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Gibbs Free Energy, a thermodynamic measure of protein-protein
interactions, correlates with neurologic disability
Michael Keegan1, Hava T. Siegelmann1,2, Edward A. Rietman1,3, Giannoula
Lakka Klement3
1 BINDS Laboratory, University of Massachusetts, Amherst, MA, USA
2 DARPA, Arlington, VA, USA
3 CSTS Health Care, Toronto, On, CA
Abstract
Background: Modern network science has been used to reveal new and often
fundamental aspects of brain network organization in physiological as well as
pathological conditions. As a consequence, these discoveries, which relate to
network hierarchy, hubs and network interactions, begun to change the
paradigms of neurodegenerative disorders. We therefore explored the use of
thermodynamics for protein-protein network interactions in Alzheimer disease
(AD), Parkinson disease (PD), multiple sclerosis (MS), traumatic brain injury and
epilepsy.
Methods: To assess the validity of using network interactions in neurological
disease, we investigated the relationship between network thermodynamics and
molecular systems biology for these neurological disorders. In order to uncover
whether there was a correlation between network organization and biological
outcomes, we used publicly available RNA transcription data from individual
patients with these neurological conditions, and correlated these molecular
profiles with their respective individual disability scores.
Results: We found a linear correlation (Pearson correlation of -0.828) between
disease disability (a clinically validated measurement of a person's functional
status), and Gibbs free energy (a thermodynamic measure of protein-protein
interactions). In other words, we found an inverse relationship between disease
entropy and thermodynamic energy.
Interpretation: Because a larger degree of disability correlated with a larger
negative drop in Gibbs free energy in a linear, disability-dependent fashion, it
could be presumed that the progression of neuropathology such as is seen in
Alzheimer Disease, could potentially be prevented by therapeutically correcting
the changes Gibbs free energy.
Keywords
Neurodegenerative disease; protein-protein interaction networks; genomic
interpretation; Gibbs Free Energy
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Background
The treatment and management of neurological dysfunction/ neurodegeneration
is an area of a great medical need. The World Health Organization (WHO)
estimates that neurological disorders contribute 10.9% and 8.7% of global
disease burden in high- and medium-income countries,[1] and as the average
age of the populations in developed countries increases in the coming decades,
it is expected that the disease burden will continue to increase. Yet, the present
treatment of neurological diseases constitutes by and large of management of
disease symptoms because the etiology remains unclear. In Alzheimer disease
for example, the earlier etiological hypothesis that amyloid deposits are caused
by environmental stimuli is being supplanted by growing body of evidence that it
is the genomic dysregulations of cellular and molecular pathways that cause
accumulation of amyloid and tau proteins.[2] This may not only explain why
targeting amyloid and/or tau proteins has been unsuccessful so far, it also
unlocks the possibility of using targeted therapies. However, to realize the full
potential of targeting these newly identified genomic patterns in Alzheimer
Disease[3, 4] and in order to pursue these novel, molecularly-guided therapies,
an improved understanding about the complexity of systems biology of
neurological diseases is needed.
Many pathologic conditions such as for example neurodegenerative disease,
chronic inflammatory disorders, or cancer; are associated not only with
mutational activation of genes, but also with re-activation of developmentally
silenced pathways. Because the processes of tissue invasion, proliferation,
inflammation and angiogenesis are common not only to oncogenic induction, but
also to embryonal development and normal host response to tissue injury - a
simple DNA mutation analysis would not provide sufficient information. The
activation of pathways associated with inflammation and tissue injury is therefore
best studied using mRNA expression. However, until very recently, it has been
very difficult to directly correlate the levels of gene expression with the cellular
effect of specific proteins. In fact high gene expression did not always imply
increased protein function or activation of a biological process. The intensity of a
biological effect is dependent on the interaction of the affected (overexpressed)
gene with its neighbors, and the quorum effect of the innumerable feedback
loops on the global protein-protein interaction network.
The realization that the perturbations of individual genes can be measured by its
effect on the global response of a network has led many scientists to finding
alternative approaches for genomic interpretations. The one presented in this
manuscript is based on using not only level of expression of a gene, but also its
topology as a measure of its connectivity. This novel approach has been enabled
by the vast amounts of well-curated information accumulated in publicly available
protein-protein interaction networks (PPINs) over the last 4 decades, and has
emerged quite recently.[5-8]
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The underlying premise of our analysis is that biological systems are complex
chemical networks. A cell, for example, consists of a large molecular network
made of DNA, RNA, proteins, peptides, small molecules, and lipids. Each of
these molecules is associated with potential energy contributing to an even larger
energetic network. The energetic state exists in equilibrium, and any perturbance
sets off a cascade of events striving to bring the overall network back to the
same entropy. The prime force pushing the reaction back is Gibbs free energy,
an expression of the thermodynamic energy reflecting the chemical potential
between interacting proteins. This Gibbs Free Energy is used as a measure of
the changes occurring within a disease-related protein-protein interaction
network.
In his earlier work, Rietman et al[9] described an inverse correlation between
Gibbs free energy and percent 5-yr survival for ten different types of cancers .
The study showed that poor prognosis cancers such as glioblastoma multiforme
have low thermodynamic entropy, less negative Gibbs free energy, and very low
5-year survival. This was consistent with the clinical status of the disease, which
has average survival post diagnosis of 6 months, and a 5-yr survival of 2%.
Similarly, the finding that in breast carcinoma, which had higher thermodynamic
entropy, a more negative Gibbs free energy, and a much higher 5-year survival
was also congruent with the clinical observation, as the average 5-year survival
for breast cancers of all stages is ~ 88%.
In this manuscript we describe a linear relationship between Gibbs free energy (a
measure of thermodynamic energy for a specific disease), and disability weight
(a clinically validated measurement of a person's functional status) for several
neurological diseases. The choice of thermodynamics for the analysis was not
fortuitous. Thermodynamic energy represents an important driving factor of
chemical and biological interactions for all living organisms, and its correlation
with biological events is not unexpected. Gibbs Free Energy, which incorporates
information of both mRNA expression as well as protein-protein interaction,
would be expected to have the ability to discriminate between “passenger
genomic events” and “driving genomic events”. This remains the main quandary -
finding ways to differentiate between causative vs ancillary molecular changes.
The use of Gibbs Free Energy represents a novel approach, and may be of
particular usefulness in neurological disorders/ neurodegerative diseases where
complex and by and large unclear etiology, pathogenesis and clinical response
prevent identification of a good therapeutic strategies.
Gibbs Free Energy (G) is the energy associated with a chemical reaction that can
be used to do work. The free energy of a system is the sum of its enthalpy (H)
plus the product of the temperature (Kelvin) and the entropy (S) of the system.
We propose that the use of this well-established thermodynamic measure is
useful for analyzing the interplay between patient’s genomic information and the
existing knowledge about protein-protein interactions.[7] Its use is based on a
number of important observations. First, proteins interacting with a large number
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of other proteins (even if not simultaneously) have higher entropy. Because each
protein-protein interaction has a different molecular configuration, given by
Boltzmann’s classic equation (S = kln(W)), the entropy, S, increases as the
natural log of the number of configurations, W. As such, proteins with many
interaction partners exhibit many possible configurations, and each protein-
partner interaction leads to a different configuration. Highly interconnected
proteins such as, for example, ubiquitin (UBC) or TP53 can undergo a
simultaneous physical interaction with hundreds of their respective interaction
partners, because at any given time one UBC molecule interacts with a protein
and another UBC molecule interacts with another protein inside the same cell. An
RNA transcriptome from a tissue biopsy thus represents an ideal mixture of UBC
and its interacting partners for a given condition.
Second, transcription (RNA expression levels) data are good surrogates for
protein concentration. Unlike the DNA level gene alterations, which are
transcribed with variable frequencies to RNA, the number of mRNA copies is
translated into individual proteins with great fidelity. Several research groups
have confirmed this fidelity: Greenbaum et al[10] and Maier et al[11] report a
Pearson correlation in the range of 0.4-0.9 for a large set of experiments across
five different species. Similarly, Kim et al[12] and Wilhlem et al[13] found an 83%
correlation between human transcription data and mass spectrometry proteomic
data for multiple tissue types, supporting the use of human transcriptome as a
surrogate for protein concentration.
Third, the use of real-world dataset contains an inherent level of noise, but as
new mRNA data sets emerge from ongoing clinical trials, the accuracy of the
information in protein-protein interaction databases as well as in the gene
expression data sets will improve as well. For the time being, the preliminary
findings are a great way to discover new avenues for future analysis, but as data
integrity and quality of our conclusions improve, we should be able to use this
data for reliable therapeutic decisions. In addition, the ability to combine different
data sources (mRNA expression data from individual patients and existing
PPINs) as introduced in this manuscript, is likely to deliver new insight into
biologically complex diseases than traditional approaches.
Materials and Methods
Because the study of chemical thermodynamics embodies chemical potential, for
two molecules A and B interacting to form a new molecule, or an A-B molecular
complex, the amount of A-B formed would be dictated by the amount of A and B.
In cases where A is present in higher concentration than B, a chemical potential
develops. As such, a protein, D, interacting with proteins, C, E, and F has
chemical potential represented by:
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where the chemical potential is the natural log of the concentration of protein D
divided by the sum of the concentrations of protein D and all the concentrations
of its neighbors. Because the argument of the natural log is a ratio, we can use
scaled “concentrations,” or in this case, scaled expression values. The log-2
normalized expression data typically fall in the range [-10, 10]. Rescaling sets the
range to be [0,1], and we can compute the minimum emin, and the maximum,
emax, for a given expression data set. The normalized expression value for each
gene are then computed as follows:
The rescaling is justified from a mathematical perspective by the fact that the
argument to the natural logarithm must be positive. Furthermore, if a gene
mutation leads to loss of its RNA transcription (RNA transcription is said to be
down-regulated), the concentration for the respective protein would essentially be
zero. Likewise, when a gene alteration leads to constitutive activation of its
transcription, multiple copies of its mRNA will be made (the RNA transcription is
said to be highly up-regulated), and very large quantities of protein will be
produced. In this case, the protein concentration would be effectively set to the
maximum of 1.
Thus, the computation of Gibbs free energy for a single protein in the PPI would
be,
which tells us to first compute the chemical potential, for protein i with neighbors
j, and multiply it by the [0,1] scaled concentration to get the Gibbs free energy for
protein i. For the overall Gibbs Free Energy for the network, the individual Gis for
each of the protein within the network are summed up. In the final calculation the
normalized expression data are overlaid on the BioGRID PPI and Gi followed by
use of the above equation.
We used three main data sources for our analysis: World Health Organization
data on disability associated with neurological diseases, protein-protein
interaction data from Biological General Repository for Interaction Datasets
(BioGRID, Human ver. 3.4.139, September 2016, https://thebiogrid.org/), and
RNA transcription data sets from Gene Expression Omnibus (GEO).
WHO Tools and Statistics for neurodegenerative diseases:
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Unlike the terminal conditions such as cancer, there is no direct correlation
between death rate and disability in neurodegenerative diseases, and death rate
is not a meaningful measure of morbidity. For example, while epilepsy may
cause more deaths, multiple sclerosis (MS) far outweighs its impact in the sense
of morbidity and disability during a person's lifetime. For this reason, the World
Health Organization has elected Disability-Adjusted Life Years (DALYs) for
evaluation of disability associated with neurodegenerative disease. DALYs
combine two components: years of life lost due to premature mortality, and years
lived with disability. DALYs are an expression of the number of healthy years a
person looses in life. It is a more accurate representation of the damage a
disease exerts on a healthy human population, and its measure - Disability
Weight - is a number between 0 (perfect health) and 1 (death). While DALYs are
population-dependent, and the same disease may lead to a larger apparent loss
in a region where the disease is widespread, Disability Weight avoids the
potential for biasing the numbers towards more prevalent diseases, and away
from uncommon diseases, because it rates severity of a disease in an individual.
The most recent public list of WHO for neurological diseases and their
corresponding disability weights was published in 2006, and the “Neurological
Disorders, public health challenges”[1] describes the demographics, geographic
distributions, graphs and projections for many neurological diseases. It
standardizes the comparison of neurological diseases, and was therefore used
for our analysis.
Sources of transcription data:
The source of the transcription data was Gene Expression Omnibus (GEO)
repository of -omics and high-throughput data https://www.ncbi.nlm.nih.gov/geo.
The data sets for each of the selected neurological diseases were collapsed from
probe IDs to gene IDs using GenePattern software (Broad Institute, Cambridge,
MA; https://software.broadinstitute.org/cancer/software/genepattern), and the
corresponding chip platform documented for the respective data set. The
following datasets for specific diseases were examined: Alzheimer’s disease
(GDS4136), Parkinson’s disease (GSE6613), Multiple Sclerosis (GSE19285),
Epilepsy (GSE32534), Cerebrovascular disease (GSE36791), and Meningitis
(GSE40586). Most data sets were already log-2 normalized, and transformed
those that were not. Table 1 lists the GEO dataset number and pubmed ID
(PMID) for each of the diseases.
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Table 1: Disability score and Gibbs free energy for the neurological
disorders studied.
Disease
Tissue
Analyzed
GEO
number
PMID
Disability
Weight
Gibbs Free
Energy
Number of
Patients
Alzheimer
Disease
hippocampus
GSE28146
21756998
0.666
-14735
22
Cerebrovascular
Disease
blood in ruptured
intracranial
aneurysms
GSE36791
23512133
0.266
-11761
43
Epilepsy
neocortex tissue
GSE32534
23418513
0.113
-11467
5
Meningitis
peripheral blood
cells
GSE40586
23515576
0.615
-16918
21
Multiple
Sclerosis
peripheral blood
cells
GSE19285
22727118
0.411
-12109
30
Parkinson
Disease
whole blood cells
GSE6613
17215369
0.351
-10043
50
Results and Discussion
Neurological disorders are common and represent a major public health problem.
Even though neurological impairment and its sequelae constitute over 6% of the
global burden of disease,[1] the management of neurological disorders has not
significantly changed in the past few decades, and the mainstay of therapy
remains focused on symptomatic management. As such the already very high
disease burden is likely to continue to increase as the life spans across the
world’s population increase. According to the recently published Global Burden of
Disease 2010 Study (GBD 2010),[14] stroke is the second leading cause of
death globally and the third leading cause of premature death and disability as
measured in disability-adjusted life-years (DALYs).
There are many reasons for the lack of effective therapies, but the principal
challenge is the complexity of data and the inconsistency in assigning causality
to genomic alterations. There exists a great struggle with discriminating between
incidental molecular findings, and those that may be driving disease
pathogenesis. This not only hinders the search for effective therapies, but the
absence of tissue targets also prevents effective clinical initiatives. Until recently
much effort was dedicated to statistical interpretation of RNA expression levels.
But the level of mRNA expression is not always reflective of the gene importance
in a biological event on in a particular disease. An overexpressed gene that is
peripheral to a major proliferative pathway will have minimal effect on
proliferation, whereas a mild elevation in the expression levels of a well-
connected gene will have a crucial effect on the process. Minute changes in the
levels of genes coding for very important growth factors such as VEGF, or
inflammation regulators interleukins lead to biological events that are normally
carefully managed through feedback loops.
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Any measurement of disease activity must therefore incorporate the intracellular
protein-protein interactions. We introduce a method for interrogating individual
tissue expression of mRNA against existing, well-curated protein-protein
interaction networks. We focus on proving that thermodynamics, i.e. the
molecular changes defined by Gibbs free energy, can be correlated with disease
state and progression. Figure 1 shows the correlation of Disability Weight with
average Gibbs Free Energy for six neurological conditions. The relationship
between disease-related disability and Gibbs Free Energy is linear, with
progressively worsening disability correlating with increasing more negative
Gibbs Free Energy. The respective values used for the figure are shown in Table
1. We confirm that low entropy (less negative Gibbs free energy) for epilepsy
correlated with the lowest disability, whereas the higher entropy (more negative
Gibbs free energy) in Alzheimer’s disease correlated with the highest disability.
These findings correlate with clinical observations that the severity of
neurological dysfunction in multiple sclerosis, bacterial meningitis and Alzheimer
disease is certainly higher than in epilepsy.
We further explored whether Gibbs free energy correlated not only with severity
of the disease, but also with disease progression. Seven stages have been
described in the Alzheimer Disease (AD) progression, from no impairment (Stage
1) to loss of ability to respond to their environment or communicate, needing
assistance with all activities of daily living, and loss of ability to swallow (Stage 7).
The GEO data sets tend to simplify these stages into only 4 stages (Figure 2a),
and we show a clear linear relationship (R = 0.7978) between thermodynamic
energy (Gibbs free energy) and the 4 stages of the disease. The positivity of the
slope is most likely reflective of neuronal loss, with less and less metabolically
active tissue. This is in direct contrast with epilepsy, typified by an abnormality of
conduction of an action potential across neuronal tissues rather than by a
neuronal loss, where Gibbs free energy is less negative (i.e. more positive).
Similarly strong correlation (R=0.932) was observed between Gibbs free energy
and the degree of amyloid deposition in Alzheimer disease (Figure 2b). In this
case, progressive histological changes in the form of extracellular deposits of
amyloid β peptides, senile plaques, and intracellular neurofibrillary tangles of
hyperphosphorylated tau in the brain, relate to neuronal death and correlate with
severe disability. The amyloid metabolic cascade and the posttranslational
modification of tau protein, often considered causal in AD, are sufficient to
explain the diversity of biochemical and pathological abnormalities in AD. There
is a multitude of cellular and biochemical changes leading to the accumulation of
extracellular senile plaques made of deposits of Aβ peptide, and all have the
outcome of neural degeneration and loss. The correlation of Gibbs free energy
with histological changes and disease progression implies a global value of our
measurement and the need for future evaluation of the hidden metabolic
information.
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Yet another scale is used by clinicians for evaluation of disability in multiple
sclerosis (MS). The Expanded Disability Status Scale (EDSS), was developed by
a neurologist (John Kurtzke) in 1983, and ranges between 0-10. It is based on
neurological evaluation of pyramidal (limb movement), cerebellar (ataxia,
coordination, tremor), brainstem (speech, swallowing and nystagmus), sensory,
bowel and bladder, vision, or cerebral (mental) functions. Its 0.5 unit increments
represent progressively higher levels of disability. EDSS 1.0-4.5 refers to people
with MS who are able to walk without any aid, whereas 5-6 refers to progressive
motor and cognitive disability. Similarly to AD, we find a positive linear slope
between Gibbs Free Energy and disability in MS (Figure 3), as consistent with
neuronal loss due to demyelination. The respective correlation coefficient
between EDSS and Gibbs free energy in MS was found to be very strong at
R=0.913.
Conclusions
We provide early evidence for using not only expression data, but also
connectivity/topology data for analysis of genomic information in
neurodegenerative disease. One of the approaches is using a thermodynamic
measure such as Gibbs Free Energy to identify the disease-related global
changes in protein-protein interactions network resulting from changes in
genomic profiles of individual patients. The ability to correlate these molecular
profiles disease severity and disease progression suggests that we can use
Gibbs Free Energy in the future to evaluate causative vs ancillary molecular
changes through mathematical simulation of the protein inhibition/stimulation.
This is the first installment in developing mathematical algorithms that would
facilitate identification of relevant, therapeutically targetable pathways in
neurodegenerative diseases. The use of Gibbs Free Energy in genomic analysis
would be beneficial not only from therapeutic point of view, but also from cost
and sustainability perspective, because it would minimize futile clinical trials.
Acknowledgments
We acknowledge (NSF)/ECCS-1533693 NSC-FO: Col “Individual Variability in
Human Brain Connectivity, Modeling Using Multi-scale Dynamics Under Energy
Constraints”, and Office of Naval Research (ONR)/N00014-15-2126. The content
is solely the responsibility of the authors and does not necessarily represent the
official views of the National Science Foundation, or the US Navy. We thank
Samuel McGuire for helpful discussions. EAR and GLK acknowledges partial
funding support from CSTS Healthcare, Toronto, Canada.
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Figures and Legends
Figure 1. The correlation of Disability Weight and Gibbs Free Energy for six
distinct neurological conditions. The mRNA expression values available from
publicly available GEO data sets (Alzheimer’s disease GDS4136, Parkinson’s
disease GSE6613, Multiple Sclerosis GSE19285, Epilepsy GSE32534,
Cerebrovascular disease GSE36791, and Meningitis GSE40586) were used to
calculate Gibbs free energy, a thermodynamic measure of protein-protein
interactions. As would be expected based on the level of clinical disability,
epilepsy has low Gibbs free energy (low entropy) and correlates with the lowest
neurological disability. This is in stark contrast with the high Gibbs Free Energy
(high entropy) and high neurologic disability in Alzheimer disease as would be
consistent with clinical observations in this disease. The respective values, and
the size of cohort are summarized in Table 1, and the error bars have been set to
5% of the average Gibbs Free energy value, given that the actual errors of the
reported mRNA gene expression values were not reported.
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Figure 2. The correlation between disease progression and pathological
findings with Gibbs free energy in Alzheimer Disease. The analysis of 2000+
Alzheimer Disease patients from GSE84422 data set revealed a correlation
coefficient R = 0.7978 between Gibbs Free Energy and disease progression
using disease stage (panel a), and an even stronger correlation (R= 0.932) using
tissue pathological analysis such as amyloid plaque density (panel b).
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Figure 3. The correlation between disease progression and Gibbs free
energy in Multiple Sclerosis. Thirty patients with multiple sclerosis from the
GEO data set GSE19285 revealed a correlation coefficient R=0.913 between
Gibbs Free Energy and disease progression as reflected by The Expanded
Disability Status Scale (EDSS).
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References:
1. WHO, editor Neurological Disorders, public health challenges2006.
2. Canter RG, Penney J, Tsai LH. The road to restoring neural circuits for the
treatment of Alzheimer's disease. Nature. 2016;539(7628):187-96. doi:
10.1038/nature20412. PubMed PMID: 27830780.
3. A M, A RM. Deep Brain Stimulation and Gene Expression Alterations in
Parkinson's Disease. Journal of biomedical physics & engineering. 2016;6(2):47-
50. PubMed PMID: 27672624; PubMed Central PMCID: PMC5022754.
4. Lewis PA, Cookson MR. Gene expression in the Parkinson's disease
brain. Brain research bulletin. 2012;88(4):302-12. doi:
10.1016/j.brainresbull.2011.11.016. PubMed PMID: 22173063; PubMed Central
PMCID: PMC3387376.
5. Benzekry S, Tuszynski JA, Rietman EA, Lakka Klement G. Design
principles for cancer therapy guided by changes in complexity of protein-protein
interaction networks. Biol Direct. 2015;10:32. doi: 10.1186/s13062-015-0058-5.
PubMed PMID: 26018239; PubMed Central PMCID: PMC4445818.
6. Klement GL, Arkun K, Valik D, Roffidal T, Hashemi A, Klement C, et al.
Future paradigms for precision oncology. Oncotarget. 2016. doi:
10.18632/oncotarget.9488. PubMed PMID: 27223079.
7. Rietman EA, Scott JG, Tuszynski JA, Klement GL. Personalized
anticancer therapy selection using molecular landscape topology and
thermodynamics. Oncotarget. 2017;8(12):18735-45. doi:
10.18632/oncotarget.12932. PubMed PMID: 27793055; PubMed Central PMCID:
PMC5386643.
8. Rietman EAB, A.; Platig, J.; Tuszynski, J. A.; Lakka Klement, G.;. Gibbs
Free Energy of Protein-Protein Interactions Reflects Tumor Stage. . BioRxiv.
2015. doi: doi.org/10.1101/022491.
9. Rietman EA, Platig J, Tuszynski JA, Lakka Klement G. Thermodynamic
measures of cancer: Gibbs free energy and entropy of protein-protein
interactions. J Biol Phys. 2016;42(3):339-50. doi: 10.1007/s10867-016-9410-y.
PubMed PMID: 27012959.
10. Greenbaum D, Colangelo C, Williams K, Gerstein M. Comparing protein
abundance and mRNA expression levels on a genomic scale. Genome biology.
2003;4(9):117. doi: 10.1186/gb-2003-4-9-117. PubMed PMID: 12952525;
PubMed Central PMCID: PMC193646.
11. Maier T, Guell M, Serrano L. Correlation of mRNA and protein in complex
biological samples. FEBS letters. 2009;583(24):3966-73. doi:
10.1016/j.febslet.2009.10.036. PubMed PMID: 19850042.
12. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al.
A draft map of the human proteome. Nature. 2014;509(7502):575-81. doi:
10.1038/nature13302. PubMed PMID: 24870542; PubMed Central PMCID:
PMC4403737.
13. Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M,
Savitski MM, et al. Mass-spectrometry-based draft of the human proteome.
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/527820doi: bioRxiv preprint first posted online Jan. 22, 2019;
Nature. 2014;509(7502):582-7. doi: 10.1038/nature13319. PubMed PMID:
24870543.
14. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al.
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions,
1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.
Lancet. 2012;380(9859):2197-223. doi: 10.1016/S0140-6736(12)61689-4.
PubMed PMID: 23245608.
.CC-BY-NC-ND 4.0 International licenseIt is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint. http://dx.doi.org/10.1101/527820doi: bioRxiv preprint first posted online Jan. 22, 2019;
