The document outlines a homework assignment focused on analyzing a research article about the inhibition of dengue virus through molecular docking and dynamics simulations targeting the ns5 methyltransferase. Students are required to read the article, answer multiple-choice questions related to the content, and utilize various computational tools to analyze peptide ligands. The research highlights the significance of targeting ns5 methyltransferase for novel antiviral drug development against dengue, emphasizing the use of cyclic peptides.

1. 05/10/2016
CH597 –computitional biochemistry
Due date: 05/13/2016
Name _____________________________
You are to work alone on this homework . If I see the exact
same "suspicious" pattern of correct and incorrect answers
between two or more students, I will suspect cheating and will
take appropriate action that may include giving you an F and/or
reporting you to the Dean.
Part I Analyzing a research article: please obtain and read the
article, " Screening of commercial cyclic peptide as inhibitor
NS5 methyltransferase of Dengue virus through Molecular
Docking and Molecular Dynamics Simulation", by Tambunan
and co-authors. The article is freely available in the Journal
"Bioinformation". After you read the article, answer the below
questions.
1. What disease is the paper focused on and how many cases are
there every year?
a. west Nile virus infection and 50-100 million
b. dengue infection and 5-10 million
c. dengue infection and 50-100 million
d. HIV infection and 50-100 million
2. The protein target the paper focused on is _______. It is
______ amino acids and is involved in ______ and DENV
________.
a. NS1, 800, RNA capping and genome replication
b. NS5, 900, RNA capping and genome replication
c. NS5, 900, DNA capping and genome replication
c. NS2B, 900, RNA capping and genome replication
3. The NS5 MTase domain is a promising target for drug
inhibition because:
a. methylation is critical to the DENV life cycle and survival

2. b. acetylation is critical to DENV survival
c. methylation is not critical to the DENV life cycle and
survival
d. de-methylation is critical to the DENV life cycle and survival
4. Two binding sites were targeted; they are:
a. SAM and SAH
b. SAH and RNA cap
c. SAM and RNA cap
d. RNA cap and DNA cap
5. What class of compound and how many were docked into the
NS5 binding sites and what PDB structure was used?
a. cyclic nucleotides, 300, 2S41
b. cyclic non-peptidic small molecules, 300, 1P41
c. linear peptides, 300, 1YAH
d. cyclic peptides, 300, 2P41
e. cyclic peptides, 3000, 2P41
6. What computational work-flow was used in the study?
a. docking, ADME prediction and normal mode analysis
b. docking, ADME and toxicity prediction and conformational
analysis
c. docking, ADME prediction and molecular dynamics
d. docking, ADME and toxicity prediction and molecular
dynamics
7. The ligand with the best predicted binding affinity at the
SAM site is
a. Atriopeptin I
b. TWY
c. SAH
d. Ser-Ala-SAP-IIB
e. Human urotensin

3. f. Tumor targeted pro-apoptotic peptide
g. none of the above
8. What primary biophysical criterion was used to identify four
promising docked ligands for each binding site?
a. predicted binding affinities less than (more negative)
standards
b. predicted binding affinities greater (more positive) than
standards
c. experimental binding affinities less (more negative) than
standards
d. experimental binding affinities greater (more positive) than
standards
9. Is [Tyr123] Prepro Endothelin (110-130) predicted to have
good ADME/Tox properties?
a. yes
b. no
10. The authors ultimately conclude that [Tyr123] Prepro
Endothelin (110-130) is a promising inhibitor of DENV
because:
a. it has a lower predicted binding affinity than standards, poor
predicted ADME/Tox and is too expensive
b. it has a lower predicted binding affinity than standards, good
predicted ADME/Tox and exhibited a stable MD trajectory
c. it has a lower predicted binding affinity than standards, good
predicted ADME/Tox and exhibited an unstable MD trajectory
d. it has a higher predicted binding affinity than standards, good
predicted ADME/Tox and exhibited an unstable MD trajectory
Part II Multiple choice questions based on 2P41 structure. You
will have to use SPDBV, VegaZZ and Vina to answer some of
the questions.
11. In terms of secondary structure, the 2P41 structure is best
classified as:
a. alpha-helical

4. b. beta
c. mixed alpha helical and beta
d. unstructured
12. Using SPDBV calculate the number of hydrogen bonds
formed between the SAH ligand and the dengue
methyltransferase protein.
a. 10
b. 2
c. 5
d. 7
e. 0
13. The SAH OXT atom forms a hydrogen bond with the N
Trp87 atom. Calculate the approximate electrostatic energy of
the SAH OXT atom interaction with N Trp85, assuming the
interacting atoms are completely buried and are surrounded by
"oily" groups. Use SPDBV to calculate the inter-atomic
distance and the partial charge on Trp87 N; assume the partial
charge on OXT is -0.55.
a. 16.23 kcal/mol
b. 0.21 kcal/mol
c. 4.06 kcal/mol
d. none of the above
14. Prior to a docking calculations, protein structures are
sometimes "regularized" or energy "minimized". Regularization
of a protein structure often involves setting all of the bond
lengths and angles to their equilibrium values. Assuming the
2P41 protein structure has been perfectly "regularized", what
would it's SPDBV molecular mechanics bond energy be?
a. > 0 kJ/mol
b. < 0 kJ/mol
c. = 0 kJ/mol
d. = 761.848 kJ/mol
15. The coordinates of the SAH C4' atom are:

5. a. -11.306, 86.124, 8.985
b. 86.124, -11.306, 8.985
c. -11.306, 86.124, 19.98
d. -11.217, 84.676, 9.419
16. Using the coordinates of the SAH C4' atom to define to SAH
binding site, and using the SAH ligand from the 2P41 file, use
Vina to re-dock SAH into its binding site. The predicted
binding affinity for the best Vina pose is approximately:
a. -12.3 kcal/mol
b. -8.0 kcal/mol
c. -6.5 kcal/mol
d. -4.7 kcal/mol
e. none of the above
17. Calculate the inter-atomic distances between the SAH ligand
x-ray coordinates and pose 1 predicted coordinates for N6-N6,
C4'-C4', and carboxyl C-C. Based on these distances, calculate
the approximate average distance deviation between pose 1 and
the x-ray SAH ligand.
a. 3.3 Å
b. 0.15 Å
c. 2.2 Å
d. 1.1 Å
open access www.bioinformation.net Hypothesis
Volume 10(1)

6. ISSN 0973-2063 (online) 0973-8894 (print)
Bioinformation 10(1): 023-027 (2014) 23 © 2014 Biomedical
Informatics
Screening of commercial cyclic peptide as inhibitor
NS5 methyltransferase of Dengue virus through
Molecular Docking and Molecular Dynamics
Simulation
Usman Sumo Friend Tambunan*, Hilyatuz Zahroh, Bimo Budi
Utomo & Arli Aditya Parikesit
Department of Chemistry, Faculty of Mathematics and Natural
Science, University of Indonesia, Depok 16424 Indonesia;
Usman
Sumo Friend Tambunan - Email: [email protected];
*Corresponding author
Received January 08, 2014; Accepted January 17, 2014;
Published January 29, 2014
Abstract:
Dengue has become a major global health threat, especially in
tropical and subtropical regions. The development of antiviral
agent
targeting viral replication is really needed at this time. NS5
methyltransferase presents as a novel antiviral target. This

7. enzyme
plays an important role in the methylation of 5’-cap mRNA.
Inhibition of the NS5 methyltransferase could inhibit dengue
virus
replication. In this research, two sites of NS5 methyltransferase
(S-Adenosyl methionine/SAM binding site and RNA-cap site)
were
used as targets for inhibition. As much as 300 commercial
cyclic peptides were screened to these target sites by means of
molecular
docking. Analysis of ligand-enzyme binding free energy and
pharmacological prediction revealed two best ligands, namely
[Tyr123] Prepro Endothelin (110-130), amide, human and
Urotensin II, human. According to molecular dynamic
simulation, both
ligands maintain a stable complex conformation between
enzyme and ligand at temperature 310 K and 312 K. Hence,
Urotensin II,
human is more reactive at 312 K than at 310 K. However, both
ligands can be used as potential inhibitor candidates against
NS5
methyltransferase of dengue virus with Urotensin II, human
exposes more promising activity at 312 K.
Keywords: Dengue virus, NS5 methyltransferase, commercial
cyclic peptides, molecular dynamics.
Background:
Dengue virus infection has become a very important global
health problem, especially in tropical and subtropical countries.
This disease is endemic in more than 100 countries and nearly
half of the world's populations are at risk of infection. As much

8. as 50-100 million cases of dengue fever occur every year,
including 500,000 cases of dengue haemorrhagic fever that lead
to hospitalization even death, mainly among children [1].
The agents that cause these infections are four serotypes of
dengue virus i.e., DENV-1, DENV-2, DENV-3, and DENV-4.
These serotypes differ in their antigenic complexes. People
whom infected with one dengue serotype do not automatically
acquire cross-protective immunity to other serotypes of
dengue. The presence of these four serotypes of DENV has
made the development of effective DENV vaccine become
challenging [2]. Current methods of treatment are limited to
support medical care and symptomatic treatment. Hence, new
treatments to control the severity of dengue infection, such as
antiviral agents, are obligatory.
The structural and non-structural proteins of DENV have
become the major target of antiviral design. The structural
proteins (capsid (C), premembrane (prM) and envelope (E))
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play vital roles in viral formation and life cycle. While DENV
non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B
and NS5) involve in genome replication, virion assembly and
avoiding innate immune responses. Among DENV non-

9. structural proteins, NS5 is the largest (900 amino acid residues)
and the most conserved protein in DENV (67% amino acids
sequence identity among dengue serotypes) [3]. NS5 has also
been an attractive target for antiviral development, as it is
required for RNA capping and DENV genome replication. The
NS5 protein consists of two domains: a methyltransferase
(MTase) domain at residues 1-296 of its N-terminal region and
an RNA-dependent RNA polymerase (RdRp) domain at
residues 320-900 of its C-terminal region. NS5 MTase exhibits
two methylation activities: guanine N-7 and nucleoside 2’-O-
methylation, with guanine N-7 occurs before nucleoside 2’-O-
methylation. Both of these activities depend on S-adenosyl-L-
methionine (SAM) as methyl donor for methylation process.
After each of methylation process, S-adenosyl-L-homocysteine
(SAH) is generated as a by-product [4]. A study suggested that
N-7 methylation is critical for viral life cycle, while failure to
perform both methylation processes is lethal to flavivirus [5].
Therefore, NS5 MTase might represent as an ideal target for
dengue virus therapy. NS5 MTase has two binding sites: the
SAM binding site, which lies in the same site for SAH, and
RNA cap site, which is also a GTP- and GTP analogues-binding
site. In this research, both of these sites became targets of
antiviral design.
Currently, the use of peptide as drug becomes a very promising
strategy to develop antiviral drug. Peptide-based drugs have
several advantages including higher bioactivity, high specificity
to their target, low interaction with other drugs and low
toxicity [6]. Peptide molecule showed antiviral activity against
Avian influenza virus subtype H9N2 [7] and subtype H5N1 [8].
Estimated market for peptide-based drugs is over $40 billion
annually [6]. In this research, we screened 300 commercial
cyclic peptides that are currently available on the market
against two binding sites of NS5 methyltransferase in order to
develop new hit inhibitors against dengue virus.

10. Figure 1: Visualisation of the interaction between two best
ligands and each site of NS5 MTase. (A) SAM site - [Tyr123]
Prepro
Endothelin (110-130), amide, human, (B) RNA-cap site -
Urotensin II, human, (C) RMSD value vs. simulation time of
protein-ligand
complex at SAM site, and (D) RMSD value vs. simulation time
of protein-ligand complex at RNA-Cap site.
Methodology:
Preparation of commercial cyclic peptides as ligands
Commercial cyclic peptides were sought online through the
database of peptide-seller chemical companies like Genesis
Peptides, Bachem, Selleck Chem, and American Peptide. Then
the structures of these peptides were drawn using ChemSketch
ACDLabs. The ligands were imported into MOE database
viewer and saved in .mdb format using the software MOE
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Informatics
2009.10. Ligands optimisation and energy minimisation were
conducted in order to adjust the structure of ligands and the
position of hydrogen atoms. AMBER94 forcefield was used in

11. the process of geometry optimisation. Energy minimisation was
performed by choosing the value 0.001 kkal/Ǻ mol for RMS
gradient.
Preparation of NS5 Methyltransferase as target protein
Amino acid sequences of DENV NS5 methyltranferase were
obtained in FASTA format from the National Center of
Biotechnology Information (NCBI) database, while 3D structure
of NS5 MTase was downloaded from the Research
Collaboratory for Structural Bioinformatics (RSCB) database.
Protein structure with PDB code 2P41 was chosen as target
protein. The protein structure was adjusted and optimised
through sequential steps of protonate 3D, partial charge and
energy minimization that are available in MOE. Forcefield
AMBER94 was also used in the process of energy minimization
of protein, but the selected value of RMS gradient for protein is
0.05 kkal/Ǻ mol.
Molecular Docking
The docking process was performed using software MOE
2009.10. Triangle matcher was assigned as placement method.
Scoring function used in this process was London dG that
ranked 100 best poses of ligand-protein complex. Refinement
was conducted based on forcefield parameter. Only one best
pose was taken out of 100 poses. The complexes with the lowest
ΔGbinding value were visualized using LigX-interaction option
in
MOE to expose their contact residues.
ADME and Toxicity Prediction of Cyclic Peptides
Prediction of ADME (Absorption, Distribution, Metabolism and
Excretion) character of cyclic peptides as drug candidates was
performed using online software, ACD I-Labs/Percepta
(http://ilab.acdlabs.com/iLab2/index.php). Toxicity analysis
was performed using offline software Toxtree v-2.5.0 based on
Benigni/Bossa rulebase for mutagenicity and carcinogenicity

12. [9].
Molecular Dynamics Simulation of Ligand-Protein Complex
Molecular dynamics simulation comprises of three stages:
initialisation, equilibration and production. Before conducting
those steps, the complex must be prepared by adjusting
forcefield parameter into AMBER94 and solvation system into
born solvation. Molecular dynamics simulation was performed
towards the best ligands obtained from molecular docking and
ADME-Toxicity prediction steps. This simulation was also done
using MOE 2009.10.
Discussion:
Screening of commercially available cyclic peptides was
performed to find potential inhibitors against two binding sites
of NS5 methyltransferase (SAM site and RNA-cap site).
Through online searching, 300 commercial cyclic peptides were
found and used as ligands to target NS5 MTase. All of these
ligands, along with the standards, were docked into SAM site
and RNA-cap site of NS5 MTase using MOE software. Amino
acid residues at the binding site of SAM are Ser 56, Lys 61, Cys
82, Gly 86, Trp 87, Thr 104, Lys 105, Asp 131, Val 132, Phe
133,
Asp 146, Ile 147, Lys 181 and Glu 217 [10]. Meanwhile, amino
acid residues with significant role at RNA-cap site are Lys 14,
Leu 17, Asn 18, Leu 20, Phe 25, Lys 29, Ser 150 and Ser 151
[11].
Standard molecules used at SAM-site were SAM, SAH and
TWY. SAM (S-adenosyl-L-methionine) is natural substrate of
this site, while SAH (S-adenosyl-L-homocysteine) is a by-
product and an analogue of SAM that was used in [10] as
inhibitor of SAM site. Standard molecules used at RNA-cap site
were RTP and YEF. RTP (Ribavirin Triphosphate) is also an

13. analogue of natural substrate of this site and also used in [10] as
inhibitor. TWY and YEF are cyclic peptides designed by
Tambunan et al [12] to target SAM site and RNA-cap site of
NS5
MTase.
Docking process generated 4 ligands with better affinity than
standards for each binding sites, as indicated by their lowest
ΔGbinding score of all protein-ligand complexes Table 1 (see
supplementary material). Negative value of ΔG in a reaction
indicates that a reaction is favourable. The most negative values
of ΔGbinding were shown by the complex of NS5 MTase –
[Tyr123] Prepro Endothelin (110-130), amide, human at SAM
site and the complex of NS5 MTase – Urotensin II, human at
RNA-cap site. Hence, these ligands have higher affinity with
their target sites than standards. Their interactions with contact
residues of SAM site and RNA-cap site were displayed at
(Table 1). The number of hydrogen bonds between ligands and
catalytic residues of RNA-cap site is less than that of SAM site,
due to smaller size of RNA-cap site than the size of SAM site.
Visualisation of ligand-protein interaction was shown at
(Figure 1a & b).
Prediction of ADME-Toxicity was conducted on 4 ligands with
the lowest ΔGbinding generated during docking stage. The
softwares used in this prediction were ACD I-Labs/Percepta
and Toxtree-v2.5.0. The properties observed using ACD I-Labs
were oral bioavailability, active transport, permeability
glycoprotein (PGP inhibitor), central nervous system (CNS)
active and probability of health effect on blood, cardiovascular,
gastrointestinal, kidney, liver and lungs. Meanwhile, the
properties observed using Toxtree-v2.5.0 was genotoxic and
nongenotoxic carcinogenicity based on Benigni/Bossa rulebase.
Based on these properties, analysis of ADME-Toxicity
generated one best ligand for each binding site, namely
[Tyr123] Prepro Endothelin (110-130), amide, human for SAM

14. site and Urotensin II, human, for RNA-cap site (see
supplementary material). These ligands possess good ADME
characters, show minimum effect on health and have no
carcinogenicity character.
Molecular dynamics simulation was conducted to study the
effect of solvent on the stability of complexes between the best
ligands and protein. This simulation was applied at
temperature 310 K and 312 K on two complexes, i.e. between
the complex of NS5 MTase – [Tyr123] Prepro Endothelin (110-
130), amide, human at SAM site and the complex of NS5 MTase
– Urotensin II, human at RNA-cap site. These temperatures
represent human body temperature at normal and fever
condition. Molecular dynamics simulation differs from
molecular docking in that it uses born solvation which takes
into account the presence of solvent molecules. Algorithm used
in this simulation is NPA (Nosé-Poincaré-Andersen), while the
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forcefield used is AMBER 94. The pressure of the simulation
process is 101 kPa.
Molecular dynamics simulation comprises of three steps
including initialisation, equilibration and production. The aim
of initialisation step is to prepare the solvent before entering the
next steps in molecular dynamics. Determination of the

15. equilibration time was conducted during 100 pico second (ps)
of initialisation step. Based on this process, potential energy of
protein-ligand complex remained steady after 40 ps. Hence,
after 40 ps the complexes between protein and ligand have
adjusted their conformation with the solvent. The initialisation
step was followed by heating and equilibration step. During
heating stage, temperature of the system was raised towards
the equilibrium stage during 20 ps. Based on the equilibration
time that was determined previously, the equilibration step of
this simulation was performed for 40 ps. The production steps
of molecular dynamics simulation were performed at 310 K and
312 K during 5000 ps. After each of production steps, the
complexes were brought into cooling stages for 20 ps to find the
lowest conformational energy of the molecules. This process is
called annealing. Cooling stage brings the temperature of
simulation into 1 K. The calculated position, velocity and
acceleration of the simulation were saved every 0.5 ps.
The conformational changes of protein-ligand complex can be
studied from the curve of simulation time versus RMSD (root-
mean-square deviation). Protein conformation is a set of three
dimensional coordinates of its atomic constituents. The
magnitude of conformational change between these coordinates
is described as RMSD value. The RMSD value of protein-ligand
complex versus simulation time was shown on (Figure 1C &
D).
According to the graph, the complex of NS5 MTase – [Tyr123]
Prepro Endothelin (110-130), amide, human did not undergo
much conformational changes as the temperature increased.
RMSD value of the complex at 310 K and 312 K showed no
significant difference, hence there are similarities in the
structure of complex at the given temperatures. Conversely, the
complex of NS5 MTase – Urotensin II, human maintained more
conformational stability of the complex at 312 K (temperature
during fever) rather than at 310 K (normal body temperature).

16. Conclusion:
Commercial cyclic peptides obtained from online websites were
screened against SAM site and RNA-cap site of NS5 MTase.
Screening through molecular docking process generated 4 best
ligands for each binding sites as shown by their lowest
ΔGbinding
compared to standards. The result of ADME-Toxicity
prediction revealed that [Tyr123] Prepro Endothelin (110-130),
amide, human and Urotensin II, human possess the best
ADME-Toxicity characters among other ligands. During
molecular dynamics simulation [Tyr123] Prepro Endothelin
(110-130), amide, human maintained a stable interaction with
the SAM site of NS5 MTase at the tested temperatures (310 K
and 312 K), while the complex of NS5 MTase - Urotensin II,
human at the RNA-cap site was more reactive at 312 K rather
than at 310 K. However, from this study, it could be inferred
that these two commercial cyclic peptides could serve as
potential candidates to be developed into antiviral agents
against DENV although Urotensin II, human is showing better
reactivity at 312 K compared with [Tyr123] Prepro Endothelin
(110-130), amide, human.
Acknowledgment:
This research was supported by grant from Hibah Penguatan
Riset Berbasis Kolaborasi Nasional Universitas Indonesia
Tahun 2014. USFT and AAP supervised this research, HZ
prepared the manuscript and BBU work on the technical
details. We would like to express our gratitude to Dr. Kiki
Ariyanti Sugeng from Department of Mathematics University
of Indonesia for proofreading the manuscript.
References:
[1] Halstead SB, Lancet 2007 370: 1644 [PMID: 17993365]

17. [2] Alen MMF & Schols D, J Trop Med. 2012 2012: 1 [PMID:
22529868]
[3] Perera R & Kuhn RJ, Curr Op Microbiol. 2008 11: 369
[PMID:
18644250]
[4] Noble CG et al. Antiviral Res. 2010 85: 450 [PMID:
20060421]
[5] Zhou Y et al. J Virol. 2007 81: 3891 [PMID: 17267492]
[6] Craik DJ et al. Chem Biol Drug Des. 2013 81: 136 [PMID:
23253135]
[7] Rajik M et al. Virol J. 2009 6: 74 [PMID: 19497129]
[8] Jones JC et al. J Virol. 2006 80: 11960 [PMID: 17005658]
[9] Benigni R et al. Environ Mol Mutagen. 2007 48: 754
[PMID:
18008355]
[10] Podvinec M et al. J Med Chem. 2010 53: 1483 [PMID:
20108931]
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[12] Tambunan USF et al. Bioinformation 2012 8: 348 [PMID:
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Edited by P Kangueane
Citation: Tambunan et al. Bioinformation 10(1): 023-027 (2014)
License statement: This is an open-access article, which permits
unrestricted use, distribution, and reproduction in any medium,
for non-commercial purposes, provided the original author and

18. source are credited
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Informatics
Supplementary material:
Table 1: Top 4 Ligands targeting SAM-site and RNA-cap site
Best Ligands at SAM
Site
ΔG
(kkal/mol)
Hydrogen Interaction Best Ligands at
RNA-cap Site
ΔG
(kkal/mol)
Hydrogen Interaction
Cyclo(GNWHGTAPD)
WSSNYYW-OH

19. -18.74 Phe 65, Leu 143, Asn 166,
Asn 170, Phe 178, Tyr 90,
Cys 82
Ser-Ala-SAP-
IIB
-15.79 Glu 149 (2), Ser 150 (3),
Glu 157, Leu 183, Gln
26, Lys 29 (2), Arg 57,
His 110
Atriopeptin I (rat) -23.14 Thr 104, Lys 105, His 110,
Glu 111
Val-Asp-
(Arg8)-
Vasopressin
-13.94 Ile 147, Ser 150 (2), Ser
151 (2), Glu 157, Thr
161, Asn 208, Thr 215,
His 216, Lys 14, Lys 181,
Leu 210, Thr 215, His
216
Tumor Targeted Pro-
Apoptotic Peptide
-22.04 Lys 61, Leu 62, Asp 79, Gly
81, Arg 84, Trp 87, Ser 88,
Glu 112, Leu 143, Leu 144,
Asp 146, Glu 149 (2), Leu
167, Cys 179, Arg 84, Ile
114, Leu 143, Gly 148, Arg
212 (2)

20. Acetyl-(Cys4,D-
Phe7,Cys10)-a-
MSH (4-13)
-15.45 Thr 8, Leu 9, Glu 11, Glu
157, Arg 212, Thr 215
(2), Glu 217, Ser 236, Lys
14, Asn 184, Tyr 186,
Arg 212, Ser 214 (2), Thr
215, His 216, Glu 217
[Tyr123] Prepro
Endothelin (110-130),
amide, human
-24.73 Glu 111, Phe 133, Glu 149,
Ser 150, Val 132, Cys 179,
Lys 181, Leu 183, Glu 217,
Lys 29 (2), Lys 42, Arg 57,
Lys 105, Arg 163, Trp 171,
Arg 212, Thr 215
Urotensin II,
human
-19.04 Val 156, Glu 157, Lys 14,
Lys 29, Ser 151, Asn 153,
Arg 160, Asn 184, Tyr
186, Trp 13 (2)
Standard SAM -16.52 Gly 81, Asp 146, Glu 217,
Lys 61, Lys 181 (2)
Standard RTP -11.17 Lys 14 (2), Ser 150 (2),
Ser 151 (2), Thr 155, Glu

21. 157, Arg 160 (2)
Standard SAH -15.94 Asp 146 (2), Glu 217, Arg
57, Lys 61
Standard YEF -13.79 Asn 213, Thr 215, Ser
236, Lys 14 (2), Tyr 28,
Ser 151, Arg 207, Thr
215, Glu 217, Ser 236
Standard TWY -18.67 Thr 104, Glu 111, Gly 148,
Glu 217 (2), Arg 57, Lys 61,
Thr 104, Gly 148, Lys 181