Post on 22-Jan-2018
IN-SILICO IDENTIFICATION OF INHIBITORS
FOR CONTROLLING RICE BLAST
By Nisha Juyal, A K Mishra, Amrender Kumar, A K Jain
AKMU, IARI, New Delhi-110012
INTRODUCTION
• Blast disease of rice was found for the first time inCalifornia in 1996.
• Rice blast is caused by the fungus, Pyricularia grisea.The telomorph, Magnaporthe grisea is known to foundin laboratory culture, but not in nature.
• Considered as most important rice disease due to itswidespread distribution(over 80 countries).
• Potential enough to cause up to 50% yield loss underfavorable growth conditions.
• Blast can infect rice from seedling stage throughmaturity.
• The disease may also be called leaf blast, collar rot,node blast, panicle blast or rotten neck blastdepending on the portion of the rice plant infected.
OBJECTIVE
Identify potential anti-fungal agents that could be successful
fungicides against the rice blast by inhibiting the enzymes
that catalyze melanin synthesis.
TARGET IDENTIFICATION
• Melanin biosynthesis is a
potential target for antifungal
agent discovery.
• Melanin is very important to
host invasion in plant
pathogens.
• Fungi produce appressoria,
structures that penetrate
plant tissue, allowing the
invasion of the host.
• Appressoria melanin cell wall
provides strength for tissue
penetration.
Two enzymes of this pathway, Trihydroxynaphthalene reductase (THNR)and Scytalone reductase (SDH) are used as fungicides’ targets and byinhibiting these enzymes, the melanin production can be checked.
Melanin Biosynthetic pathway of Magnaporthe grisea
TARGET MOLECULES TRIHYDROXYNAPHTHALENE REDUCTASE (THNR) AND
SCYTALONE REDUCTASE (SDH)
Trihydroxynaphthalene reductase [PDB ID: IYBV] was presentas NADPH and an inhibitor complex. This protein has two chains,i.e. A and B. Scytalone dehydratase (SDH) is the second targetenzyme of melanin synthesis [PDB ID: 1STD].
Trihydroxynaphthalene reductase (THNR) Scytalone dehydratase (SDH)
DOCKING STUDIES
Involves docking a huge library of chemically available
molecules with drug-like properties to the protein target
of interest.
The idock server screens the large library of molecules
from the ZINC database.
Protein PDB files and ligands were obtained from the
idock server.
From these top 1000 results, the top 2 highest
scoring ligands were selected showing highest binding
affinity with both THNR and SDH.
Binding Affinity of top 2 ligands obtained from idock against THNR
S.
No
Ligand
IDs
M.W.
(g/mol)
Idock
score
RF-
score
C-
Score
PC
xlog
P
R
B
HB
D
HB
A
NC AD(kcal/mol
)
PD(kcal/mol
)
1. 32752902 406.48 -12.99 8.293 8.913 2.1 4 2 5 0 8.94 -13.93
2. 61205934 391.47 -12.76 8.127 8.745 2.4 5 2 6 0 8.05 -16.91
Binding Affinity of top 2 ligands obtained from idock against SDH
1. 13267643 391.446 -12.60 8.421 8.833 2.81 4 2 5 0 8.38 -17.73
2. 56212084 398.89 -12.45 8.372 8.755 2.98 4 2 5 0 7.2 -14.3
Binding Scores and other properties of highly active compounds against THNR
and SDH obtained through idock
•The study involves the prediction of conformations of the top 1000
ligands with the highest binding affinity to Trihydroxynaphthalene
reductase and Scytalone dehydratase using idock.
•From these top 1000 results, the top 2 highest scoring ligands were
selected showing highest binding affinity with both THNR and SDH.
RESULTS
DOCKING OF ZINC DATABASE COMPOUNDS WITH
THNR AND SDH USING IDOCK
idock score (kcal/mol): Consists of two parts, the first part is the
weighted sum of five terms over all atom pairs which are relative to
one another, excluding 1 to 4 interactions. Second part is
independent of any conformation and is calculated as the sum of
inter and intra molecular contributions.
RF-Score score (pKd): Uses non-parametric method for predicting
the binding affinity in an entire data-driven manner. RF-Score is
much better scoring function which sorts the protein-ligand complex
based on their binding affinity.
It has shown to be particularly effective as a re-scoring function for virtual
screening and lead optimization.
Consensus score (pKd): Using consensus scoring technique we
can improve the probability of finding optimal solutions using
combination of scores from multiple scoring functions.
Consensus score is defined by the formula which is described below
Consensus Score = 0.5(-0.73349480509.idockScore + RF Score)
Molecular Weight (g/mol) - The mass of one mole of a substance.
Molecular weights in the range between160 to 480 g/ mol are
preferred for drug-likeness property.
Partition coefficient xlogP - The measure of relative solubility in a
hydrophobic versus hydrophilic solvent. ZINC logP values are
determined by fragment-based methods which are similar to xLogP.
The hydrophobicity of drug will affect drug absorption,
bioavailability, hydrophobic drug-receptor interactions, molecule
metabolism, and their toxicity. Low LogP values are preferred for
drug-likeness.
Hydrogen bond donor and acceptor- HBD is defined as number
of oxygen, nitrogen, sulphur, or phosphorus with one or more
attached hydrogen atoms. HBA is defined as the number of oxygen,
nitrogen, sulphur, or phosphorus with one or more lone pairs.
Hydrogen Bond Donor-Acceptor (HBDA) calculates the
inclination of hydrogen bond donor and acceptor of atom. HDBA
is a useful for defining ´drug likeness´.
8 Molecular Properties Of The Ligands
Rotatable bonds- “Number of rotatable bonds” is a topologicalparameter on which the flexibility of molecule depends. Oral drugbioavailability is best descripting by this.
Rotatable bond is termed as the single linear bond, bounded by non-hydrogen atom.
idock succeed when the ligand consists less than 10 rotatablebonds.
Net charge- Groups of atoms bonded to one another which have anet positive or a negative charge called polyatomic ions.
The term net charge means the sum total of the charges ofthe individual elements.
Apolar desolvation (kcal/mol) - Energy required to remove solventfrom their compound, i.e. the free energy reduction by dissolving thecompound.
Polar desolvation (kcal/mol) – The desolvation energy is proportional to the surface area change available to water.
Polar surface area tPSA (Å2) - It is a measure of polarity. tPSAdetermines the blood brain barrier penetrance. Surface of polaratoms (PSA), is a descriptor that correlated well with passivemolecular transport through membranes and, allows prediction oftransportation properties of drug.
TOP SCORING COMPOUNDS OBTAINED THROUGH PYRX AGAINST THNR AND SDH
Binding affinity of top 10 ligands docked to PyRx against THNR
S. No Ligands BA (Kcal/mol) RMSD/upper bound RMSD/lower bound
1. 1YBV_47622465 -12.4 0 0
2. 1YBV_70932033 -12.4 0 0
Binding affinity of top 10 ligands obtained through PyRx against SDH
1. 1STD_09011974 -12.2 0 0
2. 1STD_66511493 -12 0 0
VALIDATION OF RESULT USING PYRX
The top scoring ligands obtained as a result of virtual screening of compounds
from ZINC database were selected for further validation using Autodock Vina
version, an in-built tool in the PyRx soft ware.
The compounds with high binding affinity in kcal/mol and with zero value RMSD
(both upper bound and lower bound) have been reported as final anti-fungal
compounds targeting both THNR and SDH in the table.
The docking performed against
idock results for validation of the
ligands gives four compounds as
highest scoring compounds.
Two compounds for each target
protein were selected as highly
optimized compounds for targeting
THNR and SDH and can be used
as potent anti-fungal drugs in near
future after experimental validation.
These compounds are (a) ZINC47622465 (1-[[3-(piperidine-1-
carbonyl) phenyl] methyl]-3-[(1R)-tetralin-1-yl]
urea),
(b) ZINC70932033 (2-(1H-indol-3-yl)-N-[1-
(pyridine-3-carbonyl) indolin-5-yl), (c)
ZINC09011974 (2-fluoro-N-[2-hydroxy-5-oxo-
1-phenethyl-4-(trifluoromethyl) imidazol4yl]
benzamide), and (d) ZINC66511493 (3-
carbamoylphenyl).
CONCLUSION AND FUTURE SCOPE
Identifying the novel compound for lead Optimization is a challengingtask for drug discovery.
Virtual Screening is a technique used for Hit Identification in MedicinalChemistry.
We have demonstrated such an approach which is successful in findingfour novel polyketide inhibitors in the Melanin pathway from the ZINCdatabase. i.e. ZINC09011974, ZINC66511493, ZINC47622465 andZINC70932033.
All the inhibitors have shown high binding affinity with idock score of -12.109 kcal/mol, -12.421 kcal/mol, -12.594 kcal/mol and 12.546kcal/mol against THNR and SDH, respectively.
The study shows the use of small molecule libraries in enhancing thedrug discovery process before synthesis.
The approach to select novel compounds from ZINC database dependson different parameters such as Lipinski’s rule of 5, ligandpharmacophoric groups, dataset size and compound libraries.
This work can be further extended to study the ligand-receptorinteractions and, the biological activity can be used in synthesizingcompounds based on virtual screening.
REFERENCES
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