Abstract
A considerable proportion of protein-protein interactions (PPIs) in the
cell are estimated to be mediated by very short peptide segments that
approximately conform to specific sequence patterns known as linear
motifs (LMs), often present in the disordered regions in the eukaryotic
proteins. These peptides have been found to interact with low affinity
and are able bind to multiple interactors, thus playing an important
role in the PPI networks involving date hubs. In this work, PPI data
and de novo motif identification based method (MEME) were used to
identify such peptides in three cancer-associated hub proteins—MYC, APC
and MDM2. The peptides corresponding to the significant LMs identified
for each hub protein were aligned, the overlapping regions across these
peptides being termed as overlapping linear peptides (OLPs). These OLPs
were thus predicted to be responsible for multiple PPIs of the
corresponding hub proteins and a scoring system was developed to rank
them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored
higher than OLP predictions from randomly generated protein sets. Two
OLP sequences from the C-terminal of MYC were predicted to bind with
FBXW7, component of an E3 ubiquitin-protein ligase complex involved in
proteasomal degradation of MYC. Similarly, we identified peptides in
the C-terminal of MDM2 interacting with FKBP3, which has a specific
role in auto-ubiquitinylation of MDM2. The peptide sequences predicted
in MYC and MDM2 look promising for designing orthosteric inhibitors
against possible disease-associated PPIs. Since these OLPs can interact
with other proteins as well, these inhibitors should be specific to the
targeted interactor to prevent undesired side-effects. This
computational framework has been designed to predict and rank the
peptide regions that may mediate multiple PPIs and can be applied to
other disease-associated date hub proteins for prediction of novel
therapeutic targets of small molecule PPI modulators.
Introduction
There has been a gradual shift of focus in cancer research from the
study of individual proteins to edgetic perturbations of highly
connected nodes (proteins) in intra-cellular signaling networks, known
as hub nodes, which are considered essential for maintaining the
network topology [[30]1–[31]3]. Hubs that directly interact with most
or all of their partners simultaneously are called 'party' hubs
(multi-interface hubs), whereas those that bind different partners at
different times or locations are known as 'date' hubs
(singlish-interface hubs) [[32]4]. A growing number of protein-protein
interactions (PPIs) are now known to be mediated by short linear
peptides, where a globular protein or domain binds to short peptide
segments in multiple partners, generally located in the intrinsically
disordered regions [[33]5,[34]6]. Such peptides may sometimes be
present in ordered segments also, e.g. the p53 peptide that binds to
MDM2 occurs in ordered helical region [[35]7]. These peptide segments
may occur in different regions of the interacting proteins, but
sequence analysis often reveals an underlying consensus pattern or
linear motif (LM) that captures the key structural and physicochemical
features of the regions [[36]8]. The small peptides have been shown to
mimic the protein-protein interactions and may thus be useful in
extracting interacting partners in experimental procedures like
affinity purification [[37]9]. The transient and low-affinity PPIs
mediated by these short, flexible peptide segments help many date hub
proteins to employ the same interfaces for binding multiple interactors
at different time or locations [[38]10,[39]11]. Furthermore, mutations
in such peptide sequences of signaling hub proteins may affect entire
PPI networks and signaling cascades [[40]12]. Recent studies have shown
that small chemical inhibitors can target PPIs, including the ones
mediated by short peptides, and have the potential to act as new
therapeutic agents against complex diseases including cancer [[41]13].
Therefore, identification of such short peptides that may mediate
multiple protein interactions in essential cancer-associated hub
proteins can help in targeting peptide-mediated PPIs for therapeutic
intervention with structural analogues.
The goal of the present study is to develop a computational framework
for predicting peptide sequences in cancer-associated hub proteins
(CPs) that may bind to multiple interactors, using experimentally
verified PPI datasets and a network-based approach. In a protein
interaction network, where the nodes represent the proteins and the
edges their mutual interactions, most of the nodes are not directly
connected to one another, but any of the nodes can be reached from any
other node in the network through a small number of hops or edges. The
first hop protein interactors or FHPIs (the yellow rectangles marked as
P1, P2… P5 in [42]Fig 1) are the ones directly connected to CP (the
pink oval central node) by edges (black arrows). The second hop protein
interactors or SHPIs are those that are connected to the CP through the
FHPIs (the green rhomboids viz. P1-1, P1-2 & P1-3 through P1; P2-1,
P2-2 & P2-3 through P2 etc in [43]Fig 1) [[44]14]. We have chosen three
well-known cancer-associated human hub proteins viz. MYC, APC and MDM2,
each known to be linked to a large number of FHPIs and a
proportionately larger number of SHPIs. The interaction networks of
these three proteins were reconstructed up to the second hop level by
gathering the list of FHPIs interacting with each of the CPs, followed
by the list of SHPIs interacting with each of the FHPIs.
Fig 1. CP represents a multifunctional cancer-associated hub protein and P1,
P2, P3, P4 & P5 are its direct interactors (First Hop Protein Interactors or
FHPIs of CP).
[45]Fig 1
[46]Open in a new tab
P1-1, P1-2 & P1-3 are the interactors of P1 (Second Hop Protein
Interactors or SHPIs of CP), P2-1, P2-2 & P2-3 are the interactors of
P2 and so on. Red-borders have been used to mark the oncoproteins.
Sequence analysis (using MEME for de-novo motif identification) of all
the interactors of a particular FHPI (e.g CP, P1-1, P1-2, and P1-3 for
P1) may reveal some shared sequence patterns (e.g. m1 & m2 among
interactors of P1, m1, m2, m3 & m4 among interactors of P2 etc).
Alignment of the peptide sequences from CP corresponding to all such
motifs (p1 from m1, p2 from m2 etc) may then identify a common peptide
(OLP) from the overlapping sequence positions. This OLP may play a key
role in mediating interactions with multiple FHPIs and therefore help
in designing orthosteric inhibitors that can be targeted to block any
of the CP-FHPI interactions by making it specific to the binding site
of CP on a particular FHPI (e.g. P1).
MEME (Multiple Em for Motif Elicitation) [[47]15,[48]16] is a very
popular and widely used tool for searching ungapped sequence patterns
repeated across a set of fasta sequences. The amino-acid sequences of
all the interactors of an FHPI (i.e., SHPIs as well as the CP itself),
were submitted to MEME for identification of the over-represented
sequence patterns (LMs) present in all or most of these proteins, which
may mediate interactions with the FHPI. We hypothesized that since the
FHPI is a common interactor for the set of corresponding SHPIs and the
CP, some of these sequences may share a motif denoting the peptide
regions interacting with the FHPI. The MEME analysis for motif
identification was repeated for every FHPI of a CP independently, to
compile a list of motifs, each of which was predicted to interact with
a particular FHPI of a CP. After compiling the list of FHPI-specific
MEME-predicted motifs, we focused on the peptide segments in CP that
correspond to these patterns in the MEME results. Our aim is to align
these peptides and reveal the overlapping sequence positions among
them, which may be predicted as the peptide interfaces that may
interact with multiple FHPIs [[49]17,[50]18]. These overlaps have been
denoted henceforth as Overlapping Linear Peptides or OLPs.
Our proposed workflow is an attempt to provide a coarse-grained
prediction of the regions within singlish-interface hubs that may
mediate multiple PPIs, using existing in-silico methods for motif
elucidation, thereby facilitating further experimental studies on them.
MEME was chosen for the motif identification step because it does not
adjust for evolutionary relationships, (unlike DILIMOT[[51]19],
SLiMDisc[[52]20], SLiMFinder[[53]21] and QSLiMFinder[[54]22]) therefore
accounting for sequence patterns responsible for PPIs in evolutionarily
related proteins [[55]23]. A scoring system was also formulated to rank
the predicted OLPs according to a new metric designated as OLP score,
normalized across all three CPs by comparing with the median OLP score
from randomly generated sets of protein sequences. For validating the
proposed workflow, we repeated the procedure with the interaction
network of human GASP2, which involves at least three experimentally
verified examples of a single peptide mediating multiple PPIs [[56]24].
We also made an attempt to evaluate the predicted OLP-mediated FHPI
interactions through the PepSite2 [[57]25] web server, which predicted
that several OLPs from MYC and MDM2 can bind to multiple FHPIs.
Furthermore, we also performed a BLAST search with the predicted OLP
sequences to find similar peptide sequences in human proteins not
present in the SHPI networks used in our study, to enable prediction of
novel PPIs.
Materials and Methods
Overview of the proposed workflow
The first task is to recreate the PPI network of the hub protein (CP)
up to the second hop level by compiling the list of proteins known to
interact with the CP (FHPI network) and then the proteins interacting
with each of the FHPIs (SHPI network). In [58]Fig 1, the nodes in the
FHPI network have been shown as yellow rectangles and the nodes in the
SHPI network as green rhomboids. In the next step, interactors of each
FHPI (including the CP) were scanned for shared sequence patterns using
MEME. For example, let us suppose protein P1 is known to directly
interact with CP, thus being the FHPI of CP. P1 is also known to
interact with three other proteins P1-1, P1-2 & P1-3, which would be
SHPIs of CP. The sequences of CP, P1-1, P1-2 and P1-3 are submitted
together to MEME for finding shared sequence patterns among them and
two such patterns m1 and m2 are found. Here we have hypothesized that
since the four proteins have a common interactor (i.e. P1), the motifs
shared by them may mediate their interactions with P1. The same process
is repeated with P2, the next FHPI, and MEME analysis of its
interactors i.e., CP, P2-1, P2-2, and P2-3, show the shared sequence
patterns m1, m3, m4 and m5. Hence, these motifs may be predicted to
mediate interactions with P2. Similarly, other motifs are predicted for
each of the remaining FHPIs, P3, P4 and P5. The peptide sequences of CP
that correspond to the motifs (say p1 corresponds to m1, p2 to m2 etc)
are then compared to see if some of them overlap. If such overlaps are
found, then these overlapping positions may be hypothesized to mediate
interactions with multiple FHPIs (e.g. p1 may be predicted to interact
with P1, P2 and P3).
Protein-Protein Interaction Dataset
Three cancer-associated hub proteins- MYC, APC and MDM2, were used in
the study for identifying LMs, and were found to be associated with
721, 95, 177 FHPIs and 4850, 1000, 3047 SHPIs respectively, according
to the IntAct database [[59]26] (Table A in [60]S1 File). Only
experimentally verified human protein-protein interactions were
considered in this study.
Amino acid sequences of proteins
The amino acid sequences of the CPs (MYC, APC and MDM2) and the other
SHPIs were extracted from the UniProt database [[61]27] in fasta
format.
Motif identification
The protein sequences of the direct interactors of each FHPI i.e. the
CP and other SHPIs, (e.g CP, P1-1, P1-2, and P1-3 for P1), were used
for de novo motif identification by MEME. The E-value in the MEME
output was used to infer the statistical significance of each of the
reported sequence patterns or LMs, whereas, the p-value was used to
determine the extent of matching of individual peptide instances to the
corresponding LM. The statistically significant (E-value<1.0) motifs
observed in the CP as well as in other SHPIs, were selected for further
analysis. The FHPIs with 5–20 interactors were only used in this study
because with increase of sequence variability across multiple
interactors, it becomes difficult to identify conserved regions among
them using de-novo motif identification. An E-value cut-off of 1.00 was
chosen for a higher sensitivity at the cost of lower specificity
[[62]28]. The standalone version of MEME was used with parameters set
to 'zoops' (zero or one per sequence) for distribution of motifs, 6 as
minimum and 50 as maximum motif width, and 10 as maximum number of
motifs to be reported.
Multiple Sequence Alignment of Motifs
The peptide sequences in the CP corresponding to the significant motifs
identified from multiple MEME runs were aligned using Clustal Omega
[[63]29], followed by manual interpretation of the alignments to find
possible overlaps among them, thereby reducing the probability of
reporting false positives.
OLP Score
The short overlapped peptide sequences identified in each of the CPs
(MYC, APC and MDM2) were ranked according to the OLP score^observed
computed as:
[MATH: OLP scoreobserved=lo
g(NFP/(TFP*H
M))
:MATH]
Where NFP and TFP represent the number of FHPIs interacting with an OLP
and the total number of FHPIs of a CP respectively, whereas HM denotes
the Harmonic Mean of the p-values reported by MEME for all the longer
peptides having the OLP.
Generation of OLP scores from random PPI networks
Twenty-five decoy protein interaction networks were generated by
grouping random numbers of protein sequences chosen randomly from a
dataset comprising of the fasta sequences of the entire human proteome
set from UniProt [[64]27]. OLP scores were generated for these 25 sets
treating them as 25 FHPI networks and the distribution of these scores
were plotted using R scripts. This procedure was repeated four times,
creating 25 X 4 = 100 decoy FHPI networks, and the four separately
plotted distributions showed median values of 15.42, 20.03, 17.4 and
18.25 respectively (Fig A (i), (ii), (iii) & (iv) in [65]S1 File). The
average of the median values from the four distributions was 17.78 with
a standard deviation of 1.91. Hence we assumed OLP score^random =
17.78. The OLP score^observed was normalized by dividing with the OLP
score^random giving the OLP score^normalized:
[MATH: OLPscorenormalized=OLPscoreobserved/OLPscorerandom :MATH]
Validation using multiple orthogonal approaches
i) Structural bioinformatics
The PepSite2 [[66]25] server was used to analyze the interactions
between each of the OLPs with the respective FHPIs. Pepsite2 predicts
the binding site for peptides on protein surfaces using spatial
position specific scoring matrices (S-PSSMs) calculated from known 3D
structures of protein-peptide complexes derived from PDB. Users need to
provide the query peptide sequence in standard one-letter amino acid
codes, as well as the structure of the peptide-binding protein in the
PDB format. Since the structures of some FHPIs used in this study were
not available in the PDB database, their modeled structures were either
obtained from ModBase[[67]30] (if available), or modeled using the
SWISS-MODEL [[68]31] homology-modeling server. The details of PDB
structures chosen as templates for modeling FHPI structures were
reported in Table E in [69]S1 File.
ii) Functional grouping analyses
The gene ontology enrichment and pathway analyses (Reactome and KEGG)
of the CP and the FHPIs interacting with each of the identified OLPs
were performed using ClueGO [[70]32] plug-in of Cytoscape (k-value set
at 0.3) and FuncAssociate 2 [[71]33].
iii) Other analyses
Disordered region of the CP were searched in DisProt [[72]34], IUpred
[[73]35] and ESpritz [[74]36] and the locations of the identified OLPs
were mapped accordingly. In addition, the protein sequences of the CPs
(MYC, APC and MDM2) were scanned using the ELM [[75]37] resource and
searched in the LMPID [[76]38] database, to look for already reported
linear motif instances that coincide with those identified in this
study. Surface accessibility of the OLP sequences was verified by
submitting the CP sequences to SCRATCH prediction server [[77]39].
iv) Validation of the proposed workflow using known example of peptide
mediating multiple PPIs
GASP2_HUMAN ([78]Q96D09) was assumed to be a CP and three human
proteins- ADRB1, ACM1 and CALCR, known to bind to the same peptide of
GASP2, were assumed to be the FHPIs, in this study. The human proteins
interacting with each of these three FHPIs were listed from the IntAct
database [[79]26], which form the SHPI sets. The fasta sequences of the
SHPIs for each FHPI were submitted to MEME along with the sequence of
the CP (i.e. GASP2_HUMAN). The significant motifs reported by MEME were
checked for peptides from GASP2 and all such peptides were aligned to
search for overlapping positions.
BLAST homology search with predicted OLPs
The OLP sequences were searched against the human protein sequences in
SwissProt database using protein BLAST, to identify possible sequence
matches that may indicate possible interactions between these proteins
and the respective FHPIs.
Results
OLPs found in MYC protein
MYC (439 aa) is a multifunctional transcriptional factor and an
important signaling hub. A mutated form of c-MYC is found in many human
cancers, including lung cancer, breast cancer, cervical cancer, ovarian
cancer, and in colon and colorectal cancer, where Myc is constitutively
expressed [[80]40,[81]41]. In MEME runs, only 292 FHPIs with degree in
between 5–20 (Fig B in [82]S1 File) were used, and 34 significant
motifs were reported in MYC by MEME outputs (Table B in [83]S1 File),
from which 8 OLPs were identified, as shown in [84]Fig 2 (A) and
[85]Table 1. The normalized OLP scores of 6 out of the 8 OLPs were
greater than 1 (indicating these scores were better than OLP scores for
random PPI networks). For example, the C-terminal OLPs of MYC,
^371KRSFFALRD^379 and ^392KVVILKKATAY^402, were shown to be involved in
NOTCH1 transcriptional process (Reactome) and cellular response to UV
(enriched GO BP). These two peptides were also predicted to interact
with FBXW7 that forms a part of the E3 ubiquitin-protein ligase
complex, and thus can mediate proteasomal degradation of MYC. However
only the sequence ^371KRSFFALRD^379 was predicted to lie in the
disordered region and be partly surface accessible (Fig I (i) in [86]S1
File), and thus may be expected to mediate multiple PPIs. PepSite2 also
predicted the binding of this peptide to two FHPIs—CNOT4 and FBXW7,
with reasonably significant p-values (Table F and Fig H1-2 in [87]S1
File). Similarly, the N-terminal OLMs, ^10RNYDLDYD^17 and
^23FYCDEEEN^30, were predicted to have a role in E-box binding based on
enriched GO molecular functions terms. Both these peptides lie in
disordered regions, are surface accessible (Fig I (i) in [88]S1 File),
and were predicted by the ELM resource to contain motif instances of
the 'LIG' category. Another OLP sequence of MYC, ^114SFICDPDD^121, with
a normalized OLP Score of 1.76, predicted to lie in disordered region
and be surface accessible (Fig I (i) in [89]S1 File), has been shown in
[90]Fig 2 (B). This OLP may interact with five FHPIs viz. EXOC1, ILVBL,
PFDN5, NCAPG2 and MRPL14. However, no common functional annotations (GO
terms or pathways) were found to be associated with these five
proteins. This may indicate that these FHPIs could interact with the
same OLP of MYC but at different time or locations.
Fig 2.
[91]Fig 2
[92]Open in a new tab
(A) Eight OLP sequences (each marked by a different background colour)
were identified by Multiple Sequence Alignment of the peptide sequences
in MYC corresponding to all significant motifs (E-value<1.0) inferred
by MEME from its SHPI network. (B) Diagrammatic representation of an
OLP sequence ^114SFICDPDD^121 (marked with orange background)
identified in MYC, which may interact with five FHPIs. The nodes
representing oncoproteins have been marked with a red border.
Table 1. Overlapping Linear Motifs (OLPs) found in the cancer-associated hub
proteins (CPs) MYC, APC and MDM2.
Name of the CP OLP sequence OLP score Name of the FHPIs SecStr[93]^*
Surface Accessible Enriched GO terms / Pathways ELM prediction
MYC 371-KRSFFALRD-379 2.95 CNOT4, FBXW7 D Partly NOTCH1 Intracellular
DomainRegulates Transcription (REACTOME);Cellular response to UV
(GO_BP) _
MYC 114-SFICDPDD-121 1.76 EXOC1, ILVBL, PFDN5,NCAPG2, MRPL14 D Yes - _
MYC 128-IIIQDCMW-135 1.76 EXOC1, RAB11FIP5, BPTF, ILVBL, PFDN5, MSH3,
MRPL14, NUP188, ZCCHC11, KPNA4 D No - _
MYC 32-YQQQQQSELQ-41 0.92 KIF20B, KALRN D Yes - LIG_SH2_STAT3
MYC 392-KVVILKKATAY-402 2.95 FBXW7, TCF12 H No NOTCH1 Intracellular
Domain Regulates Transcription (REACTOME); Cellular response to UV
(GO_BP); E-box binding (GO_MF) _
MYC 23-FYCDEEEN-30 1.47 MSH3, GIGYF2, FASTKD2, NFIL3, TCF12 D Yes E-box
binding (GO_MF) LIG_SH2_STAT5
MYC 10-RNYDLDYD-17 1.47 FASTKD2, IL4R, TCF12 D Partly E-box binding
(GO_MF) LIG_TYR_ITIM
MYC 299-RCHVSTHQHNY-309 0.56 NCAPG2, MYO1B D Yes - LIG_14-3-3_3
APC 416-YCETCWEW-423 0.76 GIGYF2, EPAS1, ANKRD17 H No Anatomical
structure homeostasis (GO_BP) LIG_SH2_STAT5
APC 422-EWQEAH-427 0.76 NCKAP5, ANKRD17 H Yes - _
APC 155-KDWYYA-160 0.35 CYTH2, GIGYF2 H Yes - _
MDM2 456-GHLMACF-462 1.60 RNF8, GLTSCR2, TSNAX, FKBP3 H No - _
MDM2 463-TCAKKLKKRNKPC-475 1.60 RNF8, HLA-DMB, FKBP3, JUND H Yes - _
MDM2 475-CPVCR-478 0.88 PHF7, HLA-DMB, JUND C Yes - _
MDM2 305-CTSCN-309 1.95 HRSP12, MAP4K4, PIM2, ARHGEF6, NEFM, ZNF326,
TSNAX C Yes - _
MDM2 311-MNPPLPSHC-319 1.95 MAP4K4, PIM1, PIM2, ARHGEF6, YY1AP1, NEFM,
ZNF326, TSNAX, USP2 C Partly Positive regulation of cell cycle phase
transition (GO_BP);Positive regulation of mitotic cell cycle phase
transition (GO_BP); Positive regulation of G1/S transition of mitotic
cell cycle (GO_BP); Positive regulation of mitotic cell cycle (GO_BP)
LIG_SH3_3 LIG_WW_2DOC_USP7_1
MDM2 438-CVICQ-442 1.60 PHF7, TSNAX, FKBP3 E Yes - _
[94]Open in a new tab
Each row represents the name of the hub protein, the sequence of the
OLP, the OLP score^normalized, name of FHPIs that may interact with the
OLP, secondary structure of the OLP, whether the OLP is surface
accessible, enriched GO terms & pathways among the FHPIs and the CP,
and presence of the OLP in the ELM resource.
*Secondary Structure: H-Helical, E-Sheet, C-Coil, D-Disordered.
OLPs found in APC protein
Adenomatous polyposis coli (APC) protein (2843 aa) is a large
multi-domain protein encoded by the tumor suppressor APC gene, and is
involved in the Wnt signaling pathway that plays an integral role in
cell adhesion and proliferation in cancer [[95]42]. There were 95 FHPIs
and 1000 SHPIs of APC as reported in IntAct [[96]25], out of which
there were 40 FHPIs (degree in between 5–20) used in our analysis (Fig
C in [97]S1 File). Only 8 significant motifs were reported by MEME runs
(Table C in [98]S1 File), out of which 3 OLPs could be identified, as
shown in Fig E in [99]S1 File and [100]Table 1. These 3 OLPs were all
located within the Armadillo-like helical domain (ordered region), and
none of them scored higher than the median OLP score from random PPI
networks. Hence, we could not consider these OLPs as peptides that may
mediate multiple PPIs, according to our framework. This is also
reflected in the PepSite2 docking studies where none of the OLPs from
APC showed appreciable binding to the respective FHPIs (Table F and Fig
H28-34 in [101]S1 File). It is quite possible that due to its increased
length as compared to the other two CPs considered in this study, APC
may not need to employ single peptide interfaces for multiple PPIs.
OLPs found in MDM2 protein
MDM2 (491 aa) is the E3 ubiquitin-protein ligase that mediates
ubiquitination of the p53 tumour suppressor [[102]43]. There are 177
FHPIs and 3047 SHPIs of MDM2 as reported in IntAct [[103]26] database,
out of which 68 FHPIs (degree in between 5–20) were used in our study
(Fig D in [104]S1 File). MEME runs predicted 18 significant motifs
(Table D in [105]S1 File), from which 6 OLPs were identified, out of
which 5 OLPs have higher score than the random OLP score (Fig F in
[106]S1 File and [107]Table 1). We have predicted three peptides in the
region from 438–475 of MDM2 (^438CVICQ^442, ^456GHLMACF^462 and
^463TCAKKLKKRNKPC^475) that can bind to FKBP3 (also known as FKBP25),
which regulates the p53-MDM2 pathway. PepSite2 also predicts the
binding of FKBP3 to both the OLPs ^438CVICQ^442 (Fig H60 in [108]S1
File) and ^456GHLMACF^462 (Fig H37 in [109]S1 File) as highly
significant and to ^463TCAKKLKKRNKPC^475 (Fig H40 in [110]S1 File) as
moderately significant (Table F in [111]S1 File). However, only
^438CVICQ^442 and ^463TCAKKLKKRNKPC^475 were predicted to be surface
accessible (Fig I (iii) in [112]S1 File). The C-terminal OLP
^311MNPPLPSHC^319 may interact with nine FHPIs, which are associated
with positive regulation of cell cycle and regulation of protein
stability. In addition, this OLP comprises of two ELM predicted motif
instances– ^311MNPPLP^316 (LIG_SH3_3) and ^313PPLP^316 (LIG_WW_2).
PepSite2 predicts the binding of this OLM with six FHPIs, out of which
for three FHPIs the binding is highly significant. Interestingly, the
p-value for binding of this SH3 ligand motif-containing peptide to the
SH3 domain-containing protein ARHGEF6 is found to be extremely low
(9.897e-05), the lowest among all peptide-FHPI interactions evaluated
in PepSite2 (Fig H54 in [113]S1 File).
OLPs found in GASP2 protein
GASP2_HUMAN ([114]Q96D09) sequence contains the peptide
^444EEEAIFGSWFWDRDE^458 that has been shown to bind to human beta-1
adrenergic (ADRB1), muscarinic acetylcholine (ACM1) and calcitonin
(CALCR) receptors [[115]24]. The sequences of all interactors of the
FHPIs- ADRB1_HUMAN ([116]P08588), ACM1_HUMAN ([117]P11229), and
CALCR_HUMAN ([118]P30988), were analyzed using MEME. In all the three
cases, peptides from approximately the same region of GASP2 were
reported, which when aligned were found to contain the OLP
^452WFWDRDEACFDLNPCPVY^469 (Fig G in [119]S1 File). Thus, we found that
our method could provide a very near approximation of an actual
experimentally validated motif instance that can bind to multiple
proteins. The normalized OLPscore of this peptide was 1.47.
Sequence matches observed by BLAST search
The results of BLAST homology search of the OLP sequences show several
human proteins (neither of which was included in the SHPI networks used
for MEME analysis) contain peptide sequences similar to the OLPs (Table
G in [120]S1 File). For example, the membrane-associated human protein
OGFRL1 contains a peptide ^90KRSFYAARD^98 that is very similar to the
MYC peptide ^371KRSFFALRD^379. These proteins may be investigated
further for possible interactions with the FHPIs that have been
predicted by our workflow to interact with the matching OLPs.
Discussions
Identification of peptide regions in signaling hubs mediating
disease-associated PPIs can be highly useful for edgetic perturbations
on molecular regulatory networks using small-molecule inhibitors
[[121]44,[122]45]. The smaller contact area seen in peptide-mediated
PPIs, as compared to those mediated by large globular domains, offers a
better possibility of targeting such interfaces with small chemical
modulators for therapeutic intervention [[123]5]. However, if the same
peptide interface is involved in multiple PPIs, targeting one of the
PPIs with orthosteric inhibitors may also affect other PPIs at the same
site, leading to possible disruptions of essential PPI networks and
pathways. Thus, it would be useful to predict the peptides capable of
binding multiple interactors and study each of the PPIs separately,
before targeting any one of these for therapeutic purposes. This would
facilitate the design of safer and more specific PPI modulators in
future.
In our proposed workflow, we have therefore taken a novel approach of
aligning the peptides predicted to interact with different proteins to
identify overlaps among them, so that these overlaps may represent
sites interacting with multiple proteins. Hence, our framework deviates
from the existing motif identification protocols, which only predict
the linear peptide sequences capable of binding specific interactors,
whereas we have tried to further predict whether these peptides might
bind to multiple interactors. In this study, we have used MEME software
to independently predict long peptide regions that may interact with
each of the interactors of a CP, which were then aligned to reveal
shorter peptides possibly interacting with multiple interactors. Here,
we have also assumed that if a peptide is identified multiple times in
the network-based approach, then the probability of that peptide to be
involved in mediating PPIs will be higher. Thus, even though we have
chosen a lenient E-value cut-off (<1.0) for the initial longer
peptides, it is less likely that many of these longer peptide segments
would share a shorter overlapping region completely by chance. We have
used orthogonal methods to validate our findings. PepSite2 was used to
structurally validate the peptide-mediated PPIs predicted by our
method, whereas, GO and pathway enrichment analysis was done on the set
of FHPIs predicted to interact with each OLP. However, the reliability
of predictions from the PepSite2 server may have suffered due of the
use of modeled structures in absence of experimentally determined 3D
structures of the FHPI proteins. We have also searched for the presence
of ELM-predicted motifs within the OLP sequences, and probed whether
these OLPs fall in disordered and surface exposed regions of the
corresponding proteins.
Few predicted high scoring OLPs from our study look promising for
mutational studies affecting the underlying protein-protein interaction
network and subsequent experimental validation. Two OLP sequences from
C-terminal of MYC (^371KRSFFALRD^379 and ^392KVVILKKATAY^402) with high
OLP scores have been predicted to bind with FBXW7. This protein was
identified by Koch et al [[124]46] in a large scale study of C-MYC
interactions using tandem affinity purification. Interestingly, FBXW7
is a component of an E3 ubiquitin-protein ligase complex that mediates
the ubiquitination and subsequent proteasomal degradation of target
proteins. Since, the turnover rate of MYC is critical determinant of
carcinogenesis [[125]47], modulating these peptides may be useful in
changing the half-life of c-MYC. A single stretch of MDM2 (456–475)
contains two high scoring OLPs- ^456GHLMACF^462 and
^463TCAKKLKKRNKPC^475, both of which are predicted to bound to two
important proteins: RNF8 and FKBP3. RNF8 plays significant roles in
E2-E3 ubiquitin ligase complex and DNA damage response [[126]48].
FKBP3, also known as FKBP25, contributes to regulation of the p53-MDM2
negative feedback loop [[127]49]. Thus, there are strong motivations
for experimental validation of these findings using AP-MS and
peptide-protein interaction assays.
Nevertheless, there may be much more complexity associated with the
prediction of peptide segments in cancer-associated hub proteins that
mediate interactions with multiple partners. Although, we have
considered significant motifs identified in multiple MEME runs, still
there could be false positive interactors in the original PPI dataset.
In our method, we have considered a minimal overlap region, but the
actual interacting peptide segment can be longer or shorter in both N
and C-terminal directions. Furthermore, while our method does provide
information about the interacting partners, it does not investigate the
binding domain or the structural parameters involved in the
interactions. Nevertheless, the peptide-protein interactions predicted
by our method may be thereafter studied in PepSite2 for predicting the
probable binding site of the peptide on the interactor and the residues
of the interacting surface that may be involved in such binding. In
summary, we have made an attempt to develop a computational framework
for identifying OLPs that can complement and accelerate the discovery
of peptide regions mediating multiple interactions in date hub
proteins.
Supporting Information
S1 File
Table A: Number of primary (FHPI) & secondary (SHPI) intra-species
protein-protein interactions involving human MYC, APC and MDM2 as
retrieved from the IntAct database.
Table B: List of significant motifs observed by de novo analysis in
MYC.
Table C: List of significant motifs observed by de novo analysis in
APC.
Table D: List of significant motifs observed by de novo analysis in
MDM2.
Table E: Summary of FHPI protein 3D structures used for PepSite2
studies.
Table F: Results of docking of predicted OLPs against respective FHPI
3D structures in PepSite2.
Table G: Results of BLASTP search with the OLP sequences against all
human proteins in the SwissProt database.
Fig A: Distribution of OLP scores for randomly generated FHPI networks.
Fig B: Degree distribution of primary interactors of MYC.
Fig C: Degree distribution of primary interactors of APC.
Fig D: Degree distribution of primary interactors of MDM2.
Fig E: OLP identification in human APC protein. (i) Three OLP sequences
(each marked by a different background colour) were identified by
Multiple Sequence Alignment of the peptide sequences in APC
corresponding to all significant motifs (E-value<1.0) inferred by MEME
from its SHPI network. (ii) Diagrammatic representation of an OLP
sequence ^416YCETCWEW^423 (marked with blue background) identified in
APC, which may interact with three FHPIs. The nodes representing
oncoproteins have been marked with a red border.
Fig F: OLP identification in human MDM2 protein. (i) Six OLP sequences
(each marked by a different background colour) were identified by
Multiple Sequence Alignment of the peptide sequences in MDM2
corresponding to all significant motifs (E-value<1.0) inferred by MEME
from its SHPI network. (ii) Diagrammatic representation of an OLP
^456GHLMACF^462 (marked with pink background) identified in MDM2, which
may interact with four FHPIs. The nodes representing oncoproteins have
been marked with a red border.
Fig G: OLP identification in human GASP2 protein. (i) OLP identified by
Multiple Sequence Alignment of the peptide sequences in GASP2
corresponding to all significant motifs (E-value<1.0) inferred by MEME
from the SHPI network from three FHPIs- ADRB1, ACM1 and CALCR. (ii)
Diagrammatic representation of the OLP ^452WFWDRDEACFDLNPCPVY^469 that
may be predicted to interact with the three FHPIs- ADRB1, ACM1 and
CALCR.
Fig H: Screenshots of peptide-protein interactions predicted by
PepSite2 server.
1. Images for peptides from MYC_HUMAN.
2. Images for peptides from APC_HUMAN.
3. Images for peptides from MDM2_HUMAN.
Fig I: Surface Accessibility predictions from SCRATCH prediction server
for:
(i) MYC_HUMAN, (ii) APC_HUMAN, (iii) MDM2_HUMAN.
(DOC)
[128]Click here for additional data file.^ (23.1MB, doc)
Acknowledgments