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247
Computational analysis of human disease-associated genes and
their protein products
Kodangattil R Sreekumar, L Aravind and Eugene V Koonin*
The complete genome sequences for human, Drosophila
melanogaster and Arabidopsis thaliana have been reported
recently. With the availability of complete sequences for many
bacteria and archaea, and five eukaryotes, comparative
genomics and sequence analysis are enabling us to identify
counterparts of many human disease genes in model
organisms, which in turn should accelerate the pace of research
and drug development to combat human diseases. Continuous
improvement of specialized protein databases, together with
sensitive computational tools, have enhanced the power and
reliability of computational prediction of protein function.
Addresses
National Center for Biotechnology Information, National Library of
Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
∗e-mail: [email protected]
Current Opinion in Genetics & Development 2001, 11:247–257
0959-437X/01/$ — see front matter
© 2001 Elsevier Science Ltd. All rights reserved.
Abbreviations
BIR
baculovirus inhibition of apoptosis protein repeats
CARD
caspase recruitment domain
CBD
carbohydrate-binding domain
FRDA
Friedreich’s ataxia
IAP
inhibitor of apoptosis
MALT
mucosa-associated lymphoid tissue
MKKS
McKusick–Kaufman syndrome
PTP
protein tyrosine phosphatase
TFPP
type-4 prepilin peptidase
VEGFR-3 vascular endothelial growth factor receptor-3
phylogenetically divergent sources. This branch of biology
has greatly benefited from the finished and continuing
genome-sequencing projects, as well as sequencing efforts
undertaken on a smaller scale. Genome sequencing of
three eukaryotic organisms — the fruitfly Drosophila
melanogaster, the thale cress Arabidopsis thaliana and (in a
draft form) Homo sapiens — has been completed in the past
two years [1••–4••]. Table 1 lists some of the genes implicated in human disorders during the past year, and some of
the previously identified human disease genes for which
computational analysis in the same time frame has produced
important new findings.
Procedures for undertaking rigorous sequence analysis,
certain pitfalls in such analysis, methods to circumvent
these problems, and the predictive power of computational
analysis have been described previously [5–7]. Selected
tools and databases that are currently available and are
widely used in sequence and structure analysis are listed in
Table 2. Here we focus on recent insights obtained by
computational analysis of disease genes, and in particular
how defects in one or more protein domains affect the cellular function of the encoded protein. The work that we
discuss serves only to illustrate how computational analysis
of disease genes can provide important clues about molecular basis of pathogenesis (we apologize to those
researchers whose important contributions to this burgeoning
field are not be cited owing to space limitations).
Conserved globular domains and their defects
Introduction
Inherited diseases are caused by mutation(s) in one or
more genes. Diseases that are due to defect(s) in a single
gene are called monogenic diseases; polygenic diseases are
caused by defect(s) in more than one gene, with all those
genes individually contributing to the development of the
disease. In some diseases, such as Lafora’s (see below), the
defect in proteins implicated in the disease can be directly
related to the specific pathological state, whereas in others
the link between the disease manifestation and the apparent defect observed at the level of the nucleotide or
deduced amino-acid sequence may not be obvious.
Irrespective of whether there is an obvious relationship
between a defective gene and the pathological state, once
a disease-associated gene (or a ‘disease gene’, in a common, if not precise, parlance) is identified, computational
analysis of the gene is a critical step that can provide clues
to the molecular basis of pathogenesis and invaluable
insights for further experimental analysis.
Most proteins consist of one or more evolutionarily conserved domains, each with a distinct structure and
function. An alteration of the amino-acid sequence of a
domain is likely to hamper its proper functioning, especially if the alteration is in a highly conserved region and/or
involves a non-conservative replacement of an amino-acid
residue. Several tools are available for the automatic detection of protein domains to aid in function prediction
(Table 2); however, the existing domain databases are still
far from comprehensive, and the detection methods have
limited sensitivity. It is therefore critical to analyze protein
sequence in detail, on a case-by-case basis, by using several
tools and methods and by assessing the relevance of the
results obtained by computational approaches in the context of the available experimental data. Below, some recent
discoveries of defects in proteins that result in human disorders are discussed with a view to relate a defective
domain/motif to specific biological consequences.
Bcl10 and mucosa-associated lymphoid tissue lymphoma
Computational sequence analysis draws its strength from
the conservation of critical features of a gene/protein in
A frameshift mutation in Bcl10, resulting in truncation distal to the caspase recruitment domain (CARD), is
248
Genetics of disease
Table 1
Protein products of some genes mutated in human diseases.
Disease
Protein
Domain(s)*
Soluble, globular proteins
MALT
Paracaspase DEATH+ 2(IG) +
lymphoma
paracaspase
Other sequence
features/motifs
–
Demonstrated or predicted
protein function
Effect of mutation(s)
Reference
Predicted protein oligomerization, Translocation produces a
interaction with other proteins
chimericprotein that might be
and protease activity potentially
an inhibitor of apoptosis.
involved in apoptosis
[13]
[8,9]
MALT
lymphoma
Bcl10
CARD
Serine/
Threoninerich region
The Ser/Thr-rich domain may
be involved in regulatory
phosphorylation. The CARD
domain mediates specific proteinprotein interactions in the
apoptotic system
Frameshift and truncation
beyond the CARD domain
might renderthe protein
unstable or disruptSer/Thr
phosphorylation
Fukuyama
type CMD
Fukutin
Predicted
phosphorylsugar/choline
transferase
Signal peptide
Similarity to bacterial proteins
suggests a role in modifying
cell-surface glycoproteins or
glycolipids
Transposon insertion disrupts
[27,29]
the predicted enzymatic domain
Friedreich’s
ataxia
Frataxin
Frataxin
Mitochondrial
import peptide
Nuclear-encoded mitochondrial
protein with a key role in the
regulation of energy conversion
Several point mutations affect [30,33]
conserved amino-acid residues
Parkinson’s
disease
Parkin
Ubiquitin+
PARKIN_finger +
RING
–
E3 ubiquitin-protein ligase
Mutations in ubiquitin domain
[55,56•]
affect binding to target proteins;
mutations in RING prevent
recruitment of E2
Giant axonal Gigaxonin
neuropathy
POZ+IVR+ kelch
repeats
–
POZ-kelch domain
combination suggests a role in
cytoskeleton structure–assembly
Mutations that disrupt POZ or [57]
Kelch domains might disrupt
protein–protein interactions
causing in cytoskeleton defects
Retinitis
pigmentosa
MERTK
Ig-like+ Fn3-like+
Tyr-kinase
–
Fn3 and Ig-like modules indicate
ligand-binding ability; Tyr-Kinase
domain implies protein
phosphorylation; MERTK may be
a receptor for a specific extracellular ligand
Mutations affecting Tyr-kinase [58]
domaindisrupt the phosphorylation
cascade activated by it.
Laterality
defects
CFC1
EGF+ CFC
Macular
corneal
dystrophy
CHST6
Sulfo-transferase Signal peptide
Transfers sulfate groups from
PAPS to various substrates
R50C mutation in the conserved [60]
region encompassing the sulfate
donor binding site may prevent
PAPSbinding
Dominant
OPA1
optic atrophy
Dynamin-like
GTPase
Mitochondrial
import signal
Possible role in maintenance
and inheritance of mitochondria
R290Q, G300E, deletion of
[45,46]
invariant I432 and a nonsense
mutation in the dynamin GTPase
domain
X-linked
congenital
SNB
nyctalopin
LRR-NT+
15(LRR)+
LRR-CT
Signal peptide,
GP anchor
Predicted LRR–containing
glyco-protein of extracelluar
matrix mediating protein–protein
interactions
Missense mutations cause
truncation, insertion and
deletions affecting LRRs
and/or loss of GPI–anchor
[61,62]
X-linked
mental
retardation
ARHGEF6
CH+SH3+
RhoGEF+ PH
–
Signal transduction via the
RhoGTPase cycle (RhoGEF
domain); predicted to interact
with actin filaments proline-rich
peptides in proteins and inositol
phosphate.
Intronic mutation resulting in
exon 2 skipping, affects CH
domain
[63]
Lafora’s
disease
EPM2A
CBD-4 +tyrosine Signal peptide
phosphatase
(PTP)
Signal peptide
Extracellular signal molecule
and membraneassociating region
Mutations in the conserved
residuesin EGF domain could
disrupt inter-actions with its
receptor
Binds polysaccharides via CBD-4; W32G mutation in CBD,
phosphatase domain may signal T194I in the PTP domain
the catabolism of Lafora bodies.
[59]
[23]
Computational analysis of disease-associated genes and their products Sreekumar et al.
249
Table 1 (continued)
Protein products of some genes mutated in human diseases.
Disease
Protein
Domain(s)*
Other sequence
features/motifs
Demonstrated or predicted
protein function
Usher
syndrome
type 1c
Harmonin
2(PDZ)+bZIP+
PDZ
Proline/Serine/
Threonine-rich
region; leucine
zipper
Predicted to dimerize via leucine Mutations resulting in loss of
zipper and mediate proteinmost orall of the globular
protein interactions via PDZ
domains.
domain. The P/S/T-rich region
might serve as binding site for
SH3 and WW domain-containing
proteins
[64]
Familial
segmental
glomerulosclerosis
ACTN4
2(CH)+ 4(SPEC)
+ 2(EF)
–
Predicted actin-binding and
cross-linking protein (CH
domains) and Ca-binding
(EF hands) protein. Probable
component of the cyto-skeletal
network (SPEC domain)
K228E, T232I, and S235P
mutations in the 2nd
CH domain cause increased
affinity for actin
[65]
May-Hegglin MYH9
anomaly and
Fechtner and
Sebastian
syndromes
Myosin + IQ
–
Nonmuscle myosin heavy chain
9; predicted to bind calmodulin
(IQ motif)
N93K predicted to destabilize [66]
the 2nd helix of myosin catalytic
domain, R702C predicted to
alter helical stability and ATPase
activity.
Mulibrey
nanism
MUL
RING+ B-box +
BBC + MATH
–
Predicted nuclear protein,
may participate in ubiquitinmediated protein degradataion
as a E3 ubiquitin ligase
(RING) and possibly in
apoptosis (MATH)
Deletions (splice site and
lcoding) ead to truncation,
with the loss of MATH
domain in two cases
McKusickKaufman
syndrome
MKKS
Group II
chaperonin
–
Facilitates correct protein
folding in conjunction with ATP
hydrolysis
One mutation predicted to
[34••]
affect intermolecular interaction
based on structural modeling
combined
pitutary
hormone
deficiency
LHX3
2(LIM) + HOX
–
Predicted transcriptional
regulator via DNA-binding
homeodomain protein–protein
interaction via LIM domain
A conserved Y replaced by C [67]
in LIM domain; frameshift
leading to loss of homeodomain
Netherton
syndrome
SPINK5
15 (KAZAL)
Signal peptide
Potential extracellular adhesion
molecule
Insertions and deletions leading [68]
to frameshift and protein
truncation
familial
cylindromatosis
CYLD
3(CAP-GLY)+
Proline-rich
B-BOX+ UCH-2 region
Nonsense and splice site
mutations leading to truncation [69]
of the C-terminal 2/3 of the
protein
Type 2
diabetes
MAPK8IP1
SH3 + PTB
Predicted to coordinate attachment of cellular organelles to
microtubules via CAP-GLY
domain and down-regulate
protein degradation via the
ubiquitin hydrolase via UCH-2
domain
Regulation of JNK signaling
pathway by mediating
protein–protein interactions via
the SH3 and PTB domains
DNA-binding protein,
transcription factor
P148H enhances DNA-binding [15,16]
affinity of homeobox; R172H
and deletion of RK159-160
cause low DNA-binding affinity
of homeobox
Multiple DNA-binding
domains predict a
transcription factor
3 nonsense and 3 frameshift
mutations cause loss of 2–4
Zn-fingers
[71]
ER membrane-associated
translation initiation
factor eIF-2α kinase
Mutations in kinase domain or
truncations N-terminal to the
kinase domain. β-propeller
domainin the N-terminus was
previouslyundetected
[72]
CranioMSX2
synostosis
and enlarged
parietal
foramina
Homeobox
Tricho-rhino- TRPS1
phalangeal
syndrome
type 1
8 (C2H2) + GATA
+ 2(C2H2)
Membrane proteins
Wolcott–
EIF2AK3
Rallison
syndrome
β-propeller +
TMS TM + S/T
protein kinase
N-terminal lowcomplexity
region, probably
non-globular
–
Signal peptide
Effect of mutation(s)
Reference
[41•]
S59N mutation outside the two [70]
globular domains, not fully
penetrant
250
Genetics of disease
Table 1 (continued)
Protein products of some genes mutated in human diseases.
Disease
Protein
Domain(s)*
Other sequence
features/motifs
Demonstrated or predicted
protein function
Effect of mutation(s)
Reference
Endotoxin
TLR4
hypo-responsiveness
11(LRR) +
LRRCT + TMS
+ TIR
Signal peptide
Intracellular signaling (TIR
domain), cell adhesion and
LRRs interaction with protein
ligands (extracellular LRRs)
Mutations A290G and T399I
within the LRRs
[73]
Diastrophic
dysplasia
DTD
8(TMS) + STAS
–
Sulfate transporter
Impaired sulfate transport across [39,74,
cell membrane cause low sulfate 75]
content of cartilage
Pendred
syndrome
Pendrin
10(TMS) + STAS
–
Potential iodide-chloride transporter
Mutation in predicted
phosphate-binding loop.
Also deletions, missense
and splice site mutations
[38,39•]
congenital
chloride
diarrhoea
DRA
10(TMS) + STAS
–
Chloride–NaHCO3 exchanger
Missense and deletion
mutations
[39•,76]
X-linked
mental
retardation
TM4SF2
4(TMS)
Member of the tetraspanin family
that potentially interact with
β-1 integrins.
Translocation removing 4th
TMS
[77]
Membrane-associated, ATPdependent transporter
Mutations in the NTP-binding
motifs (nearly) abolish
ATP binding
[78]
Signal peptide
Stargardt
ABCA4
disease, agerelated
macular
degeneration
5(TMS) + ABC+
7(TMS) + ABC
Presenile
dementia
TYROBP
TMS
Signal peptide
and ITAM motif
Predicted transmembrane protein Large deletion, and single base [79]
involved in signal transduction via pair deletion cause truncation
the ITAM motif
in TM segment and loss of
ITAM motif
Niemann–
Pick C1
disease
NPC1
13(TMS) +
sterol-sensing
domain
Signal peptide
Lysosomal protein involved in
Glu20stop: truncation, perhaps [80,81]
sequestering sterols in lysosomes null; another truncation results
from frameshift in exon2
Spondylocostal
dysostosis
DLL3
DSL + 11(EGF)
+ TMS
Signal peptide
Predicted Ca2+-binding
membrane protein involved in
signal transduction
Insertion leading to truncation
in DSL domain; deletion in
4th EGF repeat and G385D
mutation in 5thEGF repeat
Serine/
Threonine-rich
region
Predicted protein tyrosine kinase
involved in signal transduction
and ligand-binding
Y755stop, W749stop, and a
deletion leading to frameshift
[83]
beyond G750, perhaps affect
interaction with an SH3 domain
ATP-powered Ca2+ pump
that transports Ca2+ into Golgi
bodies
Large deletion, missense and
splice site mutations cause
truncation
Class III receptor Tyr kinase;
binds ligands and is involved
in signal transduction
G857R mutation in kinase motif; [36••,85]
R1041P, R forms H-bonds with
substrate OH- and aspartic acid
that is involved in phosphotransfer
Apparently a membrane aspartyl
protease, with two aspartate
required for activity
G384A mutation, next to the
[14••,86]
critical D385, causes higher
levels of residues amyloidogenic
Aβ42 production
BrachyROR2
IGc2 + Frizzled
dactyly type B+ Kringle + TMS
+ TyrKinase
Hailey–
Hailey
disease
ATP2C1
8(TMS) + P-type
ATPase
Primary
lymphoedema
VEGFR-3
2(IG) + IGc2 +
IG + IGc2 +
3(IG) + IGc2 +
IG + IGc2 +
TMS+TyrKinase
Alzheimer’s
disease
Presenilin 1
9(TMS)
–
Signal peptide
–
[82]
[84]
*Domain name abbreviations are as in the SMART database. The
number of domains of a particular type is indicated (for example, 2IG
denotes two consecutive immunoglobulin domains). Consecutive
domains are connected with ‘+’. Abbreviations: CMD, congenital
muscular dystrophy; PAPS, phosphoadenosine phosphosulfate; TM,
transmembrane; SNB, stationary night blindness.
associated with mucosa-associated lymphoid tissue
(MALT) lymphomas that have the translocation
t(1;14)(p22;q32) [8,9]. The CARD domain was first identified in a comparison of the sequences of several apoptotic
proteins [10]; this CARD domain comprises six antiparallel
α helices and mediates homotypic interactions between
proteins involved in apoptosis [11]. Besides caspase
recruitment, which is a critical step in the chain of events
leading to apoptosis, protein–protein interactions mediated
by CARD contribute to activation of the transcription
Computational analysis of disease-associated genes and their products Sreekumar et al.
factor NF-κB. The mutated Bcl10 protein seen in MALT
lymphomas has lost its pro-apoptotic functions but retains
the ability to activate NF-κB [12].
In a different MALT lymphoma translocation,
t(11;18(q21;q21), a fusion of the inhibitor of apoptosis
(IAP)-2 gene to the MLT1/MALT1 locus generates a
chimeric protein comprising BIR repeats of IAP-2 and the
caspase-like predicted protease domain of the protein designated paracaspase [13••]. The paracaspase family of
caspase homologs has been identified recently, along with
the metacaspase family, in a detailed computational analysis
of the caspase-like protease superfamily [13••].
Paracaspases contain a predicted caspase-like proteolytic
domain, a Death domain capable of mediating specific protein–protein interactions and, in certain cases, including
the human representative immunoglobulin domains. In
the MALT lymphoma translocation t(11;18)(q21;q21), the
prodomain of human paracaspase is replaced with BIR
repeats that might function as inhibitors of apoptosis. The
BIR–paracaspase fusion has been found to activate NF-κB
in a manner dependent on the predicted catalytic cysteine
of the paracaspase, thus supporting the computational prediction [13••]. Notably, the prodomain of human
paracaspase interacts with Bcl10, which suggests that the
two translocations associated with MALT lymphomas
affect the same apoptotic pathway.
Alzheimer disease — presenilins
Familial Alzheimer disease is associated with mutations in
genes that encode the paralogous integral membrane proteins presenilin 1 and presenilin 2. Presenilins are required
to cleave several other integral membrane proteins, including the β-amyloid precursor proteins Notch and Ire1 that
are central to the pathogenesis of Alzheimer disease. It has
been shown that two aspartate residues of presenilin 1 —
one located in the intracellular loop between helices 6
and 7 and the other one located in helix 7 — are required
for these proteolytic events, leading to the hypothesis that
presenilins themselves belong to a distinct class of membrane
proteases called γ-secretases [14••].
Sequence database searches using all available methods,
including different types of profile analysis, have failed to
detect similarity between presenilins and any known proteases. However, a pattern search carried out by Steiner
et al. [14••] revealed that the signature [G/A]xGDh (where
x is any residue, h is a bulky hydrophobic residue, and
alternative residues are shown in brackets) is shared by presenilins and bacterial type-4 prepilin peptidases (TFPP),
and includes one of the functionally critical aspartates of
both protein families.
Presenilins and TFPPs have similar membrane topology,
with eight transmembrane helices present in each family,
but show no appreciable sequence similarity beyond the
above signature. Therefore, although Steiner et al.’s [14••]
251
observation reinforces the hypothesis that presenilins are
the catalytically active γ-secretases, rather than cofactors
of a still unidentified protease, it remains unclear whether
they are homologs of TFPPs or whether the similarity in
the (predicted) active sites is due to convergence. Given
the relatively relaxed functional constraints that are typical of membrane proteins, with selection preserving
mainly the membrane topology rather then sequence,
common origin cannot be ruled out despite the absence of
sequence conservation.
MSX1 and MSX2
Craniosynostosis and enlarged parietal foramina are caused
by mutations in the homeodomain — a highly conserved
DNA-binding domain containing three helical regions —
of the MSX2 protein [15,16]. A mutation (P148H) that
leads to craniosynostosis enhances the DNA-binding affinity of this protein, whereas another mutation (R172H) that
results in parietal foramina has the opposite effect of lowering the protein’s affinity for DNA. Similarly, a mutation
in the MSX1 gene (R31P) that affects the second helix of
the homeodomain [17], and a nonsense mutation that leads
to a truncation at position 104 and a peptide lacking the
entire homeodomain [18] are implicated in tooth agenesis.
Other examples of diseases caused by mutations in homeodomains include microphthalmia [19], amegakaryocytic
thrombocytopenia and radio-ulnar synostosis [20]. The
paired (PAX) domain, another DNA-binding module
involved in transcription regulation, is defective in certain
individuals diagnosed with oligodontia [21]. An enhanced or
diminished DNA-binding capacity of a transcription factor is
likely to result in altered level of protein(s) encoded by genes
under its control, which in turn might lead to pathogenesis.
Laforin
The presence of polyglucosan inclusion bodies in the brain
is characteristic of progressive myoclonus epilepsy or
Lafora’s disease. The EPM2A product, laforin, which is
defective in patients suffering from this disease, was initially designated as a protein tyrosine phosphatase (PTP)
because of the presence of a consensus sequence characteristic of the catalytic site of PTPs [22]. Minassian et al.
[23••] have carried out a detailed computational analysis
resulting in significant insights about this protein and its
probable link to the accumulation of polyglucosan inclusions. Analysis of protein domains in laforin using the Pfam
database [24] revealed the presence of the carbohydratebinding domain (CBD)-4 at the amino (N) terminus of the
protein, in addition to the dual-specificity phosphatase
domain, which belongs to a distinct subfamily of PTPs, at
the carboxyl (C) terminus. Furthermore, two sequence motifs
characteristic of the glucohydrolase family as documented
in PROSITE [25] were detected.
On the basis of these findings and the mutations detected
in the EPM2A gene, Minassian et al. [23••] proposed that
normal laforin prevents the accumulation of polyglucosan
252
Genetics of disease
Table 2
Selected tools and databases useful in sequence analysis, with an emphasis on those dedicated to disease genes*.
Tools/database
Comments
URL
Reference
Tools and databases for functional annotation
SignalP
Predicts location of signal peptide and cleavage site
in proteins.
http://www.cbs.dtu.dk/services/SignalP/
[87]
TopPred
Predicts location and orientation of TM segments.
http://www.sbc.su.se/~erikw/toppred2/
[88]
CDD
Collection of protein domains with links to structures if
available. CDD uses a library of PSSMs that is searched by
Reverse Position-Specific BLAST to match a protein query.
http://www.ncbi.nlm.nih.gov./Structure/cdd/cdd.
shtml
[89]
COG
Phylogenetic classification of proteins from complete
genomes that groups orthologous proteins; useful for
functional annotation of newly sequenced genomes.
COGNITOR can be used to place a protein
into one of the COGs.
http://www.ncbi.nlm.nih.gov./COG/
[52•]
Pfam
Collection of protein domains and families consisting of
http://www.sanger.ac.uk/Pfam/
multiple alignments and hidden Markov models generated in a
semi-automatic manner.
[24]
SMART
Searchable collection of protein domains for detecting and
analysing domain architecture.
http://smart.embl-heidelberg.de/
[90]
BLOCKS
Database of highly conserved regions of proteins derived
automatically and represented in the form of ungapped
multiple alignments. Also tools to detect and verify protein
sequence conservation.
http://www.blocks.fhcrc.org/
[91]
PRINTS
Database of protein fingerprints (group of motifs distributed
in the sequence characteristic of a family).
http://bmbsgi11.leeds.ac.uk/bmb5dp/prints.html
[92]
PROSITE
Database of biologically significant sites, patterns and
profiles in proteins that helps in function prediction.
http://www.expasy.ch/prosite/
[25]
http://www.ncbi.nlm.nih.gov/BLAST/
[28]
Tools for sequence similarity search
BLAST
Programs for rapid sequence similarity searches for
protein and DNA sequences. Also provides searches for
nucleotide sequences translated in all six frames.
HMMER
Programs for constructing multiple protein
sequence alignments and performing database searches
using the HMM approach.
http://hmmer.wustl.edu
[93]
PSI-BLAST
An implementation of BLAST capable of detecting distantly
related proteins by creating a PSSM from the significant
matches detected in one round and using that PSSM
as the query in the next iteration.
http://www.ncbi.nlm.nih.gov./blast/psiblast.cgi
[28]
http://dodo.cpmc.columbia.edu/pp/
predictprotein.html
[94]
Tools for protein structure prediction and comparison
PHD
Neural network dependent secondary structure prediction.
PSI-PRED
Prediction of secondary structure using PSI-BLAST
derived scoring matrices.
http://insulin.brunel.ac.uk/psipred
[95]
Hybrid fold
recognition
Combined algorithm for fold recognition using both
sequence-based evolutionary information and secondary
structure predictions.
http://www.cs.bgu.il/~bioinbgu
[96]
FSSP/DALI
Automatic structural classification of protein domains with the http://www2.ebi.ac.uk/dali/fssp/
DALI search facility for structure comparisons.
[97]
SWISS-PDB
Viewer and Swiss
Model
Powerful structure visualization and homology modeling
system for personal computers.
http://www.expasy.ch/spdbv/
[98]
VAST
Structure similarity search tool based on the
definition of the threshold of statistically significant
structural similarity.
http://www.ncbi.nlm.nih.gov/Structure/
[99]
Databases for disease genes
Genes and
Information on selected human diseases with links to genes,
Disease
chromosomes, scientific literature and OMIM.
OMIM
Catalog of human genetic disorders and associated
phenotypic and genetic information.
http://www.ncbi.nlm.nih.gov./disease/
http://www.ncbi.nlm.nih.gov./entrez/
query.fcgi?db=OMIM
[100]
Computational analysis of disease-associated genes and their products Sreekumar et al.
253
Table 2 legend
*The number of tools and databases for computational analysis of genes and proteins that are available online has been rapidly growing during the
past few years. Inevitably, this is an arbitrary selection of those we have used extensively and found to be useful. HMM, hidden marker model;
PSI-BLAST, position-specific iterating BLAST; PSSM, position specific scoring matrix.
inclusion bodies in neurons by binding polyglucosan
through the CBD and cleaving the β-linkages through the
putative glucohydrolase domain. A more detailed examination of the sequence and domain architecture of laforin
shows, however, that the putative glucohydrolase domain
largely overlaps with the confidently predicted dualspecificity phosphatase domain, which suggests that
the presence of the glucohydrolase motifs in this protein
is spurious (KR Sreekumar, L Aravind, EV Koonin,
unpublished data).
The dual-specificity phosphatase activity of laforin has
been confirmed experimentally [26]. The CBD domain of
laforin probably targets the protein to polyglucosan inclusion bodies, whereas the phosphatase domain might
activate a protein dephosphorylation pathway that triggers
its destruction through a still unknown mechanism. This
example illustrates both the utility of computational analysis of protein domains for function prediction and the need
for caution in interpreting computational results.
Fukutin
Fukuyama type congenital dystrophy is caused by retroposon
insertions in the gene encoding a protein called fukutin [27].
Extensive database searches using the PSI-BLAST program
[28] detected moderate, but statistically significant similarity
between fukutin and a family of bacterial and eukaryotic
enzymes that catalyze phosphoryl-ligand transfer. Additional
multiple alignment analysis showed that fukutin shares with
these proteins the predicted catalytic residues [29].
The combination of these observations with the prediction
of a signal peptide in fukutin, and the experimental data
on its localization in the endoplasmic reticulum and secretory granules [27] has led to the prediction that fukutin
modifies cell-surface molecules, most probably through
the attachment of phosphoryl-sugar moieties [29].
Frataxin
Friedreich’s ataxia (FRDA) is an autosomal recessive disease
characterized by progressive ataxia, hypertrophic cardiomyopathy and diabetes mellitus. Gibson et al. [30] have carried
out computational analysis of the FRDA gene product,
frataxin, and shown that it has homologues in bacteria of the
γ subdivision of the Proteobacteria, but not in any other
prokaryotes, and also that it possesses a non-globular N-terminal domain predicted to function as a mitochondrial
targeting peptide [30]. They predicted a distinct fold, consisting of a β sheet flanked by two long α helices, on the basis
of a multiple alignment of the frataxin homologs, and further
proposed that frataxin is anuclear encoded, and, accordingly,
that FRDA is a ‘mitochondrial disease’.
Both the mitochondrial localization and the αβ sandwich
structure predictions, which were based on computational
analysis of frataxin, have been confirmed experimentally
[31,32], and more recent studies have shown that frataxin
is a key regulator of mitochondrial energy conversion [33•].
McKusick–Kaufman syndrome
Mutations in a gene on chromosome 20 have been shown
to cause McKusick–Kaufman syndrome (MKKS) and
Bardet–Biedl syndrome (see also review by Sheffield et al.,
this issue, pp 317–321) — developmental anomalies that
involve hydrometrocolpos, postaxial polydactyly and congenital heart disease [34••,35]. Sequence database searches
show that the predicted protein encoded by the MKKS
gene belongs to a family of group II chaperonins that
promote protein folding in an ATP-dependent manner.
The three-dimensional structural model of the MKKS protein predicts that the Y37C mutation lies in the highly
conserved loop region between helix 1 and strand 2 which is
involved in interaction among subunits [34••]. Another mutation, H84Y, is predicted to lie in a region responsible for ATP
hydrolysis. This appears to be the first description of a disease
caused by a defect in a molecular chaperone — a finding that
complements the data on the role of defects in transcription
regulators in several diseases, as discussed above.
Vascular endothelial growth factor receptor-3
Another example of a human disease for which sequence
analysis and three-dimensional structure modeling has provided insights into pathogenesis is primary lymphoedema
caused by mutations in vascular endothelial growth factor
receptor-3 (VEGFR-3) [36••]. The VEGFR-3 protein consists of six classical immunoglobulin domains, four
immunoglobulin C2 domains, a signal peptide, a single
transmembrane segment and an intracellular tyrosine
kinase catalytic domain.
Four mutations that co-segregate with lymphoedema phenotype map within the tyrosine kinase domain. Finegold
and co-workers [36••] have built a three-dimensional
model of VEGFR-3, based on the crystal structure of
VEGFR-2, from which functional implications of mutations observed in VEGFR-3 can be inferred. For example,
the model shows that the G857R mutation (the last glycine
of the conserved GXGXXG kinase motif) lies in a critical
turn between β strands 1 and 2 and is expected to affect
ATP-binding and catalysis severely.
DTD, Pendrin and congenital chloride diarrhoea
Diastrophic dysplasia/achondrogenesis type IB (DTD),
Pendred’s syndrome and congenital chloride diarrhoea are
254
Genetics of disease
caused by malfunctions of anion transporters of the sulfate
transporter family [37,38]. Unexpectedly, it has been
shown that the C-terminal cytoplasmic portion of these
proteins shares a conserved domain named STAS (after
sulfate transporter anti-sigma) with bacterial anti-sigmafactor antagonists, and this connection provides clues for
the regulation of anion transporters [39•]. (Anti-sigma factors prevent formation of active RNA polymerase
holoenzyme by trapping the sigma factor. Anti-sigma
antagonists bind the anti-sigma factors and prevent the formation of sigma-antisigma complex.) The STAS domain of
the antisigma-factor antagonist binds GTP and has a weak
GTPase activity [40], leading to the prediction that the
cytoplasmic domain of the anion transporters might regulate
transport through NTP binding [39•].
Mulibrey nanism
Muscle–liver–brain–eye (Mulibrey) nanism is an autosomal
recessive disorder that affects several tissues of mesodermal
origin, which suggests that a highly pleiotropic gene is
involved in this disease. The complex multidomain architecture of the recently cloned MUL gene product is
compatible with this notion [41•]. The MUL protein comprises a RING-finger domain, a B-box domain, a coiled-coil
domain and a MATH domain. Avela et al. [41•] suggest that
MUL is likely to be involved in various protein–protein
interactions that might be important in development regulation; however, the combination of domains present in this
protein calls for more specific functional predictions.
There is growing evidence that the primary function of the
RING domain is as the E3 component of the ubiquitin ligase cascade that facilitates the transfer of ubiquitin from
the E2 protein to target proteins [42]. MATH is a versatile
adaptor domain that mediates specific protein–protein
interactions in the programmed cell death system, chromatin remodeling and other functional contexts [43].
Thus, MUL can be predicted to function as an E3 ubiquitin ligase, with additional regulatory interactions, possibly
related to apoptosis, mediated by the MATH domain.
Mutations outside functional protein domains
Mutations that lie outside globular domains and transmembrane segments of proteins, or outside the protein-coding
part of a gene altogether often affect gene expression. Such
mutations may be located in untranslated regions, promoters,
introns and sequences encoding signal peptides. The effect
of these mutations may manifest itself as a defective globular or transmembrane domain, however, owing to exon
skipping, aberrant splicing and abnormal cellular localization.
Untranslated and non-coding regions
Sequence analysis can provide hints as to the molecular basis
of defects caused by mutations in untranslated regions. For
example, in individuals with severe protein C deficiency,
who suffer from massive disseminated intravascular coagulation or neonatal purpura fulminans, promoter mutations in
the protein C (PROC) gene have been identified. These
mutations alter the consensus sequence for the transcription
factor HNF-3-binding, resulting in reduced PROC promoter
activity, and accordingly a reduced level of protein C [44].
A splice-site mutation in intron 9 of the OPA1 gene, which
has been detected in individuals with autosomal dominant
optic atrophy, leads to either exon-10 skipping or a
frameshift with a premature stop codon [45]. The OPA1
product is a mitochondrial protein comprising an N-terminal mitochondrial localization signal and a dynamin
GTPase domain. The defective protein produced by the
mutant gene lacks a part of the GTPase domain [45,46].
Signal peptides
Duplications in exon 1 of the TNFRSF11A (RANK) gene
that segregates with familial expansile osteolysis have
been identified recently [47]. These mutations are short
in-frame insertions in the hydrophobic core region of the
RANK signal peptide, and affect proper cleavage of the
signal peptide.
The RANK protein consists of four tumor-necrosis factor
repeats and a transmembrane segment, besides the signal
peptide that directs the protein to the secretory pathway.
Failure to cleave the signal peptide might result in higher
intracellular accumulation of defective RANK in compartments of the secretion pathway that could lead to a higher
incidence of receptor self-association and increased RANK
signal transduction [47]. An NF-κB responsive reporter
assay of transfected cells has indeed suggested that mutations occurring in familial expansile osteolysis result in
increased constitutive RANK signaling [47].
Conclusions and future directions
The examples of disease genes discussed here and in previous publications [6,48–50] illustrate the importance of
sequence and structure analysis in predicting the molecular basis of pathogenesis, in guiding further work and in
understanding the biology of the respective functional systems. Proteins for which predictions based on sequence
analysis have been experimentally verified include frataxin
and the MALT-associated paracaspase. Using the complete
genome sequences, efforts are being made to assemble
databases of orthologous genes from diverse organisms,
and these databases are already proving to be valuable in
functional annotation of genes [51,52•].
The presence of orthologs for many of the human disease
genes in model organisms such as mouse, fruitfly, nematode and yeast enable experimental verification of the
predictions. In particular, apparent counterparts for 178 of
287 analyzed human disease genes have been identified in
fruitfly by large-scale comparative genomics [53••,54••]. At
present, some of the human disease genes do not seem to
have an ortholog in other organisms, but the anticipated
availability in the next several years of the complete
genome sequences of primates and other mammals should
drastically reduce the number of ‘orphan’ disease genes.
Computational analysis of disease-associated genes and their products Sreekumar et al.
The predictive power of sequence and structure analysis,
combined with the wealth of genome sequence data,
should not only enhance our understanding of the biology
that underlies the effect of mutations in disease genes, but
also enable the identification of many new drug targets and
accelerate the process of drug design and the development
of therapeutic strategies.
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