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<title>Improving the prediction of yeast protein function using weighted protein-protein interactions</title>
<meta name="Subject" content="Theoretical Biology and Medical Modelling 2011, 8:11. doi:10.1186/1742-4682-8-11"/>
<meta name="Keywords" content=" "/>
<meta name="Author" content="Khaled S Ahmed"/>
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<pre>
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
RESEARCH
Open Access
Improving the prediction of yeast protein
function using weighted protein-protein
interactions
Khaled S Ahmed1,3†, Nahed H Saloma2† and Yasser M Kadah3*
* Correspondence: ymk@k-space.
org
3
Department of Biomedical
Engineering, Cairo University, Giza,
(12613), Egypt
Full list of author information is
available at the end of the article
Abstract
Background: Bioinformatics can be used to predict protein function, leading to an
understanding of cellular activities, and equally-weighted protein-protein interactions
(PPI) are normally used to predict such protein functions. The present study provides
a weighting strategy for PPI to improve the prediction of protein functions. The
weights are dependent on the local and global network topologies and the number
of experimental verification methods. The proposed methods were applied to the
yeast proteome and integrated with the neighbour counting method to predict the
functions of unknown proteins.
Results: A new technique to weight interactions in the yeast proteome is presented.
The weights are related to the network topology (local and global) and the number
of identified methods, and the results revealed improvement in the sensitivity and
specificity of prediction in terms of cellular role and cellular locations. This method
(new weights) was compared with a method that utilises interactions with the same
weight and it was shown to be superior.
Conclusions: A new method for weighting the interactions in protein-protein
interaction networks is presented. Experimental results concerning yeast proteins
demonstrated that weighting interactions integrated with the neighbor counting
method improved the sensitivity and specificity of prediction in terms of two
functional categories: cellular role and cell locations.
Background
Determining protein functions is an important challenge in the post-genomic era and
Automated Function Prediction is currently one of the most active research fields. Previously, researchers have attempted to determine protein functions using the structure
of the protein and comparing it with similar proteins. Similarities between the protein
and homologues from other organisms have been investigated to predict functions.
However, the diversity of homologues meant that these time-consuming methods were
inaccurate. Other techniques to predict protein functions including analyzing gene
expression patterns [1,2], phylogenetic profiles [3-5], protein sequences [6,7] and protein domains [8,9] have been utilised, but these technologies have high error rates,
leading to the use of integrated multi-sources [10,11]. The computational approach
was designed to resolve the inaccuracy of protein prediction, using information gained
from physical and genetic interaction maps to predict protein functions. Recently,
© 2011 Ahmed et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
researchers have introduced various techniques to determine the probability of protein
function prediction using information extracted from PPI. Results from these trials
have been promising, but they do not address effective problems including function
correlation [12-14], network topology and strength of interaction.
Network topology represents an interaction between proteins and the mechanism of
that interaction. Therefore, much information can be extracted from these networks
with regards to the strength of the interaction and its contribution to new function
prediction, i.e. weighted contribution. A PPI network can be described as a complex
system of proteins linked by interactions, and the computational analysis of PPI networks begins with the representation of the PPI network structure [15,16]. The simplest representation takes the form of a network graph consisting of nodes and edges
[17]. Proteins are represented as nodes and two proteins that interact physically are
represented as adjacent nodes connected by an edge [18]. On the basis of this graphical representation, various computational approaches including data mining, machine
learning and statistical methods can be performed to reveal the PPI networks at different levels.
The computational analysis of PPI networks is challenging and faces major problems.
The first problem concerns the unreliability of protein interactions derived from largescale experiments, which have yielded numerous false positive results (Y2H). Secondly,
a protein can have more than one function and could be considered in one or more
functional groups, leading to overlapping function clusters. The third problem concerns the fact that proteins with different functions may interact. Therefore, a PPI has
connections between proteins in different functional groups, leading to expansion of
the topological complexity of the PPI networks. Neighbour counting is a method proposed by Schwikowski et al. [19] to infer the functions of an un-annotated protein
from the PPI. This method locates the neighbour proteins and predicts their assigned
functions and the frequency of these functions; the functions are arranged in descending order according to their frequencies. The first k functions are considered and
assigned to the un-annotated protein. Some papers used this technique with k equalling three. This method makes use of information from the neighbours, but it has several drawbacks: (1) it considers the interactions to be of equal weights, which is not
logical; (2) it does not consider the nature of the function and whether it is dominant;
(3) it does not provide a confidence level for assigning a function to the protein. The
problem of confidence levels was addressed in [20], where the authors used chi-square
statistics to calculate significance levels on the basis of the probability that various
functions are present. The chi-square method provides a deeper analysis than the
neighbour counting method, but it is less sensitive and specific.
Deng et al. [21] considered various situations for the presence of a certain function
for a protein of interest: (1) number of proteins having this function; (2) number of
protein pairs (interacting) having the function; (3) number of protein pairs where one
has the function and the other does not; (4) number of protein pairs without this function. A weighted sum of these numbers is calculated according to the random Markov
field algorithm, which assigns different weights to interactions and overcomes the
above problems by considering the entire interaction network [21]. This method considers the frequency of proteins having the function of interest and the neighbours,
with less weight being placed on neighbours that are further away. Therefore, it can be
Page 2 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Page 3 of 17
used to calculate the probability that an un-annotated protein has a function of interest, and the results are more accurate than those obtained by using neighbour counting
or the chi-square method.
This paper presents a new method for predicting protein function based on estimating a weight for the strength of the interaction between proteins in the PPI. The similarity between protein interactions and the connected routers in a certain autonomous
number of networks was explored. Applying the idea of a network linked list of protocols such as OSPF (Open Shortest Path First) can allow information concerning surrounding routers to be obtained, according to the principles of cost and level (hop
count) [22,23]. The suggested algorithm was compared with the equal weight interactions method to indicate differences in the accuracy of prediction.
Results
The proposed approach was applied to infer the functions of un-annotated proteins in
yeast and used weighting interactions rather than free weights (equal interactions). In
YPD, proteins are assigned functions based on three criteria: “Biochemical function”,
“Subcellular location” and “Cellular role”. The numbers of annotated and un-annotated
proteins, based on the three functional categories, are presented in Table 1. The accuracy of the predictions was measured by the leave-one-out method. For each annotated
protein with at least one annotated interaction partner, it was assumed to be un-annotated and functions were predicted using the weighted neighbour counting method.
The predicted results were compared with the annotations of the protein. Repeating
the leave-one-out experiment for all such proteins allowed the specificity (SP) and sensitivity (SN) to be defined [22]. The corresponding values of overlapped proteins for
“Biochemical function”, “Subcellular location” and “Cellular role” were 1145, 1129 and
1407, respectively. In the first three Figures, the relationship between sensitivity and
specificity was implemented for biochemical function, cell location and cellular role,
respectively. In terms of the prediction method (neighbour counting method), a fixed
number of the highest frequency functions can be compared. In the present study,
although one data set is used, k (number of interactions) had a variety of values (from
2 to 5). Figures 1a-d demonstrate the specificity and sensitivity in terms of biochemical
function when k equals 2, 3, 4 and 5. In terms of biochemical functions (Figure 1), the
sensitivity of a proposed algorithm is higher when specificity values are low. However,
for higher specificity the weightless technique (W0) has good sensitivity. Therefore, an
established technique is sufficient for predicting biochemical function. As
Table 1 The numbers of annotated and un-annotated proteins for all proteins, based on
three functional categories: Biochemical function, cellular location and subcellular role.
Biochemical function
Annotated
3353
Un-annotated
3063
cellular location
Annotated
3181
Un-annotated
3235
Sub-Cellular role
Annotated
3894
Un-annotated
2522
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Figure 1 Biochemical function sensitivity and specificity. The sensitivity and specificity of the six
collected data (un-weighted and five weights) in the biochemical category for up to five interactions
(k = 5).
demonstrated in Figures 2 and 3, the sensitivity and specificity for all weights (new
suggested techniques W1-W5) were higher than W0 for all values of k. It can be
demonstrated that in the cell location function category, W2 (weight relating to IG1) is
the best weight to use when the number of interactions for each protein is two. W3
(weights for IG2), W1 (weights for number of experimental method) and W5 (PCA for
the basic three weights (W1, W2, W3)) were the best weights when the numbers of
interactions for each protein were 3, 4 or 5, respectively. Furthermore, W2 was the
best weight for the cellular role function category when the number of interactions
was two, and W3 (weights of IG2) were the best weights for the cellular role function
category when the numbers of interactions were 3, 4 or 5. There were overlaps
between some weights on the indicated curves (overlap curves), but there was a small
variation in terms of detecting these weights.
Conclusions
The majority of methods concerning the estimation of protein functions through protein-protein interactions (PPI) use the same weights for all interactions. Such methods
do not consider the various situations for each interaction including the number of
experimental methods used to identify the interactions, the number of leaves connected to the interaction (whether or not the protein is sticky) and the most common
graphs for the studied species within the network. Therefore, this research introduces
new weights for protein interactions to enhance protein function prediction. These
weights are W1-W5, and W1 depends of the number of experimental methods that
identify the interaction. W1 has high confidence (100%) when the number of experimental methods used is more than one. W2 depends on the number of leaves
Page 4 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Figure 2 Cell location function sensitivity and specificity. The sensitivity and specificity of the six
collected data (un-weighted and five weights) in cell location function for up to five interactions (k = 5).
Figure 3 Cellular role function sensitivity and specificity. The sensitivity and specificity of the six
collected data (un-weighted and five weights) in cellular role category for up to five interactions (k = 5).
Page 5 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
connected to the studied interactions, which indicates whether the protein is sticky or
not. The high confidence of W2 is apparent when the IG1 value is less than three (the
protein is not sticky). W3 relates to the value of IG2, which indicates the global topology of the network of the studied species; its value is highly confident when IG2 is less
than zero. In addition, there are two estimated weights, W4 and W5. W4 is the average of the basic weights (W1, W2 and W3), and W5 is the PCA value for the same
weights. Applying the suggested weights to yeast protein functions and integrating
these weights with the neighbor counting method led to enhanced results in two function categories: cell location and cellular role. The sensitivity and specificity of every
point on the curves of the two function categories were higher than those obtained
using the weightless technique (free or equal weights (W0)). W3 was the best weight
to use in the cellular role category when the numbers of interactions were 3, 4 or 5.
The cell location function category did not have a common weight for all cases but in
each case (number of interactions), there was a better weight compared with other
methods.
Methods
This paper introduces a novel algorithm by comparing the proteins in protein-protein
interaction networks to the connected routers in the same autonomous number of networking. The protein acts as a router, and the node and edge (interaction between two
proteins) act as the connection between two routers (Figures 4 and 5), where routers
have up to 100 interactions (29 interactions are the maximum in the yeast proteome).
As presented in Figure 4a, a group of routers and their movable messages are indicated, and the connected routers are presented in Figure 4b. In Figure 5, the group of
proteins are connected using different experimental methods. The routing system can
be introduced by various types of connections (LAN, WAN, Serial) as different experimental methods of interactions in the protein system. Initially, the router will be unaware of neighbour routers on the link. Therefore, the linked state protocol will be
applied to the routing system, where a link is an interface on a router and the protocols are the control system of all connected routers. The protocol includes information
concerning the interface’s IP address/mask, the type of network (ethernet (broadcast)
or serial point-to-point link), the cost of that link and any neighbour routers on that
Figure 4 Connected routers. Presentation of connected routers in a specific network.
Page 6 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Figure 5 Connected protein nodes. Seventeen connected proteins are depicted as a part of the real
interacting proteins database, where yellow nodes are leaves (last ones in the path).
link. In the protein system, a generic protocol is followed that identifies the protein by
name (gene name, locus name, accession name etc...), ID (determined number for each
protein), sequence (amino acids in given number and order) and functions (if known).
The type of network will be elucidated; interaction between two proteins (protein pair)
or dense interactions (cluster), and the weight of the interaction (our contribution).
Furthermore, neighbours of the adjacent protein (known interactions in the network)
are identified (Table 2). The protein interactions are calculated until the second level.
The algorithm is performed following four steps: (1)- determining the level and degree
for each adjacent protein, (2)- calculating the weight (cost) for each interaction (interaction with high cost/weight is strong), (3)- integrating these data to predict the function of the un-annotated proteins using the neighbourhood counting method, and (4)calculating the sensitivity and specificity for the different weights.
Protein level
There is a difference between the degree and the level of any node. The degree of a
node (protein) is defined as the total number of connected nodes or proteins directly
surrounding this node (protein A has degree equal to six) as shown in Figure 6; the
level of a node is the layer of nodes related to the main one. The directed nodes have
a level equal to one, and their neighbours are the second level as presented in Figure
6. The red nodes are the first level of protein A (black), the second level of proteins
are the yellow coloured nodes (nodes connected to protein’s A neighbours). The last
(third) level is the group of proteins coloured green. In router networks, the hop count
principle is performed to determine the router level. In this paper, the second level
was assumed to be sufficient for extracting the most important information about the
Page 7 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Page 8 of 17
Table 2 sample of proteins and their interactions
Protein ID
# interactions
p1
p2
p3
p4
p5
p6
p7
P8
p9
32
1
3258
0
0
0
0
0
0
0
0
p10
0
33
23
19
33
33
84
304
333
370
407
568
1065
34
17
56
475
1118
1277
2027
3350
3352
3342
3346
3347
35
0
0
0
0
0
0
0
0
0
0
0
36
5
36
36
2557
3092
4052
0
0
0
0
0
37
0
0
0
0
0
0
0
0
0
0
0
38
0
0
0
0
0
0
0
0
0
0
0
39
40
0
1
0
3802
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
41
3
1726
3275
386
0
0
0
0
0
0
0
42
0
0
0
0
0
0
0
0
0
0
0
43
0
0
0
0
0
0
0
0
0
0
0
44
0
0
0
0
0
0
0
0
0
0
0
45
0
0
0
0
0
0
0
0
0
0
0
46
1
3708
0
0
0
0
0
0
0
0
0
47
48
1
0
4590
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
49
0
0
0
0
0
0
0
0
0
0
0
50
0
0
0
0
0
0
0
0
0
0
0
51
0
0
0
0
0
0
0
0
0
0
0
52
0
0
0
0
0
0
0
0
0
0
0
53
0
0
0
0
0
0
0
0
0
0
0
function of a protein. The concept of node level was applied to 2559 protein-protein
interactions between 6416 proteins collected from the Munich Information center of
Protein Sequences (MIPS, http://mips.gsf.de) for the yeast Saccharomyces cerevisiae
[24]. As demonstrated in Figure 7, proteins with ID numbers 1913, 3246 and 3517 had
a level equal to one for the studied protein number 1, and the yellow nodes are second
degree.
Figure 6 Protein levels. Protein A (black) and its surroundings, which were divided into three degrees or
levels (red nodes as first level, yellow as second level and green nodes as the third level).
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
Figure 7 Saccharomyces cerevisiae network. A part of the yeast Saccharomyces cerevisiae network (MIPS
database). The level of the nodes is distributed. The figure has been drawn using the Inter-Viewer program.
PPI weight calculation
Protein-protein interaction weights are introduced and each interaction has a specific
weight. Three basic methods were considered in terms of calculating the weights of all
the interactions and overcoming problems affecting the interaction network. The first
method concerns the number of experimental methods. Protein-protein interactions
are identified by high-throughput experimental methods such as Y2H [25-29], mass
spectrometry of co-immunoprecipitated protein complexes (Co-IP) [30,31], gene coexpression, TAP purification cross link, co-purification and biochemical methods.
Challenging technical problems arise using the first two methods, which lead to spurious interactions due to self activation in Y2H and abundant contaminants with COIP. These problems lead to false positive interactions [32]. Therefore, a quantitative
method for evaluating the pathway through proteomics data is required. A number of
experimental and computational approaches have been implemented for large-scale
mapping of PPIs to realize the potential of protein networks for systems analysis. One
method utilizes multiple independent sets of training positives to reduce the potential
bias of using a single training set; this method uses association with publishing identifiers or foundation in two or more species, otherwise PPIs must have an expression
correlation more than 0.6 [33]. Another technique also obtains conserved patterns of
protein interactions in multiple species [34]. There are several methods for determining the reliability of interactions [35-38]. In this paper, the reliability or confidence is
introduced by counting the number of experimental methods for each interaction;
some interactions have been identified using many experimental methods and others
identified by just one. In yeast proteins, approximately ten experimental methods can
Page 9 of 17
Ahmed et al. Theoretical Biology and Medical Modelling 2011, 8:11
http://www.tbiomed.com/content/8/1/11
be used to identify protein-protein interactions (Edge between Protein (YBR0904) and
Protein (YDR356W) can be identified by ten experimental methods where protein
(AAC1) and protein (YHR005C-A) can be identified by one method). As demonstrated
in Figure 8, approximately 750 interactions from 2559 proteins have been identified by
more than one experimental method. More than half of all the interactions have been
identified by just one method (~1800 interactions); researchers have high confidence
(100%) concerning those interactions identified by more than one method and 50%
confidence for the others (one method identification). Table 3 presents the yeast protein interactions, the number of experimental methods used to identify them and the
identification value for each one. This method does not depend on clear points on
computational algorithms, but reflects the strength of interaction from the laboratory
viewpoint. Another approach for estimating the reliability of experimental methods
concerns calculating the stability of every method.
The second method for calculating weights of interactions is the IG1 concept (Interaction Generality 1) [39-41]. A new method for assessing the reliability of protein-protein interactions (local topology) is obtained in biological experiments by calculating
the number of proteins involved in a given interaction (number of protein leaves connecting to the two studied proteins incremented by one) as shown in Figure 9. IG1
assumes that complicated interaction networks are likely to be true positives. By implementing the IG1 on the collected data (yeast protein interactions), the range of IG1
was between one and 21 (Figure 10), meaning that some interactions have many leaves.
Figure 8 Interactions/Experimental methods relationships. Demonstrates the number of interactions
(edges) corresponding to the number of experimental methods (~1800 interactions can be identified by
one experimental method).
Page 10 of 17
Protein_1
Protein_2
# Identification Method
Y2H
Cross-link
affinity chromo
precipitation
assay
purification
in Vetro
Others
YKL161C
RLM1
1
1
-
-
-
-
-
-
-
AAC1
YHR005C-A
1
1
-
-
-
-
-
-
AAD14
AAD14
1
1
-
-
-
-
-
-
-
AAD6
ABP1
YNL201C
ACT1
1
3
1
1
-
1
-
-
1
-
-
ABP1
RVS167
4
1
-
-
1
-
-
-