only a small number of intermediate reactions. The shortest path length distribution between pairwise protein interactions has been shown in Figure 2. As the degree distribution of the resulting network has also been shown in Figure 3, it exhibits scale-free property like many biological networks in which the degree distribution of proteins approximates a power law p(k)=k-γ, with the degree exponent γ~1.38. So there are very rare highly connected nodes called hubs in a vast majority of nodes with only a few connections. Theclustering coefficient of the resulting network is significantly higher than theclustering coefficient of a random graph with the same number of vertices (0.008).Table 1
Network statistics.
Parameter
Value
Number of nodes (n)
3958
Connected components
8
Network diameter
10
Average number of neighbours
32.556
Network density
0.008
Network heterogeneity
0.942
Shortest paths
15519694 (99%)
Characteristic path length
3.096
Clustering coefficient
0.294Figure 2
Shortest path length distribution. Distribution of shortest path lengths between reachable pairwise protein interactions.Figure 3
Node degree distribution. The distribution of the probabilityp(k) that the degree of a randomly chosen vertex equals k has been shown and it follows a power law p(k)∼k.


#### 3.2.2. Network Centrality Analysis

Comparative genome analysis was helpful in filtering out 572 nonhomologous and essential proteins ofMycobacterium tuberculosis H37Rv. However, this set is still very large to validate with the aid of experimental methods. The network centrality measures have been used for further ranking and prioritizing these proteins in the generated proteome interactome network. The objective was to order the proteins such that the most important proteins can be used first in an experiment. This has been done with the subsequent steps of sorting all of the proteins in the generated network, filtering proteins that are found near to the center of gravity and identifying the ordered list from nonhomologous and essential proteins which are found in the filtered list. For longitudinal comparison of centralities, the distribution of betweenness value of sorted proteins has been indicated in Figure 4. The diagram shows the number of proteins located in separate score intervals of the network. Betweenness centrality metric is one of the significant indicators of network essentiality because proteins with high betweenness are essential for the functioning of the system by serving as a bridge of communication between several other proteins in the network [21]. In this investigation, we tried to identify proteins which are found near to the centre of gravity of the proteome network by being connected with influential proteins. Since the characteristic path length of the generated network is 3.096, a protein is said to be at the centre of gravity if its betweenness measure is above the total number of shortest paths expected to pass through the protein in the functional network of interest, which is 12253.968. This criterion has been effectively used by Mazandu and Mulder in identification of potential drug targets ofMycobacterium tuberculosis [22]. With the aid of this principle we have got 137 ranked, essential, nonhomologous, and central proteins which we believe to be reliable targets forMycobacterium tuberculosis H37Rv. The detailed list of these potential drug targets incorporating the network centrality measure scores and validation is provided as Supplementary Material available online at http://dx.doi.org/10.1155/2015/212061. The lists of refined targets were further categorized by high level of functional classes and the distribution of these potential drug targets per functional class is shown in Figure 5. The distribution indicated that most of the candidate drug targets are involved in cell wall and cell processes, followed by a significant proportion of proteins in intermediary metabolism and respiration, conserved hypothetical, and those belong to information pathway.Figure 4
Distribution of betweenness centrality values. Longitudinal comparison of centrality values which shows the number of proteins located in separate score intervals based on betweenness centrality measure.Figure 5
It illustrates the comprehensive list of central, nonhomologous, and essential proteins as potential drug targets. The candidate lists have been classified into their high level functional class and the distribution has been indicated in the diagram.The resulting lists of candidate proteins were assessed by comparing with some of known drug targets as well as potential targets predicted by using different computational and experimental methods. The dataset for this purpose was obtained by integrating manually curated targets from TDR, high confidence targets from UniProt, and attractive targets obtained by Raman et al. [29] through a series of comprehensive filters. We have also used the potential drug targets list identified in our previous investigation [23]. The Venn diagram (Figure 6) shows the overlaps among these lists of drug targets and the proposed potential target list. Based on this assessment, 43 proteins in the list were TDR validated targets, 6 of which were in the UniProt target list. An additional of 18 proteins in our list were overlapped with UniProt’s list, 5 of which were also predicted by Raman et al.; their list contains 2 more proteins. From our previous report 56 proteins were overlapped with the current candidates; some of them were already reported as potential targets by other methods. Moreover, there are four known targets of existing antitubercular drugs within this set. They are Rv1908c (KatG) (ranked 12th in the proposed list), Rv3795 (EmbB) (ranked 15th), Rv3793 (ranked 25th), and Rv3794 (ranked 30th). Rv1908c (KatG) is a validated drug target of Isoniazid whereas Rv3793, Rv3794 are target proteins of Ethambutol [24]. Rv3795 (EmbB) is a drug target for Rifampin, Isoniazid, and Ethambutol. Therefore, 67 (48.9%) proteins from our proposed list have been previously predicted or reported to be drug targets by the stated methods.Figure 6
Venn diagram of proteins in the proposed list that are reported by other methods.The lists of top 20 proteins according to each of the four centrality measures have been obtained. From these lists, 10 of the proteins are found to be common and they are listed in Table2. It is hypothesised that these proteins are better targets since they have been identified in higher ranks of the four centrality measures of the interactome network. Additional information about each protein such as function, gene name, whether it has been reported as a drug target by other methods, and interaction with the host can be referred from the Supplementary Material.Table 2
Proteins in the top 20 of all of the four centrality measures.
Protein
Functional class
Network centrality scores
PDB
Betweenness
Eigenvector
Degree
Closeness
Rv1303
Cell wall and cell processes
115190.99
0.1066408
207
0.0490584
Rv3019c
Cell wall and cell processes
71224.33
0.08889492
167
0.0488181
3H6P
Rv0311
Conserved hypotheticals
67942.516
0.07391167
156
0.048861504
Rv0556
Cell wall and cell processes
66400.05
0.103606604
193
0.049006738
Rv0451c
Cell wall and cell processes
66230.336
0.09615834
171
0.04883437
2LW3
Rv0288
Cell wall and cell processes
63475.453
0.09102694
170
0.048855472
2KG7
Rv0875c
Cell wall and cell processes
49197.566
0.11939961
197
0.04895823
Rv1274
Cell wall and cell processes
46747.54
0.11324064
197
0.04900249
Rv0817c
Cell wall and cell processes
40821.99
0.11914455
196
0.048973985
Rv0358
Conserved hypotheticals
40230.57
0.07967692
154
0.048827738Additionally, potential drug targets of the pathogen that interact with the host have been identified to understand the infection mechanism using a dataset obtained from a computational prediction ofHomo sapiens-Mycobacterium tuberculosis H37Rv protein-protein interactions [25]. This dataset is thought as a golden dataset for host-pathogen interaction. As it has been shown in Table 3, 15 proteins from the proposed target lists interact with human. The reason for the presence of only few overlaps could be due to the fact that the host-pathogen interaction dataset is not comprehensive or the host interacting proteins are not necessarily essential to the pathogen and nonhomologous with human. Identifying proteins of the pathogen participating in the complex interplay with the host could significantly increase the reliability of the targets since these interactions are key factors in determining the outcome of the infection [26].Table 3
Proposed targets which interact with the host.
Protein
Functional class
Betweenness
Eigenvector
Degree
Closeness
Rv1599
Intermediary metabolism and respiration
58334.63
0.002786182
82
0.048330363
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
Rv2150c
Cell wall and cell processes
37525.4
0.007632928
103
0.048433885
Rv3921c
Cell wall and cell processes
29409.086
0.006799817
68
0.048375268
Rv0732
Cell wall and cell processes
26715.15
0.011198077
111
0.04828495
Rv1415
Intermediary metabolism and respiration
23617.264
0.002809845
57
0.048159193
Rv2534c
Information pathways
22585.9
0.010742613
123
0.04837822
Rv2553c
Cell wall and cell processes
21154.973
0.004895513
53
0.04829615
Rv1602
Intermediary metabolism and respiration
20447.479
0.002363527
49
0.048105914
Rv1611
Intermediary metabolism and respiration
17798.758
0.002891334
57
0.048196144
Rv2455c
Intermediary metabolism and respiration
16318.1045
0.00160155
54
0.04806034
Rv3601c
Intermediary metabolism and respiration
15341.172
0.00251573
49
0.048123464
Rv2538c
Intermediary metabolism and respiration
14206.7705
0.002324578
65
0.0481065
Rv2987c
Intermediary metabolism and respiration
13133.913
0.001755499
52
0.04798341
Rv1712
Intermediary metabolism and respiration
12815.694
0.002191681
62
0.048019513Further,the study of Mycobacterium tuberculosis virulence is another path which has got much attention in the design of drugs with a new mechanism of action, the production of modern concepts, and tuberculosis treatment schemes [27]. Virulence factors have evolved as a response to the host immune reaction. In recent times, manymycobacterial virulence genes that are essential for the virulence ofMycobacterium tuberculosis complex (MTBC) species have been reported by a number of studies. Most of these genes either encode enzymes of several lipid pathways, cell surface proteins, regulators, and proteins of signal transduction systems or are involved inmycobacterial survival inside the aggressive microenvironment of the host macrophages. We took a compiled list of virulence genes from Forrellad et al. [27] and tried to observe the overlap with our proposed potential targets. It has been found out that five genes from the proposed potential target list are also reported as virulence genes. These genes have been shown in Table 4.Table 4
Genes reported as virulence factors.
Gene name
Rv number
Functional class
Betweenness
Eigenvector
Degree
Closeness
sigA
Rv2703
Information pathways
56665.156
0.004549677
80
0.04838473
katG
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
icl1
Rv0467
Intermediary metabolism and respiration
23928.854
0.002939687
53
0.048157435
pafA
Rv2097c
Intermediary metabolism and respiration
19334.732
0.00727773
57
0.048266105
ideR
Rv2711
Regulatory proteins
15661.199
0.005902891
41
0.04817209


### 3.3. Structural Assessment

One of the main criteria which increase the targetability of the prioritized lists of proteins is the availability of crystal structures. The Protein Data Bank (PDB) is freely accessible and the main worldwide repository for the three-dimensional structural data of biological macromolecules such as proteins and nucleic acids which is typically obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and biochemists from around the world [28]. By excluding those proteins which have more than 70% sequence identity, only 229 (about 6%) ofMycobacterium tuberculosis proteins have solved structure in PDB [29]. Hence, we checked the availability of solved structures of the identified potential lists of targets and out of 137 proteins from our proposed target list, 28 were successfully mapped to 82 structures from PDB which is approximated to 20.44%. This list has also been shown in Table 5 including the corresponding centrality measure values and PDB IDs of structures. However, reliable structures of the pathogen could still be obtained by using theoretically calculated homology models.Table 5
Sorted lists of proteins proposed as potential drug targets which have solved structure.
Protein
Network centrality scores
PDB
Betweenness
Eigenvector
Degree
Closeness
Rv3019c
71224.33
0.08889492
167
0.0488181
3H6P
Rv0451c
66230.336
0.09615834
171
0.0488344
2LW3
Rv0288
63475.453
0.09102694
170
0.0488555
2KG7
Rv0058
57680.18
0.00275039
62
0.0481486
2R5U
Rv1908c
50548.035
0.00563762
76
0.0484564
1SFZ; 1SJ2; 2CCA; 2CCD; 4C50; 4C51
Rv2050
40088.305
0.01941196
91
0.0485105
2M4V; 2M6P
Rv2150c
37525.4
0.00763293
103
0.0484339
1RLU; 1RQ2; 1RQ7; 2Q1X; 2Q1Y; 4KWE
Rv3793
36733.14
0.06872068
136
0.0487904
3PTY
Rv2111c
35411.24
0.00444417
37
0.0479427
3M91; 3M9D
Rv0002
33375.504
0.00362037
90
0.0482796
3P16; 3RB9
Rv3597c
27758.719
0.01894281
73
0.048406
2KNG; 4E1P; 4E1R
Rv1837c
24302.383
0.00201247
47
0.0479375
2GQ3; 3S9I; 3S9Z; 3SAD; 3SAZ; 3SB0
Rv0467
23928.854
0.00293969
53
0.0481574
1F61; 1F8I; 1F8M
Rv3240c
22790.56
0.00369075
64
0.0481586
1NKT; 1NL3
Rv0216
20616.273
0.01939864
57
0.0482126
2BI0
Rv1611
17798.758
0.00289133
57
0.0481961
3QJA; 3T40; 3T44; 3T55; 3T78; 4FB7
Rv2773c
17351.877
0.00189925
52
0.0480259
1C3V; 1P9L; 1YL5; 1YL6; 1YL7
Rv0902c
16640.244
0.02111599
42
0.0480668
1YS3; 1YSR
Rv2711
15661.199
0.00590289
41
0.0481721
1B1B; 1FX7; 1U8R; 2ISY; 2ISZ; 2IT0
Rv3601c
15341.172
0.00251573
49
0.0481235
2C45
Rv2518c
14995.93
0.00281853
25
0.0477322
3VYN; 3VYO; 3VYP; 4GSQ; 4GSR;4GSU; 4HU2; 4HUC
Rv0736
14264.871
0.01536277
50
0.0483009
3HUG
Rv2538c
14206.7705
0.00232458
65
0.0481065
3QBD; 3QBE
Rv3808c
13873.782
0.00814938
51
0.0480131
4FIX; 4FIY
Rv2416c
13419.646
0.00771849
39
0.0482891
3R1K; 3RYO; 3SXO; 3UY5
Rv2987c
13133.913
0.0017555
52
0.0479834
3H5E; 3H5H; 3H5J
Rv2986c
13109.861
0.00315177
40
0.0481223
4DKY; 4PT4
Rv2391
12401.702
0.00120635
45
0.0478749
1ZJ8; 1ZJ9


## 3.1. Comparative Analysis for Identifying Nonhomologous Essential Genes

The retrieved complete genome sequence dataset ofMycobacterium tuberculosis H37Rv consists of sequences of 3958 protein coding genes. These genes were then blasted against DEG to obtain essential genes. These genes are those which are indispensable for the survival and growth of the pathogen. As a result their functions are, therefore, considered a foundation of life. Defining these protein coding genes which are essential for the bacterial growth and its survival is believed to be important in identifying both key biological processes and potential targets for rational drug development [7]. A total of 1091 genes were identified as essential genes from the analysis.One of the important questions that needs to be addressed while choosing potential drug targets for pathogens likeMycobacterium tuberculosis H37Rv is validating whether the potential proteins to be targeted are all absent in the hostH. sapiens and therefore unique to the pathogen. Identifying those enzymes from the pathogen which does not share a similarity with the host proteins ensures that the targets have nothing in common with the host proteins, thereby eliminating undesired host protein-drug interactions. We have performed a comparative analysis of the hostHomo sapiens and the pathogenMycobacterium tuberculosis for the identified 1091 essential genes. We have adopted a stringent measure of listing out only those enzymes which have no similarity or negligible similarity (above the e-value threshold of 0.005) to the host proteins. With the aid of this approach 572 out of 1091 proteins are absent in the hostH. sapiens and therefore they are unique toMycobacterium tuberculosis H37Rv.


## 3.2. Interactome Analysis for Prioritizing Nonhomologous Essential Genes

### 3.2.1. General View ofMycobacterium tuberculosis H37Rv Proteome Network

A proteome-scale interaction network of proteins inMycobacterium tuberculosis H37Rv was generated from STRING database which is claimed to be a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions [16]. The interactions are weighted and integrated from various sources like experimental repositories, computational prediction methods, and public text collections which makes it an acting comprehensive metadatabase that maps all interaction evidence onto a common set of genomes and proteins. In this database, a combined score has been assigned for each protein-protein linkage based on the evidence from various sources. A higher score is assigned for interactions which are supported by several types of evidence. Generally, these scores are broadly classified into three, namely, “low-scores” for value less than 0.4, “medium-scores” for values between 0.4 and 0.7, and “high-scores” for those associations whose values are greater than 0.7. The existence of false positives and false negatives is widely anticipated in the networks of these types which are being constructed by using the currently available methods [13]. A recent comprehensive study has also indicated that the protein-protein interaction networks generated from STRING database are of low quality consisting of a significant amount of false positives and false negatives [19]. All interactions with value of “low-scores” have been removed from this study to minimize the impact of the problem. The resulting network contains 64,428 interactions among 3,958 proteins. Of the total 64,428 interactions, 22,395 were labeled as “high-score” and 42,033 as “medium-score.” Despite of its shortcomings, this network provides a good framework for navigation through the proteome and it also allows for refinement of the network upon the availability of new experimental data.Statistical properties of the generated network have been shown in Table1 to describe its essential properties. The characteristic path length of the network, which is the average distance between all pairs of nodes, is smaller than log⁡(n). This implies that theMycobacterium tuberculosis H37Rv proteome interaction network has “small world property” [20]. This property provides an idea about the network’s navigability by indicating how fast information can be communicated in the system irrespective of the number of nodes. Thus, from this small world property of the network, we can understand that the network is efficient in the communication of biological information. This means one protein can have an influence on another with only a small number of intermediate reactions. The shortest path length distribution between pairwise protein interactions has been shown in Figure 2. As the degree distribution of the resulting network has also been shown in Figure 3, it exhibits scale-free property like many biological networks in which the degree distribution of proteins approximates a power law p(k)=k-γ, with the degree exponent γ~1.38. So there are very rare highly connected nodes called hubs in a vast majority of nodes with only a few connections. Theclustering coefficient of the resulting network is significantly higher than theclustering coefficient of a random graph with the same number of vertices (0.008).Table 1
Network statistics.
Parameter
Value
Number of nodes (n)
3958
Connected components
8
Network diameter
10
Average number of neighbours
32.556
Network density
0.008
Network heterogeneity
0.942
Shortest paths
15519694 (99%)
Characteristic path length
3.096
Clustering coefficient
0.294Figure 2
Shortest path length distribution. Distribution of shortest path lengths between reachable pairwise protein interactions.Figure 3
Node degree distribution. The distribution of the probabilityp(k) that the degree of a randomly chosen vertex equals k has been shown and it follows a power law p(k)∼k.


### 3.2.2. Network Centrality Analysis

Comparative genome analysis was helpful in filtering out 572 nonhomologous and essential proteins ofMycobacterium tuberculosis H37Rv. However, this set is still very large to validate with the aid of experimental methods. The network centrality measures have been used for further ranking and prioritizing these proteins in the generated proteome interactome network. The objective was to order the proteins such that the most important proteins can be used first in an experiment. This has been done with the subsequent steps of sorting all of the proteins in the generated network, filtering proteins that are found near to the center of gravity and identifying the ordered list from nonhomologous and essential proteins which are found in the filtered list. For longitudinal comparison of centralities, the distribution of betweenness value of sorted proteins has been indicated in Figure 4. The diagram shows the number of proteins located in separate score intervals of the network. Betweenness centrality metric is one of the significant indicators of network essentiality because proteins with high betweenness are essential for the functioning of the system by serving as a bridge of communication between several other proteins in the network [21]. In this investigation, we tried to identify proteins which are found near to the centre of gravity of the proteome network by being connected with influential proteins. Since the characteristic path length of the generated network is 3.096, a protein is said to be at the centre of gravity if its betweenness measure is above the total number of shortest paths expected to pass through the protein in the functional network of interest, which is 12253.968. This criterion has been effectively used by Mazandu and Mulder in identification of potential drug targets ofMycobacterium tuberculosis [22]. With the aid of this principle we have got 137 ranked, essential, nonhomologous, and central proteins which we believe to be reliable targets forMycobacterium tuberculosis H37Rv. The detailed list of these potential drug targets incorporating the network centrality measure scores and validation is provided as Supplementary Material available online at http://dx.doi.org/10.1155/2015/212061. The lists of refined targets were further categorized by high level of functional classes and the distribution of these potential drug targets per functional class is shown in Figure 5. The distribution indicated that most of the candidate drug targets are involved in cell wall and cell processes, followed by a significant proportion of proteins in intermediary metabolism and respiration, conserved hypothetical, and those belong to information pathway.Figure 4
Distribution of betweenness centrality values. Longitudinal comparison of centrality values which shows the number of proteins located in separate score intervals based on betweenness centrality measure.Figure 5
It illustrates the comprehensive list of central, nonhomologous, and essential proteins as potential drug targets. The candidate lists have been classified into their high level functional class and the distribution has been indicated in the diagram.The resulting lists of candidate proteins were assessed by comparing with some of known drug targets as well as potential targets predicted by using different computational and experimental methods. The dataset for this purpose was obtained by integrating manually curated targets from TDR, high confidence targets from UniProt, and attractive targets obtained by Raman et al. [29] through a series of comprehensive filters. We have also used the potential drug targets list identified in our previous investigation [23]. The Venn diagram (Figure 6) shows the overlaps among these lists of drug targets and the proposed potential target list. Based on this assessment, 43 proteins in the list were TDR validated targets, 6 of which were in the UniProt target list. An additional of 18 proteins in our list were overlapped with UniProt’s list, 5 of which were also predicted by Raman et al.; their list contains 2 more proteins. From our previous report 56 proteins were overlapped with the current candidates; some of them were already reported as potential targets by other methods. Moreover, there are four known targets of existing antitubercular drugs within this set. They are Rv1908c (KatG) (ranked 12th in the proposed list), Rv3795 (EmbB) (ranked 15th), Rv3793 (ranked 25th), and Rv3794 (ranked 30th). Rv1908c (KatG) is a validated drug target of Isoniazid whereas Rv3793, Rv3794 are target proteins of Ethambutol [24]. Rv3795 (EmbB) is a drug target for Rifampin, Isoniazid, and Ethambutol. Therefore, 67 (48.9%) proteins from our proposed list have been previously predicted or reported to be drug targets by the stated methods.Figure 6
Venn diagram of proteins in the proposed list that are reported by other methods.The lists of top 20 proteins according to each of the four centrality measures have been obtained. From these lists, 10 of the proteins are found to be common and they are listed in Table2. It is hypothesised that these proteins are better targets since they have been identified in higher ranks of the four centrality measures of the interactome network. Additional information about each protein such as function, gene name, whether it has been reported as a drug target by other methods, and interaction with the host can be referred from the Supplementary Material.Table 2
Proteins in the top 20 of all of the four centrality measures.
Protein
Functional class
Network centrality scores
PDB
Betweenness
Eigenvector
Degree
Closeness
Rv1303
Cell wall and cell processes
115190.99
0.1066408
207
0.0490584
Rv3019c
Cell wall and cell processes
71224.33
0.08889492
167
0.0488181
3H6P
Rv0311
Conserved hypotheticals
67942.516
0.07391167
156
0.048861504
Rv0556
Cell wall and cell processes
66400.05
0.103606604
193
0.049006738
Rv0451c
Cell wall and cell processes
66230.336
0.09615834
171
0.04883437
2LW3
Rv0288
Cell wall and cell processes
63475.453
0.09102694
170
0.048855472
2KG7
Rv0875c
Cell wall and cell processes
49197.566
0.11939961
197
0.04895823
Rv1274
Cell wall and cell processes
46747.54
0.11324064
197
0.04900249
Rv0817c
Cell wall and cell processes
40821.99
0.11914455
196
0.048973985
Rv0358
Conserved hypotheticals
40230.57
0.07967692
154
0.048827738Additionally, potential drug targets of the pathogen that interact with the host have been identified to understand the infection mechanism using a dataset obtained from a computational prediction ofHomo sapiens-Mycobacterium tuberculosis H37Rv protein-protein interactions [25]. This dataset is thought as a golden dataset for host-pathogen interaction. As it has been shown in Table 3, 15 proteins from the proposed target lists interact with human. The reason for the presence of only few overlaps could be due to the fact that the host-pathogen interaction dataset is not comprehensive or the host interacting proteins are not necessarily essential to the pathogen and nonhomologous with human. Identifying proteins of the pathogen participating in the complex interplay with the host could significantly increase the reliability of the targets since these interactions are key factors in determining the outcome of the infection [26].Table 3
Proposed targets which interact with the host.
Protein
Functional class
Betweenness
Eigenvector
Degree
Closeness
Rv1599
Intermediary metabolism and respiration
58334.63
0.002786182
82
0.048330363
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
Rv2150c
Cell wall and cell processes
37525.4
0.007632928
103
0.048433885
Rv3921c
Cell wall and cell processes
29409.086
0.006799817
68
0.048375268
Rv0732
Cell wall and cell processes
26715.15
0.011198077
111
0.04828495
Rv1415
Intermediary metabolism and respiration
23617.264
0.002809845
57
0.048159193
Rv2534c
Information pathways
22585.9
0.010742613
123
0.04837822
Rv2553c
Cell wall and cell processes
21154.973
0.004895513
53
0.04829615
Rv1602
Intermediary metabolism and respiration
20447.479
0.002363527
49
0.048105914
Rv1611
Intermediary metabolism and respiration
17798.758
0.002891334
57
0.048196144
Rv2455c
Intermediary metabolism and respiration
16318.1045
0.00160155
54
0.04806034
Rv3601c
Intermediary metabolism and respiration
15341.172
0.00251573
49
0.048123464
Rv2538c
Intermediary metabolism and respiration
14206.7705
0.002324578
65
0.0481065
Rv2987c
Intermediary metabolism and respiration
13133.913
0.001755499
52
0.04798341
Rv1712
Intermediary metabolism and respiration
12815.694
0.002191681
62
0.048019513Further,the study of Mycobacterium tuberculosis virulence is another path which has got much attention in the design of drugs with a new mechanism of action, the production of modern concepts, and tuberculosis treatment schemes [27]. Virulence factors have evolved as a response to the host immune reaction. In recent times, manymycobacterial virulence genes that are essential for the virulence ofMycobacterium tuberculosis complex (MTBC) species have been reported by a number of studies. Most of these genes either encode enzymes of several lipid pathways, cell surface proteins, regulators, and proteins of signal transduction systems or are involved inmycobacterial survival inside the aggressive microenvironment of the host macrophages. We took a compiled list of virulence genes from Forrellad et al. [27] and tried to observe the overlap with our proposed potential targets. It has been found out that five genes from the proposed potential target list are also reported as virulence genes. These genes have been shown in Table 4.Table 4
Genes reported as virulence factors.
Gene name
Rv number
Functional class
Betweenness
Eigenvector
Degree
Closeness
sigA
Rv2703
Information pathways
56665.156
0.004549677
80
0.04838473
katG
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
icl1
Rv0467
Intermediary metabolism and respiration
23928.854
0.002939687
53
0.048157435
pafA
Rv2097c
Intermediary metabolism and respiration
19334.732
0.00727773
57
0.048266105
ideR
Rv2711
Regulatory proteins
15661.199
0.005902891
41
0.04817209


## 3.2.1. General View ofMycobacterium tuberculosis H37Rv Proteome Network

A proteome-scale interaction network of proteins inMycobacterium tuberculosis H37Rv was generated from STRING database which is claimed to be a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions [16]. The interactions are weighted and integrated from various sources like experimental repositories, computational prediction methods, and public text collections which makes it an acting comprehensive metadatabase that maps all interaction evidence onto a common set of genomes and proteins. In this database, a combined score has been assigned for each protein-protein linkage based on the evidence from various sources. A higher score is assigned for interactions which are supported by several types of evidence. Generally, these scores are broadly classified into three, namely, “low-scores” for value less than 0.4, “medium-scores” for values between 0.4 and 0.7, and “high-scores” for those associations whose values are greater than 0.7. The existence of false positives and false negatives is widely anticipated in the networks of these types which are being constructed by using the currently available methods [13]. A recent comprehensive study has also indicated that the protein-protein interaction networks generated from STRING database are of low quality consisting of a significant amount of false positives and false negatives [19]. All interactions with value of “low-scores” have been removed from this study to minimize the impact of the problem. The resulting network contains 64,428 interactions among 3,958 proteins. Of the total 64,428 interactions, 22,395 were labeled as “high-score” and 42,033 as “medium-score.” Despite of its shortcomings, this network provides a good framework for navigation through the proteome and it also allows for refinement of the network upon the availability of new experimental data.Statistical properties of the generated network have been shown in Table1 to describe its essential properties. The characteristic path length of the network, which is the average distance between all pairs of nodes, is smaller than log⁡(n). This implies that theMycobacterium tuberculosis H37Rv proteome interaction network has “small world property” [20]. This property provides an idea about the network’s navigability by indicating how fast information can be communicated in the system irrespective of the number of nodes. Thus, from this small world property of the network, we can understand that the network is efficient in the communication of biological information. This means one protein can have an influence on another with only a small number of intermediate reactions. The shortest path length distribution between pairwise protein interactions has been shown in Figure 2. As the degree distribution of the resulting network has also been shown in Figure 3, it exhibits scale-free property like many biological networks in which the degree distribution of proteins approximates a power law p(k)=k-γ, with the degree exponent γ~1.38. So there are very rare highly connected nodes called hubs in a vast majority of nodes with only a few connections. Theclustering coefficient of the resulting network is significantly higher than theclustering coefficient of a random graph with the same number of vertices (0.008).Table 1
Network statistics.
Parameter
Value
Number of nodes (n)
3958
Connected components
8
Network diameter
10
Average number of neighbours
32.556
Network density
0.008
Network heterogeneity
0.942
Shortest paths
15519694 (99%)
Characteristic path length
3.096
Clustering coefficient
0.294Figure 2
Shortest path length distribution. Distribution of shortest path lengths between reachable pairwise protein interactions.Figure 3
Node degree distribution. The distribution of the probabilityp(k) that the degree of a randomly chosen vertex equals k has been shown and it follows a power law p(k)∼k.


## 3.2.2. Network Centrality Analysis

Comparative genome analysis was helpful in filtering out 572 nonhomologous and essential proteins ofMycobacterium tuberculosis H37Rv. However, this set is still very large to validate with the aid of experimental methods. The network centrality measures have been used for further ranking and prioritizing these proteins in the generated proteome interactome network. The objective was to order the proteins such that the most important proteins can be used first in an experiment. This has been done with the subsequent steps of sorting all of the proteins in the generated network, filtering proteins that are found near to the center of gravity and identifying the ordered list from nonhomologous and essential proteins which are found in the filtered list. For longitudinal comparison of centralities, the distribution of betweenness value of sorted proteins has been indicated in Figure 4. The diagram shows the number of proteins located in separate score intervals of the network. Betweenness centrality metric is one of the significant indicators of network essentiality because proteins with high betweenness are essential for the functioning of the system by serving as a bridge of communication between several other proteins in the network [21]. In this investigation, we tried to identify proteins which are found near to the centre of gravity of the proteome network by being connected with influential proteins. Since the characteristic path length of the generated network is 3.096, a protein is said to be at the centre of gravity if its betweenness measure is above the total number of shortest paths expected to pass through the protein in the functional network of interest, which is 12253.968. This criterion has been effectively used by Mazandu and Mulder in identification of potential drug targets ofMycobacterium tuberculosis [22]. With the aid of this principle we have got 137 ranked, essential, nonhomologous, and central proteins which we believe to be reliable targets forMycobacterium tuberculosis H37Rv. The detailed list of these potential drug targets incorporating the network centrality measure scores and validation is provided as Supplementary Material available online at http://dx.doi.org/10.1155/2015/212061. The lists of refined targets were further categorized by high level of functional classes and the distribution of these potential drug targets per functional class is shown in Figure 5. The distribution indicated that most of the candidate drug targets are involved in cell wall and cell processes, followed by a significant proportion of proteins in intermediary metabolism and respiration, conserved hypothetical, and those belong to information pathway.Figure 4
Distribution of betweenness centrality values. Longitudinal comparison of centrality values which shows the number of proteins located in separate score intervals based on betweenness centrality measure.Figure 5
It illustrates the comprehensive list of central, nonhomologous, and essential proteins as potential drug targets. The candidate lists have been classified into their high level functional class and the distribution has been indicated in the diagram.The resulting lists of candidate proteins were assessed by comparing with some of known drug targets as well as potential targets predicted by using different computational and experimental methods. The dataset for this purpose was obtained by integrating manually curated targets from TDR, high confidence targets from UniProt, and attractive targets obtained by Raman et al. [29] through a series of comprehensive filters. We have also used the potential drug targets list identified in our previous investigation [23]. The Venn diagram (Figure 6) shows the overlaps among these lists of drug targets and the proposed potential target list. Based on this assessment, 43 proteins in the list were TDR validated targets, 6 of which were in the UniProt target list. An additional of 18 proteins in our list were overlapped with UniProt’s list, 5 of which were also predicted by Raman et al.; their list contains 2 more proteins. From our previous report 56 proteins were overlapped with the current candidates; some of them were already reported as potential targets by other methods. Moreover, there are four known targets of existing antitubercular drugs within this set. They are Rv1908c (KatG) (ranked 12th in the proposed list), Rv3795 (EmbB) (ranked 15th), Rv3793 (ranked 25th), and Rv3794 (ranked 30th). Rv1908c (KatG) is a validated drug target of Isoniazid whereas Rv3793, Rv3794 are target proteins of Ethambutol [24]. Rv3795 (EmbB) is a drug target for Rifampin, Isoniazid, and Ethambutol. Therefore, 67 (48.9%) proteins from our proposed list have been previously predicted or reported to be drug targets by the stated methods.Figure 6
Venn diagram of proteins in the proposed list that are reported by other methods.The lists of top 20 proteins according to each of the four centrality measures have been obtained. From these lists, 10 of the proteins are found to be common and they are listed in Table2. It is hypothesised that these proteins are better targets since they have been identified in higher ranks of the four centrality measures of the interactome network. Additional information about each protein such as function, gene name, whether it has been reported as a drug target by other methods, and interaction with the host can be referred from the Supplementary Material.Table 2
Proteins in the top 20 of all of the four centrality measures.
Protein
Functional class
Network centrality scores
PDB
Betweenness
Eigenvector
Degree
Closeness
Rv1303
Cell wall and cell processes
115190.99
0.1066408
207
0.0490584
Rv3019c
Cell wall and cell processes
71224.33
0.08889492
167
0.0488181
3H6P
Rv0311
Conserved hypotheticals
67942.516
0.07391167
156
0.048861504
Rv0556
Cell wall and cell processes
66400.05
0.103606604
193
0.049006738
Rv0451c
Cell wall and cell processes
66230.336
0.09615834
171
0.04883437
2LW3
Rv0288
Cell wall and cell processes
63475.453
0.09102694
170
0.048855472
2KG7
Rv0875c
Cell wall and cell processes
49197.566
0.11939961
197
0.04895823
Rv1274
Cell wall and cell processes
46747.54
0.11324064
197
0.04900249
Rv0817c
Cell wall and cell processes
40821.99
0.11914455
196
0.048973985
Rv0358
Conserved hypotheticals
40230.57
0.07967692
154
0.048827738Additionally, potential drug targets of the pathogen that interact with the host have been identified to understand the infection mechanism using a dataset obtained from a computational prediction ofHomo sapiens-Mycobacterium tuberculosis H37Rv protein-protein interactions [25]. This dataset is thought as a golden dataset for host-pathogen interaction. As it has been shown in Table 3, 15 proteins from the proposed target lists interact with human. The reason for the presence of only few overlaps could be due to the fact that the host-pathogen interaction dataset is not comprehensive or the host interacting proteins are not necessarily essential to the pathogen and nonhomologous with human. Identifying proteins of the pathogen participating in the complex interplay with the host could significantly increase the reliability of the targets since these interactions are key factors in determining the outcome of the infection [26].Table 3
Proposed targets which interact with the host.
Protein
Functional class
Betweenness
Eigenvector
Degree
Closeness
Rv1599
Intermediary metabolism and respiration
58334.63
0.002786182
82
0.048330363
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
Rv2150c
Cell wall and cell processes
37525.4
0.007632928
103
0.048433885
Rv3921c
Cell wall and cell processes
29409.086
0.006799817
68
0.048375268
Rv0732
Cell wall and cell processes
26715.15
0.011198077
111
0.04828495
Rv1415
Intermediary metabolism and respiration
23617.264
0.002809845
57
0.048159193
Rv2534c
Information pathways
22585.9
0.010742613
123
0.04837822
Rv2553c
Cell wall and cell processes
21154.973
0.004895513
53
0.04829615
Rv1602
Intermediary metabolism and respiration
20447.479
0.002363527
49
0.048105914
Rv1611
Intermediary metabolism and respiration
17798.758
0.002891334
57
0.048196144
Rv2455c
Intermediary metabolism and respiration
16318.1045
0.00160155
54
0.04806034
Rv3601c
Intermediary metabolism and respiration
15341.172
0.00251573
49
0.048123464
Rv2538c
Intermediary metabolism and respiration
14206.7705
0.002324578
65
0.0481065
Rv2987c
Intermediary metabolism and respiration
13133.913
0.001755499
52
0.04798341
Rv1712
Intermediary metabolism and respiration
12815.694
0.002191681
62
0.048019513Further,the study of Mycobacterium tuberculosis virulence is another path which has got much attention in the design of drugs with a new mechanism of action, the production of modern concepts, and tuberculosis treatment schemes [27]. Virulence factors have evolved as a response to the host immune reaction. In recent times, manymycobacterial virulence genes that are essential for the virulence ofMycobacterium tuberculosis complex (MTBC) species have been reported by a number of studies. Most of these genes either encode enzymes of several lipid pathways, cell surface proteins, regulators, and proteins of signal transduction systems or are involved inmycobacterial survival inside the aggressive microenvironment of the host macrophages. We took a compiled list of virulence genes from Forrellad et al. [27] and tried to observe the overlap with our proposed potential targets. It has been found out that five genes from the proposed potential target list are also reported as virulence genes. These genes have been shown in Table 4.Table 4
Genes reported as virulence factors.
Gene name
Rv number
Functional class
Betweenness
Eigenvector
Degree
Closeness
sigA
Rv2703
Information pathways
56665.156
0.004549677
80
0.04838473
katG
Rv1908c
Virulence, detoxification, and adaptation
50548.035
0.005637622
76
0.048456423
icl1
Rv0467
Intermediary metabolism and respiration
23928.854
0.002939687
53
0.048157435
pafA
Rv2097c
Intermediary metabolism and respiration
19334.732
0.00727773
57
0.048266105
ideR
Rv2711
Regulatory proteins
15661.199
0.005902891
41
0.04817209


## 3.3. Structural Assessment

One of the main criteria which increase the targetability of the prioritized lists of proteins is the availability of crystal structures. The Protein Data Bank (PDB) is freely accessible and the main worldwide repository for the three-dimensional structural data of biological macromolecules such as proteins and nucleic acids which is typically obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and biochemists from around the world [28]. By excluding those proteins which have more than 70% sequence identity, only 229 (about 6%) ofMycobacterium tuberculosis proteins have solved structure in PDB [29]. Hence, we checked the availability of solved structures of the identified potential lists of targets and out of 137 proteins from our proposed target list, 28 were successfully mapped to 82 structures from PDB which is approximated to 20.44%. This list has also been shown in Table 5 including the corresponding centrality measure values and PDB IDs of structures. However, reliable structures of the pathogen could still be obtained by using theoretically calculated homology models.Table 5
Sorted lists of proteins proposed as potential drug targets which have solved structure.
Protein
Network centrality scores
PDB
Betweenness
Eigenvector
Degree
Closeness
Rv3019c
71224.33
0.08889492
167
0.0488181
3H6P
Rv0451c
66230.336
0.09615834
171
0.0488344
2LW3
Rv0288
63475.453
0.09102694
170
0.0488555
2KG7
Rv0058
57680.18
0.00275039
62
0.0481486
2R5U
Rv1908c
50548.035
0.00563762
76
0.0484564
1SFZ; 1SJ2; 2CCA; 2CCD; 4C50; 4C51
Rv2050
40088.305
0.01941196
91
0.0485105
2M4V; 2M6P
Rv2150c
37525.4
0.00763293
103
0.0484339
1RLU; 1RQ2; 1RQ7; 2Q1X; 2Q1Y; 4KWE
Rv3793
36733.14
0.06872068
136
0.0487904
3PTY
Rv2111c
35411.24
0.00444417
37
0.0479427
3M91; 3M9D
Rv0002
33375.504
0.00362037
90
0.0482796
3P16; 3RB9
Rv3597c
27758.719
0.01894281
73
0.048406
2KNG; 4E1P; 4E1R
Rv1837c
24302.383
0.00201247
47
0.0479375
2GQ3; 3S9I; 3S9Z; 3SAD; 3SAZ; 3SB0
Rv0467
23928.854
0.00293969
53
0.0481574
1F61; 1F8I; 1F8M
Rv3240c
22790.56
0.00369075
64
0.0481586
1NKT; 1NL3
Rv0216
20616.273
0.01939864
57
0.0482126
2BI0
Rv1611
17798.758
0.00289133
57
0.0481961
3QJA; 3T40; 3T44; 3T55; 3T78; 4FB7
Rv2773c
17351.877
0.00189925
52
0.0480259
1C3V; 1P9L; 1YL5; 1YL6; 1YL7
Rv0902c
16640.244
0.02111599
42
0.0480668
1YS3; 1YSR
Rv2711
15661.199
0.00590289
41
0.0481721
1B1B; 1FX7; 1U8R; 2ISY; 2ISZ; 2IT0
Rv3601c
15341.172
0.00251573
49
0.0481235
2C45
Rv2518c
14995.93
0.00281853
25
0.0477322
3VYN; 3VYO; 3VYP; 4GSQ; 4GSR;4GSU; 4HU2; 4HUC
Rv0736
14264.871
0.01536277
50
0.0483009
3HUG
Rv2538c
14206.7705
0.00232458
65
0.0481065
3QBD; 3QBE
Rv3808c
13873.782
0.00814938
51
0.0480131
4FIX; 4FIY
Rv2416c
13419.646
0.00771849
39
0.0482891
3R1K; 3RYO; 3SXO; 3UY5
Rv2987c
13133.913
0.0017555
52
0.0479834
3H5E; 3H5H; 3H5J
Rv2986c
13109.861
0.00315177
40
0.0481223
4DKY; 4PT4
Rv2391
12401.702
0.00120635
45
0.0478749
1ZJ8; 1ZJ9


## 4. Assessment of the Method

It would be ideal to have standard validation data in order to assess the performance of the four centrality measures used in this analysis, but it is not readily available. The list of essential proteins obtained through a comparative analysis has been used as a test data. Since the main objective of centrality measures in a network is to identify the proteins which are influential, taking this data for evaluation is reasonable. These centrality measures were compared with other typical centrality measures: Local Average Connectivity- (LAC-) based method, Network Centrality (NC), Subgraph Centrality (SC), and Information Centrality (IC). As it can be seen in the jack-knife line chart (Figure7), there is no huge difference among the eight centrality measures with the AUC value of degree centrality the highest of all. Information and closeness centralities ranked second and third, respectively.Figure 7
Jack-knife line chart of eight centrality measures. The cumulative count of essential proteins of eight different centrality measures has been shown to assess the performance of the four centrality measures used for this analysis.Other testing data, including validated drug targets and intersection of high confidence targets from UniProt and attractive targets from Raman et al. [29], were identified. This list contains 47 proteins. Then, the eight centrality measures were compared in terms of the average rank of the drug targets in which lower average rank indicates better performance. The absolute count of drug targets in 1% of all candidate proteins (practically in the top 40 proteins), in the top 5% (practically in the top 198 proteins), in the top 10% (practically in the top 396 proteins), in the top 15% (practically in the top 594 proteins), and in the top 20% (practically in the top 792 proteins) among all candidates was reported (Table 6). For instance, in the top 1%, betweenness centrality identified 2 drug targets while the others found 1. Eigenvector identified joint maximum potential targets in all of top 5%, 10%, 15%, and 20%. We took up to 20% for comparison because these proteins are found near the center of gravity values.Table 6
Number of drug targets and its average position among different methods in top 1%, 5%, 10%, 15%, and 20% of the candidate proteins list.
Method
1% (40)
5% (198)
10% (396)
15% (594)
20% (792)
LAC
0
6
9
12
15
Subgraph
1
8
13
19
23
Information
1
6
9
13
17
Network
1
6
9
12
15
Eigenvector
1
8
13
19
23
Betweenness
2
7
10
14
18
Degree
1
6
9
13
17
Closeness
2
5
10
15
22


## 5. Conclusion

In this study we have identified a list of proteins which could be an attractive and reliable target forMycobacterium tuberculosis H37Rv through a comprehensive analysis of comparative genome and network centrality measures of protein-protein interaction network. The comparative genome analysis has helped in identifying those lists of proteins which were stated as essential for the survival and growth of the pathogen to increase success rate of drugs to be designed. It was also useful in filtering out those proteins which are present in human to eliminate all those with a risk of causing host toxicity. In traditional drug discovery the side effect or drug safety has normally been addressed by making modification on the drug molecule, but systematic way of dealing with this problem at the drug target identification phase in the modern rational drug discovery process seems to be more effective [13]. These refined lists of proteins were then analyzed by network centrality measures to prioritize the identified lists of candidate protein targets by hypothesising that the proteins that are at the centre of gravity of the disease specific protein-protein interaction network are more important proteins in the pathogen and hence more likely to be attractive targets. The comparison of these lists of targets with some of known drug targets as well as potential targets predicted by using different computational and experimental methods revealed that about half of them have been previously predicted or reported to be potential drug targets forMycobacterium tuberculosis H37Rv. The structural assessment of these proteins has also showed those which have an experimentally solved three-dimensional structure which increases their targetability. In general, we believe that this comprehensive analysis will have significant contribution in providing an important input for the experimental study of developing new antibiotics for infamousMycobacterium tuberculosis H37Rv pathogen.



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*Source: 212061-2015-11-05.xml*