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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Rao, K. Manibhushan, and Rao Manibhushan. Rice blast disease. Daya Books,1994.

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Hongjian Li, Kwong-Sak Leung, and Man-Hon Wong. idock: A Multithreaded VirtualScreening Tool for Flexible Ligand Docking. In Proceedings of the 2012 IEEESymposium on Computational Intelligence in Bioinformatics and ComputationalBiology (CIBCB), pp.77-84, San Diego, United States, 9-12 May 2012. DOI:10.1109/CIBCB.2012.6217214.

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