Article

Mining Alzheimer Disease Relevant Proteins From Integrated Protein Interactome Data

Indiana University School of Informatics, Purdue University School of Science, Dept. of Computer and Information Science Indianapolis, IN 46202, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 02/2006; 11:367-78. DOI: 10.1142/9789812701626_0034
Source: PubMed
ABSTRACT
Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.

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1
MINING ALZHEIMER DISEASE RELEVANT PROTEINS FROM
INTEGRATED PROTEIN INTERACTOME DATA
JAKE YUE CHEN
Indiana University School of Informatics
Purdue University School of Science, Dept. of Computer and Information Science
Indianapolis, IN 46202, USA
CHANGYU SHEN
Division of Biostatistics, Indiana University School of Medicine
Indianapolis, IN 46202, USA
ANDREY Y. SIVACHENKO
Ariadne Genomics, Inc., 9700 Great Seneca Hwy
Rockville, MD 20850, USA
Huge unrealized post-genome opportunities remain in the understanding of detailed
molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a
computational method to rank-order AD-related proteins, based on an initial list of AD-
related genes and public human protein interaction data. In this method, we first collected
an initial seed list of 65 AD-related genes from the OMIM database and mapped them to
70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765
proteins using protein interactions from the Online Predicated Human Interaction
Database (OPHID). We showed that the expanded AD-related proteins form a highly
connected and statistically significant protein interaction sub-network. We further
analyzed the sub-network to develop an algorithm, which can be used to automatically
score and rank-order each protein for its biological relevance to AD pathways(s). Our
results show that functionally relevant AD proteins were consistently ranked at the top:
among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to
the original 70 AD seed protein set. Our method represents a novel use of protein
interaction network data for Alzheimer disease studies and may be generalized for other
disease areas in the future.
1. Introduction
Alzheimer Disease (AD) is a progressive neurodegenerative disease with 4.5
million patients in the United States today. This number of AD patients is
To whom correspondence should be sent. Email: jakechen@iupui.edu.
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 1
expected to increase to 11 to 16 million by 2050 when the baby boomers age.
The cognitive function of an AD patient deteriorates irreversibly over time and
complete care is required for basic daily activities in the late stages of the
disease. In 2000, health care costs for AD patients in the United States totaled
approximately $31.9 billion, which is expected to reach $49.3 billion by 2010
(above statistics can be found at http://www.alz.org
/). Therefore, AD is a major
and rapidly growing public health concern.
The exact molecular mechanisms leading to the clinical symptoms and
neuropathological changes associated with AD remain unclear. Selective brain
neuronal loss, extracellular amyloid (senile) plaques, and intracellular
neurofibrillary tangles (NFT) of hyperphosphorylated tau protein are
characteristically seen in the brains of AD patients [1, 2]. According to the
widely-accepted “amyloid hypothesis” [3, 4], an unusual accumulation of beta-
amyloid peptides (A!), cleavage products of the amyloid precursor proteins
(APP), are the major cause of AD in its earliest stages. In Familial Alzheimer
Disease (FAD), genetic defects code for abnormal variants of either the APP or
presenilin (PSEN1, PSEN2)— often leading to abnormal formation of A! as
“protofibrils” [5]. A! protofibrils can incite inflammatory response through
cytotoxic cytokines and disrupt intracellular Ca
2+
homeostasis through over-
activation of glutamate receptors, therefore leading cells to oxidative stress and
mitochondrial injury. A! protofibrils deposit in the extracellular space which
may also cause neuronal cell damage by blocking axonal transport. Aberrant A!
accumulation further causes aberrant accumulation of tau, a protein which
normally is essential to the initiation and stabilization of the neuronal
microtubules. As time going by, gradual breakdown of neuronal cytoskeleton
eventually leads to neuron apoptosis in AD patients (For a comprehensive
review, see [1, 2] and references therein). The complexity and broad range of
these cellular and biochemical events make researchers believe that there must
be a sophisticated network of AD signal transduction, gene regulation, and
protein-protein interaction events. Therefore, deciphering AD-related molecular
network “circuitry” can help researchers understand AD disease model details
and propose treatment ideas.
In this work, we will conduct initial AD-protein interaction network
analysis and demonstrate how to gain protein functional knowledge not directly
implied from sequence information. We will organize the main body of the
work by presenting our computational data analysis methods and results. We
will discuss potential interpretations and significance of our results at the end.
Pacific Symposium on Biocomputing 11:367-378(2006)
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2. Computational Method
We introduce the computational techniques and procedures developed for AD
protein interaction sub-network analysis, which can be summarized as follows.
First, we searched the Online Mendelian Inheritance in Man (OMIM) database
[6] to obtain an initial collection of AD-related genes. Second, we used the
HUGO Gene Nomenclature Committee (HGNC) [7] database to map the initial
AD-related genes to AD-related proteins identified by their SwissProt IDs.
Third, we used a nearest-neighbor expansion method to build an expanded AD
protein interaction sub-network. Fourth, we developed and applied a
bioinformatics software tool, ProteoLens [8], to visualize and annotate the AD
interaction sub-network. Fifth, we performed statistical analysis to assess the
significance of the subnetwork extracted. Sixth and lastly, we developed a
heuristic algorithm and scoring method, which we used to obtain a rank-ordered
list of proteins significantly related to the AD. A detailed description of our
method is provided below.
2.1. Initial Collection of AD Related Genes
We used the OMIM database [6] as the starting point to retrieve an initial
collection of AD related genes. In OMIM, human genes associated with genetic
disorders are recorded in a mini-review format, along with additional
information such as their functions, participating molecular pathways, and other
disease-related information. To obtain a list of AD-related genes, we performed
a search of the OMIM database (integrated into our biological data warehouse in
early 2004), retrieving each OMIM gene record in which the “description” field
contains the term “Alzheimer”. 65 OMIM gene records were retrieved. Note
that since the retrieval method is coarse (i.e., based on simple term matches), the
65 collected AD-related gene records may suffer from both false positives
(containing retrieved genes that are not actually functionally relevant to AD)
and false negatives (missing genes that are indeed functionally related to AD but
not retrieved). Soon in subsequent protein interaction network analysis, we will
use protein interaction network neighborhood information and show that these
concerns can be ameliorated.
2.2. Mapping of Initial AD Related Genes to Proteins
We used the HUGO Gene Nomenclature Committee (HGNC) [7] database of
gene symbols and proteins to map gene symbols to their correct protein
identifiers. HGNC is an international standard repository of officially approved
gene symbols. For each gene, the HGNC database provides its standard gene
symbol and gene mappings to various IDs used in common public databases,
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 3
e.g., Swiss-Prot, NCBI RefSeq, NCBI Locuslink, and KEGG enzyme. For our
work, we started with 65 sets of OMIM gene records, some of which were
associated with more than one gene symbol. After mapping all the gene symbols
to protein SwissProt IDs using the HGNC gene mapping table, we obtained 70
AD-related proteins. The slight increase in protein count is due to one-to-many
mapping between a gene and its multiple splice variant forms at the protein
level.
2.3. Collection and Expansion of AD Related Protein Interactions
We used the Online Predicted Human Interaction Database (OPHID) [9] to
collect AD-related protein interaction data. OPHID is a web database of more
than 40,000 human protein interactions involving ~9,000 human proteins. It is a
comprehensive repository of known human protein interactions, both from
curated literature publications and from high-throughput experiments. It also
contains predicted interactions inferred from eukaryotic model organisms, e.g.,
yeast, worm, fly, and mouse. The prediction was performed by mapping
interacting protein pairs from available model organisms onto their orthologous
protein pairs in human, or by making inference from interacting domain co-
occurrence, co-expression, and GO semantic distance evidence. More than half
of OPHID’s records are predicted human protein interactions; however, not all
OPHID human protein interactions carry the same level of significance. In
general, those derived from real human protein interaction experiments should
be much more trustworthy than those derived from predictive methods applied
on yeast data sets. Therefore, to assign an estimated interaction confidence
score, we developed the following heuristic scoring rules:
1. Protein interactions from human experimental measurement or from
literature curation are assigned a high confidence score of 0.9;
2. Human protein interactions inferred from high-quality interactions
in mammalian organisms are assigned a medium confidence score
of 0.5;
3. Human protein interactions inferred from low quality interactions or
non-mammalian organisms are assigned a low confidence score of
0.3.
With the initial AD-related protein list and a comprehensive OPHID protein
interaction data set, we can now derive a AD-related protein interaction sub-
network using a nearest-neighbor expansion method. Here, we denote the
initial 70 AD-related proteins as the seed-AD-set. To build AD sub-networks,
we pulled out protein interacting pairs in OPHID such that at least one member
of the pair belongs to the seed-AD-set. The set of interacting pairs pulled out
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 4
will be called the AD-interaction-set. We denote the new set of proteins
expanded from initial seed-AD-set by new proteins involved in the AD-
interaction-set as the enriched-AD-set (a superset of seed-AD-set). In our
study, the AD-interaction-set contains 775 human protein interactions; the
enriched-AD-set contains 657 human proteins identified by Swissprot IDs.
2.4. Visualization of AD Protein Interaction Sub-Network
We developed ProteoLens [10], a visual biological network data mining and
annotation tool that can be freely downloaded at
http://bio.informatics.iupui.edu/proteolens/
, to help us analyze the AD-related
protein interaction sub-network. ProteoLens has native built-in support for
relational database access and manipulations. It allows expert users to browse
database schemas and tables, filter and join relational data using SQL queries,
and customize any combination of data fields. The reconfigured view of data
can be immediately visualized in the ProteoLens network viewer without
needing to be exported as flat files first. Note that network nodes and edges can
be used to represent proteins and protein interactions, whereas node/edge size,
width, shape, and color can all be used to dynamically bind to customized data
fields (such as gene symbol, functional category, and confidence score) to be
visualized. Once a visual network layout is generated, the layout, visual
annotation, and network member proteins/protein interactions can be tweaked
without file editing.
2.5. Statistical Evaluation of Sub-network
We performed statistical data analysis tests to examine the significance of the
connected sub-network formed by AD-interaction-set. Our hypothesis for this
statistical evaluation is that if the enriched-AD-set indeed consists of
functionally related proteins involved in the same process—even if the process
were complex and broad—then we should expect that the connectivity among
the enriched-AD-set proteins to be higher than that among a set of randomly
selected proteins.
To formulate our hypothesis precisely, we introduce three concepts. First,
we define a path between two proteins A and B as a set of proteins P1, P2,…,
Pn such that A interacts with P1, P1 interacts with P2, …, and Pn interacts with
B. Note that if A directly interacts with B, then the path is the empty set.
Second, we define the largest connected sub-network of a network as the
largest subset of proteins and interactions, among which there is at least one
path between any two proteins in the subset. Third, we define the index of
aggregation of a network as the ratio of the size of the largest sub-network that
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 5
exists in this network to the size of this network. Note that size is calculated as
the total number of proteins within a given network/sub-network.
To test the hypothesis that the enriched-AD-set proteins are more
connected” than a randomly selected set of protein, we develop the null
hypothesis test using the following resampling procedure [11]:
1) Randomly select from the OPHID database, the same number of human
proteins as in the seed-AD-set.
2) Build the superset of the selected set by using the same nearest-neighbor
expansion method described earlier.
3) Find the largest sub-network of the superset.
4) Compute the index of aggregation of the superset.
5) Repeat steps 1 through 4 1,000 times to generate a distribution of the
index of aggregation under random selection.
6) Compare the index of aggregation of the enriched-AD-set with the
distribution obtained in 5 and calculate the p-value.
2.6. Scoring of Significant Proteins in the Sub-network
In the final step, we present a scoring method to rank proteins in the sub-
network, based on their overall roles and contribution to the AD related protein
interaction sub-network. The role of a protein in the sub-network can be
qualitatively defined as its ability to connect to many protein partners in the
network with high specificity (the less promiscuously connected, the better) and
high fidelity (the higher the interaction confidence, the better). To define this
role quantitatively, we introduce a heuristic relevance score function s
i
for each
protein i from the sub-network:
"
"
#
$
%
%
&
'
(
"
"
#
$
%
%
&
'
)
**
+,+, AiNjAiNj
i
jiNjipks
)()(
),(ln),(ln*
, (1)
In Eq. 1, i and j are indices for proteins in the sub-network, k is an empirical
constant (k>1; we set k=2 here), N(i) is the set of interaction partners of protein i
in the network, A is the set of proteins in enriched-AD-set, p(i,j) is the initial
confidence score that we assigned to each interaction between proteins i and j
(described in section 2.3)
, and N(i,j) holds the value of 1 if protein j belongs to
the intersection of
AiN +)( or 0 otherwise. Empirically assessing the relevance
score function, we can tell that the score s
i
ranks favorably in situations where
interacting proteins with many high confidence interactions among its neighbors
will fare out better than those with many low-quality interactions and those with
only a few interactions. To avoid showing a negative score, in this work, we
further converted s
i
to the exponential scale using the transformation t
i
= exp(s
i
),
and report t
i
as the final protein ranking score.
Pacific Symposium on Biocomputing 11:367-378(2006)
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3. Results
By following the data analysis steps outlined in the Method section, we obtained
the following results.
3.1. AD-Related Proteins and Protein Interactions
In the AD seed set, we have an initial list of 65 AD related OMIM gene records.
These records are later mapped to 70 seed-AD-set proteins using gene-to-
protein mapping tables from HGNC. As explained earlier, this discrepancy was
due to the one-to-many mapping relationships between genes and their protein
products. Using OPHID and the nearest-neighbor expansion method, we
obtained 775 AD-related human protein interactions (as the AD-interaction-
set). This expanded AD-interaction-set contains an expanded 657 human
proteins (as the enriched-AD-set).
The proteins in the enriched-AD-set form 16 sub-networks, with a size
ranging from 2 to 586 (or, a relative size from 0.3% to 89.2%). Therefore, the
largest connected sub-network of the enriched-AD-set contains 586 proteins and
the index of aggregation is 82.9%. This suggests that the majority of AD
related proteins are closely related by physical interaction—a phenomena that
we would like to test for statistical significance (see Section 3.3 )
3.2. Visualization of AD Expanded Protein Interaction Network
Figure 1 shows the enriched-AD-set proteins and the AD-interaction-set of
human protein interactions in visualized network. All the seed-AD-set proteins
(shown as nodes) are colored dark gray, while the non seed-AD-set proteins
(also shown as nodes) are colored light gray. Proteins with high ranking scores
(see Table 1 and the Discussion section) are also draw as nodes with sizes
proportion to the ranking score. All the protein interactions (shown as edges) are
also color-labeled, with high-quality interaction in black, medium-quality
interactions in dark gray, and low-quality interactions in gray. We observe that
interactions tend to “fan out” from a few protein hubs in the network, and that
there are relatively few interactions among the proteins extending from the seed-
AD-set. One expects that true AD-related proteins would interact with many
seed-AD-related proteins with high degree of confidence and specificity.
3.3. Statistical Significance
The empirical distribution of the index of aggregation obtained after 1000
random re-samplings is shown in Figure 2. Only 8 runs out of 1000 resulted in
an index of aggregation value greater than 89.2%. Therefore, the p-value of the
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 7
observed index of aggregation of the enriched-AD-set is 0.008. It is not
surprising to observe such a significant result since the AD-set is selected in a
way that proteins inside the set share certain level of connection since all of
them are associated with AD.
Figure 1. A network of OPHID human interactions expanded from initial 70 seed-AD-set (colored in
dark gray). Protein interactions are colored in different shades of gray according to confidence level
assigned, and protein node size as shown in proportion to their significance in the network (see text
for details).
Next, the relevance score was calculated for each protein in the enriched-
AD-set. The results (Table 1) show that our scoring function exhibits very high
specificity: out of 20 top-scoring proteins, all but one (!-catenin, CTNNB1) are
known AD-related proteins according to OMIMM annotation. Further literature
study (see discussion) suggests that even CTNNB1 could be involved in the AD
disease development process [12]. This result opens up exciting new
possibilities to identify novel or previously ignored members of the AD
pathway(s) for subsequent protein drug target investigations or disease
biomarker studies.
Pacific Symposium on Biocomputing 11:367-378(2006)
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index of aggregation
frequency
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0 10 20 30 40 50
Figure 2. Histogram of the index of aggregation distribution for the enrichments of sets of proteins
(size=48) randomly selected from OPHID. The arrow indicates the index of aggregation value for the
enriched-AD-set.
4. Discussion
The integrated approach to the analysis of human interaction data and
Alzheimer disease proteins allowed us to validate existing disease protein
targets and predict novel ones not present in the initial list of disease protein
targets that we started with. The result can be interesting to Alzheimer disease
biologists, and our method can be generalized to other disease biology areas. By
further examining our discoveries in the top-ranked proteins (top 20 are
tabulated in Table 1, other will be made available on our web site
http://bio.informatics.iupui.edu/
soon), we can make the following comments:
First, one of the important Alzheimer disease proteins, tau protein (MAPT,
ranked 31), a well-known participant in AD-linked degeneration pathway(s),
was not initially retrieved by our automated procedure from OMIM data but was
later recovered from the interaction data analysis. Therefore, at least in a few
isolated cases, our method can allow the recovery of false negatives.
Second, the amyloid beta A4 precursor-protein binding protein, APPB1
(ranked 33), represents another interesting case. It is a well-known interaction
partner of APP, but a genetic link to AD was reported in OMIM only for the
other member of the family, APPB2 (ranked 32). Our method still predicts that
APPB1 also plays some role in AD. A recent literature report [13], shows that
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 9
APPB1 indeed directly associates with tau and may provide the crucial missing
link between tau and APP proteins in Alzheimer disease.
Table 1. Top 20 rank-ordered AD relevant proteins.
Score Gene Description
AD
Relevance
43.01 APP amyloid beta (A4) precursor protein (protease nexin-II,
Alzheimer disease)
Known
36.98 PSEN1 presenilin 1 (Alzheimer disease 3) Known
35.64 LRP1 low density lipoprotein-related protein 1 (alpha-2-
macroglobulin receptor)
Known
21.87 PSEN2 presenilin 2 (Alzheimer disease 4) Known
20.89 PIN1 protein (peptidyl-prolyl cis/trans isomerase) NIMA-
interacting 1
Known
19.37 FHL2 four and a half LIM domains 2 Known
15.39 S100B S100 calcium binding protein, beta (neural) Known
12.96 FLNB filamin B, beta (actin binding protein 278) Known
12.37 CTNND2 catenin (cadherin-associated protein), delta 2 (neural
plakophilin-related arm-repeat protein)
Known
12.15 CLU clusterin (complement lysis inhibitor, SP-40,40, sulfated
glycoprotein 2, testosterone-repressed prostate message
2, apolipoprotein J)
Known
11.34 APBA1 amyloid beta (A4) precursor protein-binding, family A,
member 1 (X11)
Known
10.00 NAP1L1 nucleosome assembly protein 1-like 1 Known
9.54 GTPBP4 GTP binding protein 4 Known
9.48 NCOA6 nuclear receptor coactivator 6 Known
9.15 CDK5 cyclin-dependent kinase 5 Known
7.44 CTSB cathepsin B Known
7.29 ASL argininosuccinate lyase Known
4.86 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa Novel
4.86 NCKAP1 NCK-associated protein 1 Known
4.86 AGER advanced glycosylation end product-specific receptor Known
Third, !-Catenin (CTNNB1, ranked 18 as shown in Table 1), was not
previously associated with AD in OMIM or in the general biomedical
community, therefore representing a “clear” case of computational prediction
results. Interestingly, while the exact role of !-catenin in AD is not well
understood, it is known that Wnt signaling pathway (which !-catenin is a part
of) is a target of A! toxicity [14]. Moreover, the Wnt-3a ligands and other
agents that are reported to overcome beta amyloid toxicity stabilize CTNNB1
levels in cytoplasm [15, 16]. It should be stressed that while the OPHID
interactions between !-catenin and AD-set proteins are of high-quality, i.e.
derived from the literature, one could only speculate about the potential role of
!-catenin, since, for instance, both CTNNB1 and its interaction partners’s
Pacific Symposium on Biocomputing 11:367-378(2006)
Page 10
expressions are far from being limited to neurons. It is the pattern of !-catenin
interactions revealed through the analysis of combined evidence that resulted in
high AD-relevance score for !-catenin.
In all, our method incorporated protein interaction data and helped us to
successfully carry out Alzheimer disease-related biological studies. The
computational results, which began with inputs that are not necessarily highly
reliable, showed high biological relevance. Going down the ranked protein
targets, one may generate many new biological hypotheses about the new
functions of proteins in the protein interaction network context beyond the
scope of this work. We are currently developing collaborations with industrial
partners who have accumulated experimental human protein interactions? to
further conduct these computational investigations on high-confidence data sets.
Meanwhile, we are also in the process of developing better scoring functions
and applying these methods to the study of other disease areas.
Acknowledgments
This work was supported in part by systems obtained by Indiana University
through its relationship with Sun Microsystems Inc. as a Sun Center of
Excellence. We would like to thank Stephanie Burks who helps us maintaining
a robust computer systems and Oracle 10g database server at Indiana
University.
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  • Source
    • "sign i has a value of 1 if the evidence i represents activation while has a value of -1 if the evidence i represents inhibition. Then, to rank each interaction, we used HAPPI [16], an integrated protein interaction database that comprehensively integrated weighted human protein-protein interaction data from HPRD, BIND, MINT, STRING, and OPHID by assigning a weight weight(p) for each drug's interacting proteins using the following formula adapted from [17]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. Methods To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. Results In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps webcite Conclusions Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.
    Full-text · Article · Sep 2015 · BMC Bioinformatics
  • Source
    • "While it remains an open question how many measurable human protein interactions there are, the use of PPI data in building disease-relevant molecular interaction network models has already emerged as a major theme for "translational bioinformatics", studies that aim to facilitate the transformation of bioinformatics discoveries from "Omics" experiments into biomedical applications via bidirectional information exchange [12,13]. Recent research studies have shown that, by building comprehensive disease-relevant PPI sub-networks, researchers can generate and validate biological hypothesis that could lead to novel biomarkers or therapeutic developments for many complex diseases such as Huntington's disease, Alzheimer's disease, Breast Cancer, Fanconi Anemia, and Ovarian Cancer1415161718. These studies, however, were primarily based on available human PPIs in existing PPI database repositories with limited coverage and/or uncertain qualities. "
    Full-text · Dataset · Apr 2015
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    • "Our hypothesis of this statistical evaluation is that if the prediction model consists of de-centric targets involved in the same process even if complex and broad, then we should expect that the connectivity among the de-centric targets be lower than the connectivity among a set of randomly selected genes. We defined the index of aggregation of a network [7] as the ratio of the size of the largest sub-network that exists in this network to the size of this network. Note that the size is calculated as the total number of genes within a given network/sub-network. "
    [Show abstract] [Hide abstract] ABSTRACT: e developed a new computational technique to identify de-centric ge-netic regulatory relationship candidates. Our technique takes advantages of functional genomics data for the same species under different perturbation con-ditions, therefore making it complementary to current computational techniques including database search, clustering of gene expression profiles, motif match-ing, structural modeling, and network effect simulation methods. It is fast and addressed the need of biologists to determine activation/inhibition relationship details often missing in synthetic lethality or chip-seq experiments. We used GEO microarray data set GSE25644 with 158 different mutant genes in S. cere-visiae. We screened out 83 targets with 610 activation pairs and 93 targets with 494 inhibition pairs. In the Yeast Fitness database, 33 targets (40%) with 126 activation pairs and 31 targets (33%) with 97 inhibition pairs were identified. To be identified further are 50 targets with 484 activation pairs and 62 targets with 397 inhibition pairs. The aggregation test confirmed that all discovered de-centric regulatory relationships are significant from random discovery at a p-value=0.002; therefore, this method is highly complementary to others that tend to discover hub-related regulatory relationships. We also developed criteria for rejecting genetic regulator candidates x as a candidate regulator and assessing the ranking of the regulator-target relationship identified. The top 10 high suspected regulators determined by our criteria were found to be significant, pending future experimental verifications.
    Full-text · Conference Paper · Jun 2014
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