Isoelectric point prediction from the amino acid sequence of a protein

74 Pages·2016·8.91 MB·English

by  Matthew Conte

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Matthew Conte
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Conte, Matthew, "Isoelectric point prediction from the amino acid sequence of a protein" (2005). Thesis.
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THESIS
ISOELECTRIC POINT PREDICTION FROM THE
AMINO ACID SEQUENCE OF A PROTEIN
Submitted by
Matthew Conte
Department of Biological Sciences
In partial fulfillment of the requirements
For the Master of Science degree in
Bioinformatics at
Rochester Institute ofTechnology
Summer 2005
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Abstract
Proteins often do not migrate as expected in two dimensional electrophoresis
based on their primary sequence. The predicted isoelectric point (pi) frequently does not
coincide with experimental pivalues obtained in the laboratory. The reasons forthese
differences led to this study. Initially, 2DE data from theE. coliproteome was collected
and formatted. This dataset was split into three parts each consisting ofdifferent levels of
pi discrepancy (Apl). The protein sequence data for each Apl subsetwas run through a
pipeline. At each stage ofthepipeline the datawere analyzedbycomparingeach ofthe
three Apl subsets to one another. The pipeline consisted ofanaive approach(considering
individual amino acid frequencies), followedbythe application fourdifferent alphabets
to represent sequences in a simplerwayby grouping similaramino acids based ontheir
charge, functional, chemical, andhydrophobic properties . The final step inthe pipeline
involved investigating the dipeptides ofall ofthese sequences usingboth the 20 amino
acid alphabet andthe simplifiedgroupings. An evaluation ofthe alphabet dipeptide
analysis demonstrated the existence ofcertain dipeptide sequences which correlate well
with differences betweenpredictedpi and experimental pi.
in
Table of Contents
1 Introduction 1
2 Methods 7
2.1 Forming the data set 7
2.2 Experimental and predicted pi values 9
2.3 Extracting useful information from collected subset
10
sequences
2.2.1 Amino acid frequencyanalysis (naive approach) ... 10
2.2.2 Frequencyofamino acids (alphabetsapproach) ... 1 1
2.2.3 Frequencyofaminoacids (dipeptide approach) ... 14
2.2.4 Pipelineworkflow 15
3 Results 18
3.1 Naive approach 18
3.2 Alphabets approach 19
3.2.1 Charge 19
3.2.2 Chemical 21
3.2.3 Functional 22
3.2.4 Hydrophobic 23
3.3 Dipeptide approach 24
3.4 Dipeptide threshold 26
3.5 Dipeptide using alphabets 28
3.5.1 Charge 28
3.5.2 Chemical 29
3.5.3 Functional 31
3.5.4 Hydrophobic 32
4 Discussion 34
5 Conclusions 42
6 References 44
Introduction
Two-dimensional gel electrophoresis (2DE) has been an important laboratory
technique forthe field ofproteomics for overtwo decades. 2DE allows the researcherto
separate and identifythousands ofproteins from a cellularextract in a single experiment.
2DE is difficult and time consuming as it is necessary to determine ideal initial
conditions, wait forresults, andpossibly change conditions afterthat (1). In addition,
reproducibility ofgels and comparison of2DE results between separate groups has
proved difficult (1). In 2DE, proteins are separatedinthe first dimensionbytheir
isoelectric points (the pH atwhichthe net charge ofthe protein is zero) and inthe second
dimension bytheir molecularweights. The accurate prediction ofprotein isoelectric
point (pi) andmolecular weight (MW) using simplythe amino acid sequence ofthe
protein wouldbe extremelyvaluableto researchers who usetwo-dimensional gel
electrophoresis.
Computationalprocedures forcalculating andpredictingthe pi fromthe amino
acid composition ofaproteinbased onthe dissociationconstants ofthe charged groups
withintheproteinhavebeen developed(2-8). The accuracy ofthese algorithms is
limitedbythe certaintyofthe values forthe dissociations constants andby
microenvironmental effects such as charge-charge interactions andpost-translational
modifications.
To systematicallyexplore the relationship betweenpi, molecularweight and
protein sequence, adata set ofproteins was collected and organized from amodel
organism. TheEscherichia coliproteome was chosen since it contains fewpost-
translational modifications suchas methylation, acylation, gylcosylation, or
1
phosphorylationwhich canalterthe pI/MW; the presence ofthese modifications makes
pI/MW predictions much more difficult since the modifications in the proteins maycause
themto migrate to aposition on a 2-D gel that is quite differentthan what is predicted
based solely on the amino acid sequence ofthe protein. E. coli is also one ofthe best
characterizedprokaryotes andmuch more databeyondsimplythe protein sequence for
eachprotein is widely available for it.
Atthis point it is necessaryto considerthebasic structural features ofproteins and
the role ofindividual amino acids inthe structure and function ofproteins. Figure 1
below shows the structure ofthe 20 amino acids with side chain structures shown inred
(10).
The charge on all proteins arises from some ofthe amino acid side chains, as well
as the carboxy- and amino-termini, someprosthetic groups, andbound ions. Ourpi
predictiontool (11) is designedto calculate charge based on the side chains and carboxy-
and amino-termini. The charge on amino acid side chains depends onthe pH ofthe
solutionandthepKa ofthe side chains. It is also affectedbythe localized environment
around a side chain. Our current calculationmodel uses the followingpK.A values for
ionizable groups onthe protein and does notmake any adjustments to the pKAvalues of
the side chains regardless oftheir environmentwithinthe protein (Table 1). We also
assume thatthe separation is based onthe total charge on theprotein, notthe mass-to-
charge ratios.
H H H H H
1 1 P 1 P 1 P 1 P
.0
H3N+-aC1 -ceVj H3N+ -ac1 - cXeP H3N+-aC1 -cxe'o H3N+-a1 c-cX'eP H3N+ -aC1 C^XeP
(CH2)3 CH2 CH2 CH2 CH2
11 1 1 h1 w
NH CH2 t^
1 1 KJ y
H
C=NH2 C=0
1 | OH
NH2 NH2 Phenylalanine Tyrosine Tryptophan
(Phe/F) (Tyr/Y) (Trp.W)
Arginine Glutamine
(Arg/R) (Gln/Q)
H H H
H 1 p 1 P 1 P
H3N+ -aC1 - C^/SP H1 /> H3N+ -ac1 c'XeP H3N+-aC1 -Cp"P H3N+ -ttC1 -CX>P
1 xo H3N+ -Mc - ce CH3 / rcH2 CH2
XC
(CH2)4 1 HN 1
,N
| H OH
NH2 Glycine Alanine Histidine Serine
Lysine (Gly/G) (Ala/A) (His/H) (Ser/S)
(Lys/K) H H H H
H2 1 P 1 P 1 P 1 P
C H3N+ -^C-C^e H3N+ -aC - C*e H3N+ -aC - CS H3N+ -aC-Ce
1 XP 1 XP 1 ^P 1 XP
CH3 CH2 H-C-OH CH2
\ / P '
1 1 1 1
H2N+ -aC - Ce CH2 COOH CH3 SH
0
Proline 1
(Pro/P) COOH
GlutamicAcid AsparticAcid Threonine Cysteine
jl
(Glu/E) (Asp/D) (Thr/T) (Cys/C)
1
yP
H3N+ - ac - c -e H H H H
XC
1 1 P 1 P 1 /P 1 p
CH2 HsN+^c-c'e H,N+ -*c - ce H3N+ -"C CS H3N+ -aC-Cve
1 1 XP 1 XP 1 XP 1 ^P
CH2 CH2 CH2 HC-CH3 CH
1 1 1 1
S CH c=o CH2 CfH3 CH3
1 P\ 1 1
CH3 CH3 CH3 NH2 CH3
Methionine Leucine Asparagine Isoleucine Valine
(Met/M) (Leu/L) (Asn/N) (He/1) (Val/V)
Figure 1. Structures ofamino acids with side chains shown in red, carboxylate
groups in green, and amino groups inblue (10).
The charge onthe protein is the sum ofthe charges onthe individual amino acid
side chains. However, the charge on individual amino acid side chains canvarywhen
they are near a group ofnon-polarorhighlycharged side chains. For example the normal
pKa for glutamic acid is about4.1. In lysozyme, two glutamic acid residues are in the
active site. One is in apolar environment andhas anormal pKA value. The other
glutamate side chain is in ahydrophobic environment, where a negative charge is
energeticallyunfavorable. Therefore the pKA value forthis glutamate side chain
increases, whichthen decreases the extent ofthe deprotonation ofthat side chain. This is
veryimportant in the mechanism oflysozyme activity, whichrequires that one ofthe side
chains be charged (deprotonated) andthe otherbe uncharged (protonated) atthe same
time.
In a second example, the serine inthe active sites ofserine proteases has amuch
different acid-basebehaviorthan other serines normallyfound inproteins (9). The
normal pKA value forthe hydroxyl group onthe serine side chain is greaterthan 15,
meaningthatthis group is not found in an ionized state inmostproteins. In serine
proteases, the interaction ofthe active site serine withnearbyhistidine and aspartate side
chains (the so-called catalytic triad) leads to the ionization ofthe serine hydroxyl group.
Meanwhile, the pKAvalue is reduced from about 15 to avalue closerto 7 or 8. This
examplemakes it clearthat the microenvironment ofan individual amino acid side chain
can change it ionizationbehavior.
Other effects on the pKA ofan amino acid side chain canbe seenwhen certain
amino acids arepositionednextto each other. For example, atypical Arginine residue
which is basic will have apKA ofabout 12.5 (Table 1 below) and carrya full +1 charge
inthe physiological pH range. However, when two ofthese basic Arginine residues are
adjacent in aprotein sequence the pKA values will decrease, due to repulsionbetweenthe
twopositive charges. This reduction inpKAvalue, inturn, will cause one orboth ofthe