Introduction
Protein methyltransferases (MTases) are the enzymes,
which are meant for methylation of protein (Boriack-Sjodin and Swinger 2016).
The SET domain MTases catalyze the reaction between a protein substrate and
S-adenosyl-L-methionine (SAM), yielding a methylated protein and S-adenosyl-
L-homocysteine (SAH). S-adenosyl-L-methionine (SAM) acts as an important
cofactor for the transfer of the methyl group to biological molecules like DNA,
RNA and proteins (Petrossian and Clarke 2011).
SET domain MTases are a new family of methyltransferases, which specifically
methylate lysine residues of a large number of different proteins (Yeates
2002). SET domain family was named after the Drosophila genes in which
it was first discovered; Su (var),
Enhancer of zeste, and Trithorax (Jenuwein et
al. 1998). Later on, it was revealed that these genes encode histone lysine
methyltransferases (Rea et al. 2000;
Nishioka et al. 2002). SET domain proteins have now been found in all
eukaryotic organisms. The domain, which is approximately 130 amino acids long,
was characterized in 1998 (Dillon et al.
2005).
At the molecular level, RKM4 involves in the
transcription cofactor activity, histone methyltransferase activity, zinc ion,
and tetra-pyrrole binding, oxidoreductase activity and acts on other
nitrogenous compounds as donors. On the other hand, RKM4 also plays an
important role in many biological processes, which include; Organ development,
negative regulation of cellular macromolecule biosynthetic process and RNA
metabolic process, negative regulation of gene expression, role in histone
methylation, generation of precursor metabolites and energy, establishment of
localization and peptidyl-lysine mono-methylation (Yang and Zhang 2015; Zhang et al. 2017).
Methylation is an extremely
important post-translational modification (PTM) of proteins. Methylation has
been widely studied concerning the “histone code” gene expression regulation (Black
et al. 2012). The recent studies show
that the methylation of protein also has a potential role in the non-histone
proteins (Erce et al. 2012; Low and
Wilkins 2012; Clarke 2013). Dozens of methylation sites have been explored in
Ascomycetes (Plank et al. 2015; Yagoub
et al. 2015) and a hundred and
thousand sites in human through the methylproteome enrichment studies (Bremang et al. 2013; Guo et al. 2014).
The current study is focused on the investigation of
genetic variations in the RKM4 gene
of different strains of S. fimicola, collected
from “Evolution Canyon”, Israel. The “Evolution Canyon”, Israel has two
contrasting slopes: south-facing slope (SFS) and the north-facing slope (NFS).
It presents a microscale environment for the study of genetic variations in
different organisms due to its diverse environmental conditions (Nevo 2012).
Genetic variations are caused by recombination, spontaneous mutations, gene
conversion, and environmental stress. These are key driving factors for
evolution and species adaptation (Hoffmann and Hercus 2000; Saleem et al. 2001). The strains of S. fimicola from the south-facing slope
(SFS) bear a high frequency of mutations and gene conversion than the strains
from the north-facing slope (NFS), (Arif et
al. 2019; Jamil et al. 2019).
Therefore, the SFS strains undergo more genetic variations than the NFS strains
of S. fimicola (Saleem et al. 2001). The fact is that SFS has
xeric, harsh conditions and NFS has mild environmental conditions (Nevo 2012).
So environmental stress is a major cause of genetic variations (Saleem et al. 2001). Genetic variations happening in the DNA ultimately pass into the
proteins and then these effect post-translational modifications of proteins.
The differences in the position of modified sites in the same protein among
different strains of S. fimicola and
in the S. cerevisiae are the
reflections of genetic variations (Arif et
al. 2017a, b). Another purpose of this study is to predict the possible
post-translational modifications, 3D structures, and functions of RKM4 protein.
Very little work has been done on post-translational modifications of the RKM4.
To bridge this knowledge gap, we have used different bioinformatics tools to
investigate post-translational modifications in this study. Although the
bioinformatics tools are reliable, there is a need to study the
post-translational modifications of the RKM4 protein experimentally to
authenticate this study.
Materials and Methods
Sub-culturing of experimental organism
The Molecular
Genetics Laboratory of Department of Botany, University of the Punjab, Lahore,
provided the stock cultures of parental strains of S. fimicola. Originally, these strains were isolated from
“Evolution Canyon”, Israel by Prof. Nevo’s Colleagues. The S1, S2,
S3 strains collected from the south-facing slope (SFS) and the N5,
N6, N7 strains were collected from the north-facing slope
(NFS). The sub-culturing of these strains was carried out on PDA (potato
dextrose agar) media under sterile conditions. The mature fungal growth was
obtained after 9 days by incubating the samples at 20℃ in the
refrigerated incubator.
Extraction
of genomic DNA
Genomic DNA from all parental strains of S. fimicola was extracted by the
modified Spano et al. (1995) method
(without a phenolic wash) followed by resolving DNA fragments by 1% agarose gel
electrophoresis stained with 0.3µl ethidium bromide. 1 kb DNA ladder was used
and the gel was photographed under UV light in the gel documentation system.
Afterward, primers specific to the RKM4
region were designed using the Primer-BLAST tool from the NCBI server (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to
amplify the RKM4 gene in all strains
of S. fimicola.
Touchdown PCR conditions for amplification of RKM4 gene
Touchdown
PCR (TD-PCR) conditions were used for the amplification of genes to study
the possible genetic variations and potential sites for post-translational
modifications of different strains of S.
fimicola. 15 μL
PCR reaction mixture was composed of 2 μL
DNA sample, 1 μL forward primer,
1 μL reverse primer, 10 μL 2X Amp Master Mix, and 1 μL ddH2O. PCR took109 minutes and
40 cycles for the complete amplification of the gene. The time required for
each step of the PCR and other conditions are given in Table 1. The amplified
product was resolved at 1% agarose gel electrophoresis followed by
visualization under UV light in the gel documentation system and the PCR
product was sent to Macrogen Korea for sequencing. Afterward, the sequences
translated into protein sequences by EMBOSS Transeq online server (https://www.ebi.ac.uk/Tools/st/emboss_transeq/Protein).
3D structures prediction and visualization
I-TASSER
was used to predict the 3D structures and functions of RKM4 proteins. The
confidence of each model is quantitatively measured by the C-score value that
is calculated on the base of the significance of threading template alignments
and the convergence parameters of the structure assembly simulations. The
protein structures were visualized in PyMol molecular system. The
ligand-protein interaction predictions were carried out by using BioLip, which
is a ligand-protein binding database.
Tools used for prediction of post-translational modifications
Different
online bioinformatics tools were used for the prediction of post-translational
modifications. The PMes Server (bioinfo.ncu.edu.cn/inquiries_PMeS.aspx)
was used for the prediction of methylation at lysine and arginine residues and
NetPhos 3.1 Server (http://www.cbs.dtu.dk/services/NetPhos/) for the prediction
of phosphorylation at threonine (T), tyrosine (Y) and serine (S) residues. The
PAIL (http://bdmpail.biocuckoo.org/prediction.php) and the NetNES Servers (http://www.cbs.dtu.dk/services/NetNES/) were used for
the prediction of acetylation at arginine (R) residues and nuclear export
signals, respectively.
Results
Multiple sequence alignment
The extracted DNA from different strains (S1, S2, S3, N5,
N6, and N7) of Sordaria fimicola were subjected to
amplification of the RKM4 gene using
touchdown PCR conditions. RKM4
regions with 900 base pairs length were amplified in all studied strains of S. fimicola. After sequencing, the
sequences of the RKM4 gene of
different strains of S. fimicola were
aligned with S. cerevisiae (reference strain) by online clustal omega alignment
tool to observe genetic variations among different strains of S. fimicola.
We obtained 12 different
polymorphic sites in the RKM4 regions
of six strains of S. fimicola with
respect to the S. cerevisiae. Out of 12
polymorphic sites, six non-synonymous substitutions were observed in the RKM4 region. Non-synonymous
substitutions are those substitutions, which change the coding amino acid. At
first polymorphic site in the SFS strains, T was substituted with A at the
second base of a codon, resulting in the change of ATC codon into AAC, which
changed the Isoleucine (I) into asparagine (N). At a second polymorphic site in
the S2, S3 and N5 strains, A was substituted
with G, resulting in the change of codon from GAG to GGG, which changed the
encoding amino acid from glutamate (E) to glycine (G). At the third site in SFS
strains, AT was substituted with CG, changed the codon from GAT to GCG and
changed the encoded amino acid from aspartate (D) to alanine (A). In fourth
polymorphic site in NFS strains, T was replaced with A at first base of the
codon, where TTT is converted into ATT and changed the amino acid from
phenylalanine (F) to isoleucine (I). In SFS strains at fifth polymorphic site,
G was substituted with A at third base of the codon (ATG-ATA), resulted in the
change of methionine (M) into isoleucine (I). At tenth site in the S3 strain,
the substitution of T with A at second base of codon was occurred (TTT-TAT),
which substituted the tyrosine (Y) with phenylalanine (F). Other polymorphic
sites did not change the coding amino acids, hence known as synonymous
substitutions (Fig. 1–2).
Analysis of 3D structures and ligand-protein interactions
Table 1: Touch Down PCR conditions
|
Stage 1 |
Step |
Temperature (°C) |
Time |
|
1 |
Denaturation |
95 |
3 min |
|
2 3 |
Denaturation Annealing |
95 Tm + 10 |
30 s 45 s |
|
4 |
Elongation |
72 |
60 s |
|
Repeat steps 2-4 for 15 times |
|||
|
Stage 2 |
Step |
Temperature (°C) |
Time |
|
5 |
Denaturation |
95 |
30 s |
|
6 |
Annealing |
Tm or (Tm – 5) |
45 s |
|
7 |
Elongation |
72 |
60 s |
|
Repeat 5-7 steps for 25 times |
|||
|
Termination |
Step |
Temperature (°C) |
Time |
|
8 |
Elongation |
72 |
5 min |
|
9 |
Stop reaction |
4 |
15 min |
|
10 |
Hold |
23 |
Until removed from machine |
The 3D cartoon models of RKM4 protein for S. cerevisiae and S. fimicola are shown in Fig. 3. The motifs shown in red color are
α-helix, motifs in yellow color are β-sheets and motifs in green
lines are expressing coils. Both 3D protein structures are different at loop
regions and have a difference in coiling. The ligand-protein interaction is
shown at 3D models of protein with ligand binding site residues for S. cerevisiae and S. fimicola in Fig. 4–5, respectively. S. cerevisiae has three ligands; SAM, Zn+2 and (R,
R)-Butane-2, 3-diol, and each of the ligands has its binding site residues. SAM
has binding site residues; E80, G81, L82, S221, R222, D239, L240, I241, N242,
H243, Y287, Y300, and F302; while the Y287 provides a catalytic binding site.
The binding site residues of Zinc are; C65,
C68, H86, and C90. (R, R)-Butane-2, 3-diol has four binding site residues; Y41,
Y54, C55 and T220 (Fig. 4a–f).
S.
fimicola has two SAM and lysine ligands. SAM binding site residues include; V72, A73, G74, Y75, A222, Y223, D248,
I249, L250, N251, H252, Y285, Y297, and F299; while lysine has six binding site
residues; A222, S224, F225, Q226, Y285, and Y297 (Fig. 5a–d).
Prediction of post-translational modifications
Prediction of phosphorylation: For the RKM4 protein of S. cerevisiae,
phosphorylation was predicted at 26 serine (S), 18 threonine (T) and 7 tyrosine
(Y) residues at different sites in the amino acid sequence. Phosphorylation was
observed at 14 serine (S), 8 threonine (T) and 6 tyrosine (Y) residues of S1
and
S2 strains. Phosphorylation for S3 and N5 strains
was found at 13 serine (S), 7 threonine (T) and 8 tyrosine (Y) residues. For
%20935-942_files/image002.png)
Fig. 1: Multiple sequence alignment of
different strains of S. fimicola with
respect to the S. cerevisiae to
observe genetic diversity among different strains of S. fimicola for RKM4 gene
Keywords:
Symbol (*) showing fully conserved sites, space and highlighted regions showing
polymorphic sites
N6 strain, it was predicted at 13
serine (S), 6 threonine (T) and 7 tyrosine (T) residues. 13 serine (S), 6
threonine (T) and 8 tyrosine (Y) residues of N7 strain
were phosphorylated (Table 2).
Prediction of methylation: In this study, it is reported that only
arginine residues of RKM4 have undergone methylation. Methylation at six arginine residues (R98, R213,
R243, R388, R390, and R445) was investigated in S. cerevisiae.
Only one arginine residue R62 was found to have the potential for methylation
in all studied strains of S. fimicola (Table 3).
Prediction of
acetylation: In S. cerevisiae, acetylation was
investigated at 18 lysine (K) residues. In the S1, S2 and S3 strains of S. fimicola, acetylation was found at
five lysine residues and seven sites in the N5,
N6 and N7 strains (Table 3).
Prediction of
nuclear export signals (NES): We have reported two nuclear export signals at positions 359L, 140L and
three nuclear export signals at positions 60L, 276I, 279I of RKM4 in S. cerevisiae and S1,
S2 and S3 strains, respectively. In N5,
N6 and N7 strains, four NES sites (53L, 60L, 275I, and 278I) were observed. 60 L
residue has been found common for all
strains of S. fimicola and other sites are
present in close proximity with respect to one another. This shows that the 60 L
site is conserved in all strains of S. fimicola (Table 3).
Discussion
To the best
of our knowledge, the RKM4 gene is
first time reported in S. fimicola. In the current study, genetic variations
investigated in the RKM4 gene of S. fimicola. The SFS strains have nine
polymorphic sites and the NFS strains have three polymorphic sites (Fig. 1).
Non-synonymous substitutions were observed at six sites in the RKM4 region, resulted in the change of
coding amino acid (Fig. 2). As SFS have xeric and more stressful conditions,
due to this SFS strains have more genetic variations than NFS strains. This
reveals that environmental stress has a role in the creation of genetic
variations as reported by other geneticists. Arif et al. (2017a) investigated polymorphism
in the S. fimicola with the help of
SSR marker and identified that SFS strains have more variations than NFS
strains. Hosid et al. (2008) reported
high levels of polymorphism with the help of SSR marker in the soil fungus Emericella
nidulans from a stressful environment and low levels of polymorphism in
the fungus collected from an arid environment. Moreover,
genetic variations help the species to better survive in the fluctuating and
stressful environment and are major causes of evolution (Hoffmann and Hercus 2000; Saleem et al. 2001). Environmental conditions encounter organisms with
natural selection by manipulating parental and genetic variants and thus
genetic variations become a requirement for evolution as they determine the
evolutionary potential of a population (Arber 2000).
%20935-942_files/image004.png)
Fig. 2: Multiple sequence alignment of amino
acid sequence of RKM4 protein of different strains of S. fimicola with respect to the reference strain S. cerevisiae amino acid sequence to
spot genetic diversity
Symbol (*)
showing conserved sites, space and highlighted regions showing polymorphic
sites
On the other hand,
genetic variation also has its reflection on post-translational modifications
of proteins as described by Arif et al.
(2017b) in Frequency Clock and Mating Type a-1 proteins of parental strains of S. fimicola from EC Israel. Therefore,
the difference in the positions of modified sites for the same protein in the
different strains of S. fimicola is
the reflection of genetic variations (Table 2 and 3). Genetic variations of
nucleotide sequence ultimately translated into the protein and this produces
the proteins with unique PTM sites, which lead towards the functional diversity
of proteins. Thus, post-translational modifications (PTMs) are very
important because they change the configuration of proteins and this affects
their catalytic functions. Therefore, it is necessary to study them to see how
the PTMs play their role in maintaining the biological functions of proteins
(Lothrop et al. 2013). There are many
types of PTMs of proteins, while the present study mainly focused upon
phosphorylation, methylation, and acetylation of RKM4 methyltransferase of S. cerevisiae and S. fimicola.
Methyltransferases are involved in important biological
processes and methylate specific lysine (Lys) and N-terminal residues of
different subunits of the translational machinery (Porras-Yakushi et al. 2007; Lipson et al. 2010; Hamey et al.
2016). SAM-dependent MTase, RKM4 mono-methylates 60S ribosomal protein L42
(RPL42A and RPL42B) at 'Lys-55' (Webb et
al. 2008; Lipson et al. 2010). A
second SET domain methyltransferase Rkm2 is also identified, which is
responsible for tri-methylating the ribosomal protein L12ab (Rpl12ab) at lysine
10. The second site of lysine methylation for Rpl12ab at position 3 by RKM2 is
identified (Porras-Yakushi et al.
2006; Webb et al. 2008; Gardner et al. 2011).
The RKM4 has many important molecular and biological
functions, which were predicted by I-TASSER in this study (Yang and Zhang 2015;
Zhang et al. 2017). Uslupehlivan et al. (2018) predicted the 3D structure
and functions of the prion protein of sheep (Ovis aries) by I-TASSER. Likewise, Rong et al. (2019) used I-TASSER to predict the membrane-spanning helices and topology models for Δ17 fatty acid
desaturases from Rhizophagus irregularis and Octopus bimaculoides.
Methylation
predominantly found on lysine (Lys) and arginine (Arg) residues in
eukaryotes (Clarke, 2013). In the present
study, methylation was predicted at arginine residues of RKM4 MTase of S. cerevisiae and S. fimicola. The six-arginine residues of RKM4 MTase of S. cerevisiae have the potential for
methylation. Only one site (R62) was
predicted for RKM4 protein, which is common for the SFS and the NFS strains of S. fimicola (Table 3). Winter et al. (2017) observed methylation at
528th lysine residue of the RKM4 protein of S. cerevisiae using mass spectrometry.
Protein
phosphorylation plays an important role in the regulatory and signaling
processes of the cell. This affects up to 30% of the proteome and essential in
the regulation of cellular functions, protein degradation, and stabilization.
In addition, phosphorylation networks are also essential backbones of the
communication system within cells (Manning et
al. 2002; Ficarro et al. 2002). In the present study, a total 51 phosphorylated sites
were investigated for the RKM4 protein of S.
cerevisiae (Table 2). Winter et al.
(2017) have identified 3 serine phosphorylation sites at S24, S420, and S446
positions and one threonine phosphorylation site at 480 amino acid position of
RKM4 MTase of S. cerevisiae. Serine
modification at S420 and threonine modification at T480 was also predicted in
the present study beside other sites in S.
cerevisiae (Table 2).
%20935-942_files/image006.png)
Fig. 3: 3D structure models of RKM4 protein of (a) S.
cerevisiae (b) S. fimicola generated by I-TASSER and
visualized by PyMol. Motifs shown in red color indicate α-helix, yellow
indicate β-sheet and motifs shown in green represent coil structure
%20935-942_files/image008.png)
Fig. 4: Ligand-protein interaction of RKM4 protein of S. cerevisiae. RKM4 protein has three
ligands; SAM and lysine generated from BioLip (ligand-protein binding
database). (a). SAM ligand
attachment with protein. (b). SAM
ligand with its binding site residue, while Y287 is catalytic site residue (c). Zinc ligand attachment with
protein. (d). Lysine ligand with its
binding site residues. (e). (R-R)-Butan-2,3-diol attachment with protein. (f). (R-R)-Butan-2,3-diol with its
binding site residues shown in violet color
In S1, S2,
and S3 strains of S. fimicola,
phosphorylation reported at 28 sites. For N6 and N7 strains of S. fimicola, 26 and 27 sites were predicted, respectively (Table
2). Zhu et al. (2001) identified
4,200 phosphorylation events affecting 1,325 proteins from the 87 yeast protein
kinase assays by the use of proteome chip technology. Bodenmiller et al. (2010) studied two serine
phosphorylation sites at 67 and 69 positions. Winter et al. (2017) studied phosphorylation at two other serine residues
at 129 and 573 positions. Albuquerque et
al. (2008) also reported phosphorylation at one serine residue at 573 amino
acid position experimentally for RKM1 of S.
cerevisiae.
Table 2: Phosphorylation predicted sites with their protein kinases for RKM4
protein of S. cerevisiae and different strains
of S. fimicola. Numbers in third
column are showing the phosphorylation positions on serine, threonine and
tyrosine residues of RKM4. The numbers in the others columns (last four) are
showing the positions, where the specific protein kinase involved in the
phosphorylation of its respective residue i.e. serine, threonine, and tyrosine
|
Organism |
Residues |
Phosphorylation sites |
Protein kinases |
|||
|
CKII |
Unsp |
PKC |
PKA |
|||
|
S.
cerevisiae |
Serine |
5, 24, 59, 60, 63, 67, 75, 86, 118, 137, 158, 172,
187, 189, 197, 198, 208, 270, 337, 420, 425, 429, 446, 450, 485, 486 Total = 26 sites |
5, 189, 197, 198, 207, 208, 337, 450 |
24, 63, 75, 86, 158, 172, 198, 207, 208, 420, 425,
429, 446, 485, 486 |
63, 75, 158, 187, 446, 486 |
59, 60, 137, 429 |
|
Threonine |
17, 44, 52, 65, 84, 171, 207, 232, 240, 303, 306, 308,
396, 401, 431, 457, 472, 480 Total = 18 sites |
17, 84, 171, 308, 431 |
171, 232, 396, 457, 480 |
44, 52, 240, 401, 472 |
- |
|
|
Tyrosine |
217, 241, 261, 278, 286, 338, 341 Total = 7 sites |
- |
217, 261, 286, 341 |
241, 278 |
- |
|
|
S1, S2 |
Serine |
6, 38, 57, 78, 87, 92, 107, 109, 117, 118, 128, 190,
204, 257 Total = 14 sites |
109, 117, 118, 128, 190, 257 |
6, 92, 118, 128, 204 |
78, 107 |
38, 57 |
|
Threonine |
4, 91, 127, 152, 160, 223, 226, 228 Total = 8 sites |
4, 127, 226, 228 |
91, 152 |
160 |
- |
|
|
Tyrosine |
137, 161, 181, 199, 206, 261 Total = 6 sites |
- |
137, 161, 181, 198, 206, 261 |
- |
- |
|
|
S3, N5 |
Serine |
6, 38, 57, 78, 92, 107, 109, 117, 118, 128, 190, 204,
256 Total = 13 sites |
109, 117, 256 |
6, 92, 118, 128, 204 |
78, 107 |
38, 57 |
|
Threonine |
4, 91, 127, 152, 160, 225, 227 Total = 7 sites |
4, 227 |
91, 127, 152 |
160 |
- |
|
|
Tyrosine |
100, 137, 161, 181, 196, 206, 257, 260 Total = 8 sites |
225 |
137, 161, 181, 198, 206, 260 |
- |
- |
|
|
N6 |
Serine |
6, 38, 57, 78, 92, 107, 109, 117, 118, 128, 190, 204,
256 Total = 13 sites |
109, 117, 118, 128 190, 256 |
6, 92, 118, 128, 204, 78 |
78, 107 |
38, 57 |
|
Threonine |
4, 91, 127, 152, 160, 225 Total= 6 sites |
4, 91, 127, 225 |
91, 127, 152 |
160 |
|
|
|
Tyrosine |
100, 137, 181, 196, 206, 257, 260 Total = 7 sites |
- |
137, 161, 181, 198, 206, 260 |
- |
- |
|
|
N7 |
Serine |
6, 38, 57, 78, 92, 107, 109, 117, 118, 128, 190, 204,
256 Total = 13 sites |
109, 117, 118, 128, 190, 256 |
6, 78, 92, 118, 128, 204 |
78, 107 |
38, 57 |
|
Threonine |
4, 91, 127, 152, 160, 225 Total = 6 sites |
4, 91, 127, 225 |
91, 127, 152 |
160 |
- |
|
|
Tyrosine |
100,137, 161, 181, 198, 206, 257, 260 Total = 8 sites |
- |
137, 161, 181, 198, 206, 260 |
- |
- |
|
Table 3: Table showing predicted methylation, acetylation and nuclear export
signals (NES) sites for RKM4 protein of S. cerevisiae and all studied strains of S. fimicola. Numbers are showing
methylation positions on arginine (R), acetylation positions on lysine (K) and
NES positions on leucine (L) and isoleucine (I)
|
Organism |
Residue |
Methylation
sites |
Residue |
Acetylation
sites |
NES sites |
NES potential |
|
S.
cerevisiae |
Arginine (R) |
98, 213, 243, 388, 390, 445 Total = 6 sites |
Lysine (K) |
30, 46, 49, 77, 146, 149, 219, 234, 247, 368, 403,
423, 426, 445, 484,488, 493, 494 Total = 18 sites |
359-L |
0.575 |
|
140-L |
0.529 |
|||||
|
S1, S2, S3 |
Arginine (R) |
62 Total = 1 site |
Lysine (K) |
66, 69, 139, 154, 167 Total = 5 sites |
60-L |
0.624 |
|
276-I |
0.507 |
|||||
|
279-I |
0.562 |
|||||
|
N5, N6, N7 |
Arginine (R) |
62 Total = 1 site |
Lysine (K) |
66, 69, 139, 154, 167, 220, 287 Total = 7 sites |
53-L |
0.514 |
|
60-L |
0.646 |
|||||
|
275-I |
0.505 |
|||||
|
278-I |
0.561 |
Symbols L=Leucine and I=Isoleucine
Protein kinases are the most
crucial enzymes, involve in protein phosphorylation. They transfer a phosphate
group from ATP to the protein substrate and phosphorylate the protein. They
constitute about 2% of eukaryotes genome and phosphorylate about 30% cellular
proteins (Ubersax and Ferrell 2007). CKII, Unsp, PKC, and PKA are some important protein
kinases, which are potentially involved in phosphorylation at different
residues of RKM4 protein of S. fimicola and S.
cerevisiae (Table 2). Arif et al.
(2019) reported four protein kinases (PKC, Unsp, PKA, cdc2), which are involved in the phosphorylation of
Cytochrome c oxidase (COX1).
%20935-942_files/image010.png)
Fig. 5: Ligand-protein
interaction of RKM4 protein of S.
fimicola. RKM4 protein has two ligands; SAM and lysine generated from
BioLip (ligand-protein binding database). (a). SAM ligand attachment with
protein. (b). SAM ligand with its binding site residues, while H252 is
catalytic site residue (c). Lysine ligand attachment with protein. (d). Lysine
ligand with its binding site residues
Lysine acetylation is a reversible post-translational modification of
proteins and plays a key role in regulating gene expression (Choudhary et al. 2009). Protein lysine acetylation has emerged as a key post-translational
modification in cellular regulation, particularly through the modification of
histones and nuclear transcription regulators (Zhao et al. 2010). For the RKM4 protein of S. cerevisiae, acetylation predicted at 18
lysine (K) residues. In the S1, S2 and
S3 strains of S.
fimicola, acetylation observed at five lysine residues and
in the N5, N6, and N7 strains,
seven acetylation sites were reported (Table 3). The difference in the
acetylation sites for RKM4 protein lies between the strains of two contrasting
slopes of EC. There was no difference in between the strains of each slope. Winter et
al. (2017) studied the two lysine sites at 403 and 488 positions in the
RKM4 protein of S. cerevisiae and
these sites are also identified in all strains of S. fimicola. This shows the conservation of both sites
and these sites might be involved in the regulation of RKM4 protein.
Nuclear export signals (NESs) play an extremely
important role in the regulation of the subcellular location of proteins. Other
nuclear processes and transcription are affected by this regulation. These
processes are very important in maintaining the viability of the cell. The most
important properties of NESs are accessibility and flexibility allowing
relevant proteins to interact with the signal (Cour et al. 2004). For the RKM4 protein of S. cerevisiae, two positions; 140 L and 359 L have been predicted.
Three positions (60-L, 276-I, 79-I) of nuclear export signals for S1,
S2, and S3, four positions (53-L, 60-L, 275-I, 278-I) for
N5, N6 and N7 have been predicted. One
position 60-L is common for all studied strains of S. fimicola (Table 3). The presence of these nuclear export signals
in the RKM4 protein of S. cerevisiae
as well as in different strains of S.
fimicola indicates that this protein is primarily regulated through these
nuclear export signals. Arif et al.
(2017a) predicted nuclear export signals in the frequency clock protein of S. fimicola at 328th amino
acid residue and in Neurospora crassa
at 323rd amino acid residue. Furthermore, some recent studies have
been carried out by Jamil et al.
(2018) and Arif et al. (2019) on the
post-translational modifications of H3/H4 histones and cytochrome c oxidase
(COX1) of S. fimicola, respectively
by using different bioinformatics tools, also used in this study. These studies
and current study will help to bridge the knowledge gap related to the
post-translational modifications of proteins in S. fimicola.
Conclusion
It is concluded that SFS
strains have more genetic variations
than NFS strains because SFS strains have stressful environmental conditions.
These genetic variations also have their reflections upon post-translational
modifications of proteins. Therefore, in this study different
post-translational modified sites are reported for RKM4 methyltransferase of S. fimicola. RKM4 has an important role
in molecular and biological processes, which are predicted by I-TASSER.
However, experimental studies need to authenticate these functions and to
clarify the unknown functions of phosphorylation, methylation, and acetylation.
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