Deviation of amino acid utilization and correlation with G+C
composition in bacterial genome
Sajia Akhter, Hochul K Lee, Barbara Bailey, Peter Salamon, Robert Edwards
Computational Science Research Center, San Diego State University 2008 Kullback-Leibler Divergence on Amino Acid
Composition
0.09
Kullback-Leibler Divergence
0.08
The
Kullback‐Leibler
divergence
(KLD)
was
calculated
to
compare
the
distribu4on
of
amino
acids
in
different
protein
coding
sequences
as
a
measure
of
how
much
those
sequences
deviate
from
the
standard.
The
Kullback‐Leibler
divergence
(KLD)
was
calculated
for
372
whole
bacterial
genomes
and
for
proteins
in
subsystems
by
As
used
here,
Pi
is
the
frequency
of
the
ith
amino
acid
in
a
given
bacterial
genome
and
Q
i
is
the
average
frequency
of
ith
amino
acid
calculated
from
all
complete
genomes.
Non‐diverge
Genome
0.07
0.06
Divergence
of
Amino
Acid
u4liza4on
are
not
significantly
different
from
the
mean
for
all
subsystems.
0.05
0.04
0.03
0.02
0.01
0
A mino A cids and
Derivatives
Carbohydrates
Cell Divis ion and
Cell Cycle
DNA M etabolis m
M embrane
Trans port
Nitrogen
M etabolis m
Phos phorus
M etabolis m
Protein
M etabolis m
RNA M etabolis m
Sulfur
M etabolis m
Different Subsystems
Possible Explanation
Amino Acid Utilization
for
Divergence
of
Bifidobacterium adolescentis
0.16
0.14
Mean of KLD with SEM
0.12
Salmonella bongori 12149
Chlamydophila pneumoniae CWL029
Mean
Frequency of Amino Acid Utilization
  Secondary
metabolism
has
poor
correla4on
between
GC
content
and
amino
acid
composi4on
–
  High
level
of
horizontal
gene
transfer
  Limited
(167)
bacteria
have
this
subsystem,
and
most
of
those
have
GC
content
between
40%
and
60%.
Predicting Amino Acid composition based
on G+C content
An
explicit
expression
for
the
informa4on
content
is
available
once
a
surprisal/
devia4on
analysis
is
carried
out
(Levine,
1978)
m
ln(Qi /Pi ) = "0 + # "r Ar (i) !
r=1
where
Ar(i)
are
a
set
of
m
proper4es
for
the
state
i.
For
the
devia4on
of
amino
acid
composi4on,
since
the
interested
property
is
only
GC
content,
the
model
!
will
be
ln(Qi/Pi)
=
λ0
+
λ
(GC%)
[eqn
1]
Amino
Acids
and
their
GC
Sensi4vity
!"#$(-,$&%'(%)%*+(,$
40,5%-(%(0$647$
!18,+9%(0$6:7$
;5<0-(%(0$6;7$
0.1
0.08
0.06
Amino
Acid
0.04
0.02
a
s
ae
ri
te
te
og
ae
ac
ot
ch
m
ob
er
iro
 
are
significantly
different
from
the
mean
for
all
subsystems.
 
The
differences
are
not
restricted
to
one
or
few
metabolic
process
but
are
across
all
subsystems.
0.3
0.25
0.2
$
Th
Sp
a
ia
ria
te
ac
ob
te
ro
ap
m
ia
ri
er
ct
ba
eo
te
ot
ro
np
lo
si
am
Class of Bacteria
Divergence of Amino Acid Utilization in
different Subsystems
The
most
Divergent
Genome
  The
most
diverse
Genome
have
low
GC
content
(ranging
from
22%
to
28%)
  GC‐poor
bacteria
have
few
codons
for
alanine,
glycine,
proline,
and
arginine
  GC‐rich
bacteria
have
few
codons
for
phenylalanine,
isoleucine,
lysine,
asparagine,
and
tyrosine
Divergence of Amino Acid Utilization and G+C content
G
ia
er
er
te
ct
ct
ac
ba
ob
eo
te
ot
ro
pr
pr
ap
et
el
B
D
Ep
i
ha
lp
A
ta
es
ut
lic
ba
Fu
so
i
ia
ill
cc
ac
rid
ol
M
co
st
B
no
lo
C
e
ia
ia
ia
er
ob
ct
or
ba
hl
ei
C
no
D
C
ya
a
ri
ria
te
yd
te
ac
ac
am
ob
ob
hl
ng
hi
Sp
C
e
ri
ca
et
te
ifi
id
ro
qu
te
A
av
ac
B
Fl
ac
ob
in
ct
A
es
a
0
Kullback-Leibler Divergence
Nostoc sp. PCC 7120
Frequency
of
Amino
acid
 
Life
S'le
of
Organism
The
organisms
which
have
the
most
skewed
amino
acid
composi4ons,
are
intracellular
pathogens
with
a
very
limited
ecological
niche
range
and
restricted
lifestyle.
 
Phylogene'c
Effects
There
is
a
significant
difference
between
amino
acid
u4liza4on
in
different
phylogene4c
groups
of
bacteria.
Bacillus B-14905
  The
rela4onship
between
%GC
and
amino
acid
divergence
is
given
by
the
equa4on
y
=
2(x‐0.5)2,
where
x
is
the
%GC
and
y
is
the
divergence
of
amino
acid
composi4on.
  Most
subsystems
has
similar
parabolic
equa4on
with
high
regression
coefficient,
which
suggest
that
the
DNA
content
and
amino
acid
composi4on
were
related.
0.15
From
eqn
1,
Qi
=
Pi
exp
(λ0
+
λ
(GC%))
where,
λ
=
fidng
equa4on
1
with
actual
frequency
λ0
=
weighted
average
of
G+C
content
Finally,
Qi
=
Pi
exp
λ
(GC%
‐
avg(GC%))
Previous
Work
According
to
Knight’s
(2001)
correla4on
between
Amino
Acid
and
GC%
Qi
=
λ0
+
λ
(GC%)
Significance
of
Exponen'al
rela'onship
than
Linear
rela'onship
 
Exponen4al
rela4onship
uses
1
parameter
(λ)
instead
of
2
(λ
and
λ0)
though
the
Regression
coefficient
(R^2)
is
almost
same
for
both
rela4onship.
References
0.1
0.05
0
A mino A cids and
Derivatives
Carbohydrates
Cell Divis ion and
Cell Cycle
DNA M etabolis m
M embrane
Trans port
Nitrogen
M etabolis m
Phos phorus
M etabolis m
Protein M etabolis m
RNA M etabolis m
Different Subsystems
Wigglesworthia glossinidia
Borrelia garinii
Mycoplasma mycoides
Ureaplasma parvum serovar
Buchnera aphidicola
Mean
Sulfur M etabolis m
!"#$%&$&%'(%)%*+(,$
./0$&1-20$
3/0$&1-20$
=1+(%(0$6=7$
!18,+9%*$+*%?$6G7$
">&,0%(0$6"7$
C50(>1+1+(%(0$6H7$
=&2+<,%*$+*%?$6@7$
I&-108*%(0$6I7$
!1>*%(0$6!7$
B>&%(0$6J7$
A%&,%?%(0$6A7$
=&2+<+'%(0$6K7$
B08*%(0$6B7$
L0<%(0$6L7$
C<-1%(0$6C7$
;><-&%(0$6M7$
=<'%(%(0$6D7$
E+1%(0$6E7$
;<>2,-25+($6F7$
Levine
(1978),
“Informa4on
Theory
Approach
to
Molecular
Reac4on
Dynamics”
Annual
Review
of
Physical
Chemistry,
29(1):59
Knight
(2001),
“A
simple
model
based
on
muta4on
and
selec4on
explains
trends
in
codon
and
amino‐acid
usage
and
GC
composi4on
within
and
across
genomes”
Department
of
Ecology
and
Evolu4onary
Biology,
Princeton
University,
Princeton,
NJ
08544,
USA