Insights into the Evolutionary Features of Human
Arup Panda, Tina Begum, Tapash Chandra Ghosh*
Bioinformatics Centre, Bose Institute, Kolkata, India
Comparative analyses between human disease and non-disease genes are of great interest in understanding human disease
gene evolution. However, the progression of neurodegenerative diseases (NDD) involving amyloid formation in specific
brain regions is still unknown. Therefore, in this study, we mainly focused our analysis on the evolutionary features of
human NDD genes with respect to non-disease genes. Here, we observed that human NDD genes are evolutionarily
conserved relative to non-disease genes. To elucidate the conserved nature of NDD genes, we incorporated the
evolutionary attributes like gene expression level, number of regulatory miRNAs, protein connectivity, intrinsic disorder
content and relative aggregation propensity in our analysis. Our studies demonstrate that NDD genes have higher gene
expression levels in favor of their lower evolutionary rates. Additionally, we observed that NDD genes have higher number
of different regulatory miRNAs target sites and also have higher interaction partners than the non-disease genes. Moreover,
miRNA targeted genes are known to have higher disorder content. In contrast, our analysis exclusively established that NDD
genes have lower disorder content. In favor of our analysis, we found that NDD gene encoded proteins are enriched with
multi interface hubs (party hubs) with lower disorder contents. Since, proteins with higher disorder content need to adapt
special structure to reduce their aggregation propensity, NDD proteins found to have elevated relative aggregation
propensity (RAP) in support of their lower disorder content. Finally, our categorical regression analysis confirmed the
underlined relative dominance of protein connectivity, 39UTR length, RAP, nature of hubs (singlish/multi interface) and
disorder content for such evolutionary rates variation between human NDD genes and non-disease genes.
Citation: Panda A, Begum T, Ghosh TC (2012) Insights into the Evolutionary Features of Human Neurodegenerative Diseases. PLoS ONE 7(10): e48336.
Editor: Christian Scho ¨nbach, Kyushu Institute of Technology, Japan
Received June 4, 2012; Accepted September 24, 2012; Published October 30, 2012
Copyright: ? 2012 Panda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Department of Science & Technology. Government of India (www.dst.gov.in). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
The pathogenesis of neuronal vulnerability in neurodegener-
ative diseases (NDD), involving amyloid formation in specific
brain regions, is still not clear. Therefore, tracing evolutionary
etiology of human misfolding and aggregation related disease
genes canshed lightinto
neurodegenerative disease pathogenesis by identifying the factors
that harbor disease causing mutations in normal genes. Thus,
we used Homo sapiens as model organism to assess the molecular
divergence of neurodegenerative diseases by computing the ratio
of number of non-synonymous substitution per site (dN) to the
number of synonymous substitution per site (dS) against non-
disease genes as the control parameter .
Decades-long continuous efforts have facilitated to characterize
protein evolutionary rates with the signatures of gene expression
level [2,3], protein length [4,5], aggregation propensity [6,7],
number of interacting partners , miRNA targets , gene
dispensability [10,11] and protein disorder content [12,13]. Due to
lack of proper 3D structure, protein intrinsically disordered region
provide global flexibility that promotes binding to their partners in
protein-protein interactome [14,15]. Moreover, highly connected
miRNA targeted genes are highly disordered in nature . On
the other hand, exposed hydrogen bonds in highly disordered
proteins are known to elevate the risk of protein aggregation,
which may impose selective constraints on protein structures
[13,16]. Hence, to resolve the ambiguity of relations affecting
protein evolutionary rates, we specifically analyzed human
aggregation prone neurodegenerative disease genes compare to
non-disease genes. We considered protein intrinsic disorder
content, miRNA targeting and protein connectivity as the
functions of evolutionary rates.
Finally, our comprehensive analysis revealed the conserve
nature of human NDD genes relative to non-disease genes. We
exploited several evolutionary parameters to explain the slower
evolutionary rates of NDD genes with respect to non-disease
genes. Moreover, we here obtained lower disorder content in
NDD genes, conflicting the previously established analyses of
Roychoudhury et al.  and Uversky . Relative impor-
tance of the determinants in modulating evolutionary rates of
proteins was further confirmed from categorical regression
analysis which emphasized that protein connectivity, 39UTR
length, relative aggregation propensity (RAP), nature of hubs
(singlish/multi-interface) and disorder content are largely re-
sponsible for such evolutionary behaviour of human NDD
genes. Furthermore, we also confirmed that the nature of hub is
also an important evolutionary rate regulator.
PLOS ONE | www.plosone.org1October 2012 | Volume 7 | Issue 10 | e48336
Materials and Methods
Dataset Preparation for Evolutionary Rate Estimation
We listed 460 non-redundant NDD genes from Biobase
Knowledge Library (BKL) (http://www.biobase-international.
com/) out of 848 readily available NDD annotations by matching
their functional description with any one of the neurodegenerative
diseases common to literatures (such as Alzheimer disease,
Parkinson disease, Huntington disease, Adrenoleukodystrophy,
Creutzfeldt-Jakob disease, Friedreich ataxia, Leigh syndrome,
Neuronal Ceroid lipofuscinosis, Myoclonic epilepsy, Pick disease,
Spinocerebellar ataxia, Supranuclear palsy, Charcot-Marie-tooth
disease, Wolfram syndrome, Alexander disease, Amyotrophic
lateral sclerosis, Canavan disease, Familial dysautonomia, Leu-
koencephalopathy, Metachromatic leukodystrophy, Multiple scle-
rosis, Myotonic dystrophy, Prion diseases, Rett syndrome,
Schizophrenia, Spastic paraplegia, Spinal muscular atrophy,
Multiple system atrophy and Tay-sachs disease) [19–34]. Howev-
er, some of the afore-mentioned diseases may belong to
neuropathy or lysosomal storage disease groups [35,36] and were
excluded from our gene set. To extract actual disease genes, we
also removed potential risk associated disease susceptible genes as
per Online Mendelian Inheritance in Man (OMIM) , Human
Gene Mutation Database (HGMD)  and Genetic Association
Database (GAD)  from our dataset. In our comparative study,
genes not showing any disease annotation in BKL or OMIM or
HGMD or GAD and did not follow ubiquitous expression pattern
, were regarded as non-disease genes. Following 1:1 orthology
relationship , we extracted the corresponding mouse ortholo-
gues of the human genes from Ensembl v.60 using biomart 
and also obtained their pairwise non-synonymous (dN) and
synonymous (dS) substitution rates to compute gene specific
evolutionary rate (dN/dS). Genes having dS .3 were discarded
from our analysis to get rid of problems due to mutational
saturation . Human protein coding sequences were also
acquired from Ensembl database. For genes with more than one
isoform, the longest isoform was considered. Finally, we yielded
a list of 375 NDD and 7578 non-disease genes with available
evolutionary rate for further analysis (Table S1).
Determining Gene Expression Level and Expression
Following the method of Wu et al. , we estimated gene
expression level using HG-U133A affymetrix probe set in addition
to the GNF1B, GCRMA dataset obtained from Gene Expression
Atlas (http://biogps.gnf.org/downloads/). An average intensity
value in 84 tissues was considered as the expression level for each
gene. In case of genes with different probe sets, we averaged the
mean expression values of all the probe sets of a gene to yield final
gene expression level . Gene expression width is determined as
per Park et al. , where, we took a cutoff signal intensity value
as 200 to consider a gene is expressed in that particular tissue. We,
thereby, obtained expression data for 356 NDD genes and 3930
Protein-Protein Interaction Data
Human protein-protein interaction data was collected from
biological interaction repository BioGRID database v.3.1.77 
which houses over 10271 unique human proteins annotated with
39931 non-redundant interactions. BioGRID acts as an extensive
interaction pool compare to other human interaction databases
like HPRD, MIPS, FlyBase etc [47–49]. Therefore, for systematic
analysis of interaction network, we chose BioGRID database to
compute protein connectivity by counting the number of in-
teraction partners (excluding self interaction) that a protein
Identification of Nature of Hub Proteins (Singlish/Multi
Hub proteins can be characterized by the proteins with $5
interactors . As per Kim et al. , we have assigned the hub
proteins as singlish/multi interface hubs by identifying their
interacting domains using Pfam database . To assign a domain,
the following criteria were used: (a) e-value of alignment should be
,1024, (b) protein sequence should overlap .80% of the domain
length and (c) length of the domain should be greater than 12
residues . Following Kim et al. [50,51] we annotated hub
proteins with one or two interacting interface as singlish interface
hub and those having more than two interacting interfaces as multi
microRNA Targeting and 39 UTR Length Calculation
Human miRNA target predictions were obtained from micro-
RNA.org database (August’2010 releases) . We only consid-
ered miRNAs, whose target sites remain conserved across the
mammalian phylogeny, to acquire a reliable outcome . Using
the prediction, we next computed the number of regulatory
miRNAs per gene in our dataset. Ensembl v.60  was used to
calculate the length of 39UTR region for each gene.
Estimation of Protein Disorder Content
In our dataset, we predicted intrinsic disorder of a protein using
weizmann.ac.il/fldbin/findex)  using its default parameters.
To reduce false positive rate only the sequences with 30 or more
disordered residues at a stretch were considered [12,55]. The
fraction of disorder content was estimated by dividing the number
of disordered residues of a protein to the length of that protein.
Computing Protein Relative Aggregation Propensity
Aggregation propensity of both NDD and non-disease proteins
was retrieved using TANGO algorithm . Based on the
physicochemical properties, TANGO predicts the b-aggregation
score of a protein. To calculate RAP of a protein, we took the ratio
of its TANGO aggregation score to the maximal TANGO
aggregation score of the whole dataset [7,57].
The entire statistical analyses were performed using SPSS v.13.
Mann-Whitney U test was used to compare the average values of
different variables between two classes of genes. For correlation
analysis, we performed the Spearman’s Rank correlation co-
efficient r, where the significant correlations were denoted by
Gene Expression Level Constraining the Evolutionary
Rates of NDD Genes
Neurodegenerative diseases are known to be arisen through
complex interaction between genetics of a given individual and
multiple environmental factors . Therefore, studying evolu-
tionary aspect of progressive degenerative diseases of the central
nervous systems has enormous impact on evolutionary genetics,
which led us to estimate the evolutionary rates (dN/dS) of 375
neurodegenerative disease and 7578 non-disease genes in our
Evolutionary Factors & Neurodegenerative Diseases
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comparative analysis. We observed that NDD genes are under
purifying selection pressure as compare to non-disease genes (dN/
dS of NDD genes=0.126, non-disease genes=0.158 and
P=1.9061026for NDD vs. non-disease genes). Therefore, to
illuminate the conserved nature of NDD genes, we computed gene
expression levels of both NDD and non-disease genes as
expression levels are known to be the major evolutionary rates
indicator . Reasonably, we noticed that mean expression levels
of NDD genes (94.338) are ,2.54 fold higher than non-disease
genes (Expression level=37.057, P=3.3161027for NDD vs. non-
Moreover, we obtained a strong negative correlation between
gene expression levels and protein evolutionary rates (Spearman’s
r=20.108, P=1.0061026). Hence, it can be concluded that
gene expression levels to be one of the potential evolutionary
features responsible for such rate variations.
Examining Protein Connectivity and miRNA Targeting as
Influential Factors of Protein Evolutionary Rates
Proteins with higher interacting partners evolve slower as
mutations in protein interaction sites may disrupt the network
connectivity affecting the functionality of the proteins [8,60–63].
Hence, considerable lower evolutionary rates of NDD genes in
contrast to non-disease genes directed us to scrutinize whether
protein connectivity has any influence on their evolutionary rates
differences. We found that highly expressed NDD genes encoding
proteins have ,2 fold higher network connectivity in comparison
with non-disease genes encoding proteins (average connectivity of
P=1.95610214for NDD vs. non-disease proteins). Additionally,
in agreement to Fraser et al. [8,61] a significant negative
correlation is detected between protein connectivity and evolu-
tionary rates (Spearman’s r=20.162, P=1.0061026). Thus, we
infer that protein connectivity may have an impact on evolution-
ary rate differences between NDD and non-disease genes.
It is now obvious that highly connected proteins are targeted by
greater number of miRNAs because genes targeted by various
types of miRNAs are subject to enormous functional constraints
and thus, evolve slowly . Therefore, retrieving miRNA targets
against each gene revealed that NDD genes are highly targeted by
various types of miRNAs compare to the non-disease genes (mean
miRNA targets of NDD gene=44.88, non-disease gene=39.44,
P=1.1161023for NDD vs. non-disease genes). Moreover,
miRNAs can recognize target sites at the 39UTR regions of the
genes and hence genes with longer 39UTR evolve at slower rates
compare to genes with shorter 39UTR . Estimation of the
39UTR length of NDD and non-disease genes (Mean 39UTR
length of NDD gene=1749 bp, non-disease gene=1536 bp,
P=2.7261022for NDD vs. non-disease genes) also supports the
earlier results . Correlation analysis revealed that evolutionary
rate is negatively correlated with the number of distinct miRNA
types (Spearman’s r=20.087, P=1.0061026) and also with
39UTR length (Spearman’s r=20.192, P=1.0061026). Thus,
our results emphasize that number of miRNA types and 39UTR
length altogether modulate the rate difference between NDD and
Protein Intrinsic Disorder Content and Nature of Hub
Proteins as the Functions of Protein Evolutionary Rates
Genes encoding proteins with higher intrinsically disorder
regions (IDRs) are targeted by higher number of miRNAs rather
than genes encoding proteins with lower IDRs . Therefore, it
is expected that highly connected NDD genes should have greater
disorder content than non-disease genes, as observed earlier
[17,18]. Interestingly, our observation contrasts our expectation
i.e. NDD genes have significantly lower disorder content (21.98%)
than non-disease genes (25.98%) (P=5.2361023for NDD vs.
non-disease proteins). In favor of our observation, we also found
a significant positive association (r=0.080, P=161026) between
IDR content and dN/dS. In addition, it is well known that highly
disordered proteins serve as flexible linkers in the protein-protein
interaction networks to promote promiscuous binding to their
interacting partners [64,65]. Since, we observed a greater
connectivity of NDD genes compare to non-disease genes; it is
expected that NDD genes should have higher disorder content
than the non-disease genes as observed previously . Moreover,
highly connected ‘‘hub’’ proteins (with $5 interactors) in the
protein-protein interaction network play a vital role in controlling
biological processes of cell . Surprisingly, we observed that
NDD genes have greater proportion of hub proteins than non-
disease genes (Table 1). Previously, it has been reported that multi
interface hubs (party hubs) interact simultaneously with their
partners and exhibit relatively conserved evolutionary rates with
lower disorder content than singlish interface hubs (date hubs) that
facilitate transient binding with their different partners at different
times/locations [12,50,51]. Moreover, due to lack of compact 3-D
structures in native state, intrinsically disorder proteins are under
less structural constraint and have elevated evolutionary rates .
Accordingly, we found that NDD genes are enriched with multi
interface hubs (party hubs) (Figure 1) in favor of their lower
disorder content and also supports for their lower evolutionary rate
compare to non-disease genes.
Relative Aggregation Propensity Negatively Steers
Protein Evolutionary Rates
Earlier it has been reported that the frequency of aggregation
nucleating segments is significantly lower in intrinsically disor-
dered proteins compare to properly folded proteins. These results
have been explained due to lack of structural constraints in
intrinsically disordered proteins which finally safeguards proteins
against aggregation [6,67–69]. This led us to measure the RAP of
each individual protein in our dataset using TANGO algorithm
[7,56–57]. We found that NDD genes encoded proteins are highly
aggregation prone with respect to non-disease gene encoded
proteins (average RAP of NDD proteins=0.097, non-disease
proteins=0.083, P=9.2161026for NDD vs. non-disease pro-
teins). Moreover, we found an overall negative correlation between
RAP andpercentage ofintrinsically
(r=20.467, P=161026) and between RAP and evolutionary
rates (r=20.072, P=161026). Thus, we propose that RAP also
regulate the evolutionary rates of NDD and non-disease genes.
Independent Forces of Protein Evolutionary Rates Using
Categorical Regression Model
We have identified that gene expression level, number of
miRNAs targeting the gene, 39UTR length, percentage of
intrinsically disordered residues, number of interacting partners,
natures of hub (i.e. singlish interface hub/multi interface hub) and
RAP are the attributes regulating the evolutionary rates of the
NDD genes with respect to non-disease genes. In order to excavate
the independent influence of the above mentioned six predictor
variables on protein evolutionary rates, we performed categorical
regression analysis to best predict the value of the dependent
variable as categorical regression can optimally scale the
categorical data to its numerical equivalents . According to
our ANOVA model (F=13.648, P,0.05), protein connectivity,
Evolutionary Factors & Neurodegenerative Diseases
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39UTR length, RAP, nature of hubs (singlish/multi interface) and
disorder content were found to be the independent evolutionary
rate modulators (Table 2).
Profiling human neurodegenerative diseases from the perspec-
tive of protein evolutionary rates and comparing them with non-
disease genes can provide therapeutic clues against disease
pathogenesis. With this aim, we analyzed the evolutionary forces
affecting NDD genes taking non-disease genes as the control one.
We, thereby, found that higher selective pressure prevailed on
NDD genes compare to the non-disease group. To explicate the
reason behind such observation, we studied gene expression level,
protein connectivity, regulatory miRNAs, disorder content, nature
of hub proteins and relative aggregation propensity as evolutionary
functions. In support of the conserved nature of NDD genes, we
obtained higher gene expression level, higher protein connectivity
along with greater miRNA regulation associated with them
compare to the non-disease class. Interestingly, we observed lower
disordered content of NDD genes contrasting previous publica-
tions [17,18]. Moreover, the lesser disordered content of NDD
genes underpin higher aggregation propensity of NDD genes due
to lack of their conformational entropy , as reflected in our
results. Emphasizing on the evolutionary rates differences between
NDD and non-disease genes, our categorical regression model
ascertained the independent influence of protein connectivity,
presence of singlish/multi interface hub, protein disorderness,
RAP and 39UTR length among all the evolutionary parameters
studied in this present analysis (Table 2).
Figure 1. Multi-interface proteins are prevalent in NDD genes compare to non-disease genes. The bar diagram depicts the percentage of
hub proteins in NDD and non-disease genes within singlish and multi-interface hubs respectively. In each group, the dark bar represents non-disease
genes whereas other bar belongs to NDD category.
Table 1. Proportions of hub proteins in NDD and non-disease gene encoded proteins with different cutoff values for interaction
Hub contents in different
NDD proteins Vs. Non-disease
proteins (Respectively) Z scoreSignificance Level
With partner $5 50.000% vs. 33.880%5.49799.9%
With partner $10 31.292% vs. 15.599%6.80399.9%
With partner $20 14.966% vs. 5.760% 6.02899.9%
Note. 100% confidence level refers to significance level: P,0.01.
Table 2. Categorical regression to illustrate the independent
influential evolutionary features.
Parameter Standardized b score P value
39 UTR length
Protein intrinsic disorder 0.101
Gene Expression level
Evolutionary Factors & Neurodegenerative Diseases
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Our results share a conflicting view with Roychoudhury et al.
 and Uversky  regarding disorder content of NDD
proteins compare to the non-disease one. According to Roy-
choudhury et al. , NDD proteins are highly disordered
proteins being ‘‘hub’’ in nature whereas we uttered about low
disorderness of NDD proteins besides being hub proteins (Table 1).
The disagreement in our result with Roychoudhury et al. 
may arise due to the following reasons. In their analysis,
Roychoudhury et al.  chose only three well-known neurode-
generative diseases (‘‘Huntington’’, ‘‘Parkinson’’ and ‘‘Alzheimer’’
diseases) as the representative of NDD group among all the
different neurodegenerative diseases known at that time. However,
for proper characterization of NDD proteins, it is essential to
include all possible neurodegenerative diseases in the analysis.
Since, our analysis highlights evolutionary rate difference between
NDD and non-disease proteins, we only considered NDD proteins
which have available evolutionary rate data. By this means, we
collected 375 NDD proteins excluding neuropathies or lysosomal
storage diseases and risk associated disease susceptible genes
following extensive literature survey. Among them, 21.33%
proteins (80 proteins out of 375 proteins) in our dataset overlapped
with 352 NDD proteins selected by Roychoudhury et al. .
Proceeding further, we considered only overlapping 80 proteins
as NDD group. In doing so, our comparative study established
that NDD proteins share non significant (P=0.722) difference in
disorder content with respect to non-disease group. From these
results, we can conclude that the difference in gene selections may
be a reason for obtaining such dissimilar result with Roychoudh-
ury et al. . Moreover, our in depth analysis revealed that NDD
proteins are enriched with multi-interface hub (party hub) while
the non-disease class are well populated with higher proportion of
singlish-interface hub (date hub) (Figure 1). Since, multi-interface
hubs promote simultaneous binding through their interaction
domains compare to singlish-interface hubs, higher population of
multi-interface hub in NDD category go in favor of their
conserved nature. Hence, we proposed that the nature of ‘‘hub’’
was more important regulator of protein disorderness than hub
content and thereby, protein evolutionary rates. However,
Uversky  considered several case studies to demonstrate that
intrinsically disordered proteins can easily form ordered hydro-
phobic b-sheet topology in contrast to folded globular proteins,
required for fibril formation in aggregating proteins. Thus, he
concluded that human aggregation prone neurodegenerative
diseases are highly disordered proteins by nature. Regarding the
aforementioned controversy with Uversky , we can say that by
definition intrinsically disordered proteins lack any stable ordered
secondary/tertiary structure under physiological conditions and
prefers hydrophilic residues [13,18]. In addition, intrinsically
disordered positions in protein structures can not adopt any
ordered structure and it is reasonable to assume that the crystal
structures of those proteins do not contain any coordinate data of
the atoms in these intrinsically disordered positions. On the other
hand, b-sheet structures, a class of ordered secondary structures,
have their coordinate data maintained in the X-ray crystal
structures. Therefore, formation of b-sheet topology from in-
trinsically disordered proteins can contradict with their structural
definitions. On a final note, we can say that being a positive
evolutionary rate regulator , lower disorderness of NDD
proteins in our dataset can completely describe the conserved
nature of NDD proteins contrast to non-disease group.
From the perspective of gene expression level, our result
supports Bortoluzzi et al.  for having higher gene expression
level of human disease genes. On our way, we noticed that NDD
genes are ,2.54 fold highly expressed than non-disease class.
Moreover, tissue expression breadth data also supports our result
(Expression width of NDD genes=5.37, non-disease genes=2.16
Figure 2. Expression profiles of NDD and non-disease genes considering 84 tissues in 10 major tissue categories. In this bar diagram,
Cancerous, Circulatory, Connective, Excretory, Gland, Immune, Muscle, Neural, Reproductive and Respiratory tissues are abbreviated as Canc, Circ,
Conn, Excr, Gland, Immu, Musc, Neur, Repr, and Resp respectively. The dark and light bars in each group represent non-disease and NDD genes
respectively. From the picture, it is evident that our NDD genes are highly expressed in nervous system related tissues.
Evolutionary Factors & Neurodegenerative Diseases
PLOS ONE | www.plosone.org5 October 2012 | Volume 7 | Issue 10 | e48336
and P=5.63610218for NDD vs. non-disease genes). To obtain,
a suitable reason for the elevated expression level for NDD genes,
we checked the tissue distribution pattern of NDD genes compare
to the rest of the non-disease group (Figure 2). Following Greco
et al. , we classified 78 normal tissues into 9 major tissue
categories and considered rest of the 6 abnormal tissues as
‘‘cancerous’’ group. In doing so, we obtained that except
cancerous tissues, NDD genes share elevated expression level in
all tissue types (Connective, Excretory, Gland, Immune, Muscle,
Neural, Reproductive and Respiratory as shown in Figure 2).
Since, our primary focus is on neurodegenerative diseases, our
analysis (Figure 2) strongly supports the highest (P=0.0001)
expression of NDD genes near nervous system related tissues. In
addition, we observed that our non-disease genes on average show
uniform gene expression level within the range of 25–60 whereas,
for NDD class the inhomogeneous expression level often fluctuates
within the range of 25–150.
Molecular evolution is strongly fostered by genes’ efforts to
avoid/tolerate errors while producing proteins. Besides identifying
the evolutionary features of human neurological disorders, our
investigation has clarified the complicated relationships between
protein disorder content and RAP. Without these crucial
informations, the ability to diagnose, prevent, and treat neurolog-
ical disorders will remain incomplete.
genes and non-disease genes used in this study.
List of human neurodegenerative disease
We are thankful to two anonymous referees for their helpful suggestions in
improving our manuscript. We acknowledge S. Chakraborty and S.
Podder for their valuable comments and help. We also like to thank Mr.
Sanjib Gupta for his technical support.
Conceived and designed the experiments: AP TCG TB. Performed the
experiments: AP. Analyzed the data: AP. Contributed reagents/materials/
analysis tools: AP. Wrote the paper: TB TCG AP.
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