This document discusses computer assisted drug discovery (CADD) in plant pathology. It begins by outlining problems faced by plant protectionists like pathogen variability and pesticide resistance that necessitate new targeted approaches. The key stages of CADD are then described, including identifying suitable drug targets in plant pathogens, generating 3D structures through homology modeling or crystallography, molecular docking to screen compounds, and ligand-based approaches like pharmacophore modeling and QSAR when no target structure is available. Case studies applying these CADD methods to discover treatments for various fungal and bacterial diseases are also mentioned. The document concludes by noting potential challenges in applying CADD for plant pathogens.

2. Computer Assisted Drug Discovery in
Plant Pathology
Speaker:
Amrutha Lakshmi. M
11090
IIyear, Ph.D.
Chairman: Dr. Kalyan K Mondal
Seminar leader : Dr. Kajal K. Biswas
Dr. Lakshman Prasad
Division of Plant Pathology
ICAR-IARI

3. Need for CADD in plant pathology
Terminologies in CADD
Procedure of CADD for agrochemicals
Case Studies
Conclusion
Future thrust
Content

4. Pesticide SprayResistant Cultivar
Plant Diseases: Long standing threat to global food security

5. Growers, however, often rely heavily on
pesticides and other agrochemicals,
although great efforts have been put into
development and deployment of crop
plants that are resistant to plant
pathogen(s) (Chandler et al., 2011).
Heavy reliance on pesticides

6. …
Pathogen
variability
Fungicide
resistance
Emergence
of New
Pathogen
Environment
al Pollution
Climate
Change
Sustainable
agriculture
Problems faced by plant protectionists

7. In 1960s , more than 1 kg of agrochemical was
usually applied per ha due to lack of knowledge
about molecular target, today the use rates can be
reduced as 10 g/ha, it is only 1% of that previously
required (Lamberth et al., 2013)
Need of target specific approach
Agrochemical
Molecular target
molecular recognition processes

8. Targets for discovery of agrochemicals
The progress in Bioinformatics and Computational chemistry
facilitated the rapid investigation of agrochemicals for crop
plant protection (Avram et al., 2014).

9. Screening Separation Characterisation Synthesis
Bottlenecks
 Screening of large number of molecules for the desired
activity is difficult
 Low hit rate
 Time-consuming
 Cost-ineffective
Steps in Traditional Drug Discovery Pipeline

10. What is CADD????
Design/discovery of molecules that has strong binding
affinity to biomolecular target in a computer-modeling-
dependent manner.

11. Serves as an alternative strategy to
experimental screening techniques
greatly reducing the cost, time and
workload for drug development
Can be used for other stages of drug
discovery including hit-to-lead
optimization of affinity and selectivity.
Decrease the number of compounds
that need to be screened without
compromising lead discovery

12. During the last two decades, CADD has become a critical part of the development of
novel drugs in pharmaceutical industries (Taylor, 2015).
Role of CADD in Pharmaceutical Industries

13. Computer aided molecular docking and designing is a rational approach that is often
used in agrochemical discovery as an essential tools for screening and optimization of
ligands molecules
CADD in Plant Pathology
(Walter, 2002).
Jordan et al.,
2000
indamine and salicylamide
analogue
Yang et al.,
2002
2-heteroaryl-4-chromanones
Target Drug Pathogen Reference
ABC transporter,Amr1,
Beta-tubulin,Cutinase,
Fusicoccadiene synthase
Glutathione transferase
Camalexin
Brassilexin
Rutalexin
Spirobrassinin
Alternaria brassicae Pathak et al.,
2016
FGB1 Human antifungal
agents
Fusarium oxysporum
f.sp.cubense
Soundharaman
et al.,2011
Tps1 (trehalose-6-phosphate
synthase 1)
Molecular
Libraries Small
Molecule
Repository
Magnaporthe oryzae Xue et al.
(2014)
Gnt-R like regulators Xanthomonas
axonopodis pv. Citri
Liu et al.
(2012)
14α-demethylase synthetic XF-113
and ZST- 4
Ustilago maydis Han et al
(2010).
1)Lack of information and training on CADD for
researchers in plant pathology-related disciplines
2) Insufficiency of data for accurate modelling and
simulation

14. Terminology Definition
Drug (or) ligand The small chemical compound that can bind to protein or enzyme and can
treat the disease or a small chemical compound that binds to
macromolecules as signals to start (catalyse) the reaction.
Receptor (or)
Target
A biological molecule (mostly macromolecules such as protein and DNA)
that can receive a chemical signal (ligand) to catalyse a reaction or
function.
Docking This is a process of analysing the binding interactions of ligand and
receptor molecules.
Virtual
screening
A computational process in which a large number of ligand (small)
molecules are screened (analysed) to possess the best docking
interactions with the receptor molecule.
Pharmacophore The 3D representation of chemical features such as H-bond acceptors, H-
bond donors, and hydrophobic regions possessed by the ligand compound
or receptor binding site.
QSAR Quantitative structure activity relationship: a mathematical model used to
define the relationship between the physico-chemical properties and
biological activity of compounds.
Terminologies in CADD

15. 1.A schematic diagram of a typical CADD for agrochemicals
1.Plant pathogen drug target identification
Criteria for ideal macromolecule as
drug target
1.Essentiality : role in pathophysiology
2.Specificity: most specific to particular
disease/ host
3.Druggablity: function modification
through the binding of small molecules
4.Selectivity : well defined active site
allowing differential binding of small
molecules

16. Drug target Function Target pathogen Reference
Mur Enzymes Peptidoglycan
synthesis
Bacterial pathogens El Zoeiby et al., 2003
Pectate lyase Cell wall degrading
enzymes
Bacterial and fungal
pathogens
Herron et al., 2000
Ergosterol
biosynthesis pathway
Generation of a major
constituent of the
plasma membrane
Fungal pathogens Siegel, 1981
Lanosterol 14α-
demethylase
Steroid biosynthesis Fungal pathogens Sagatova et al., 2015
β-tubulin (TUB2) Microtubule assembly Fungal pathogens Wride et al., 2014
Threonyl-tRNA
synthetases
Protein translation
and cell viability
Phytophthora sojae Gao et al., 2012
Dihydrofolate Nucleotide precursor Phytophthora spp., Jain et al., 2017
Trehalose-6-
phosphate synthase 1
(Tps-1)
Trehalose synthesis –
energy and carbon
storage
Magnaporthe oryzae Xue et al., 2014
Asparagine synthase
(Asn1p)
Pathogenecity Magnaporthe grisea
Botrytis cinerea
Fusarium
graminearum
Colletotrichum spp.
Ustilago maydis
Ramakrishnan et al.,
2016
Dunn et al., 2009
Isocitrate lyase Virulence Leptosphaeria
maculans
Magnaporthe grisea
Stagonospora
nodorum
Colletotrichum
lagenarium
Rhodococcus fascians
Xanthomonas
campestris
Dunn et al., 2009
Dean et al.,
Type III secretion
system
Pathogenicity Pseudomonas
syringae
Ralstonia
solanacearum
Xanthomonas
axonopodis
Mansfield et al., 2012
Jovanovic et al., 2011
Boucher et al., 1985
Type IV secretion
system
Transport into the
host
Agrobacterium
tumefaciens
Pitzschke and Hirt, 2010
Rpf gene products Regulation of
pathogenicity factors
Xanthomonas oryzae
Xanthomonas
campestris
Xanthomonas
axonopodis
Mole et al., 2007
Boch and Bonas, 2010
Mansfield et al., 2012
HrpN Pathogenicity Erwinia amylovora
Ralstonia
solanacearum
Bocsanczy et al., 2008
Boucher et al., 1985
Potential drug targets in plant pathogens

17. 3D Structure of Target
Structure based drug
designing
A drug designing approach that works only with
availability of protein (receptor) 3D structure. Based
on the structure of the target protein, It allows
design of candidate drugs that are predicted to bind
to the target with high affinity and selectivity
2.A schematic diagram of a typical CADD for agrochemicals
SBDD (Structure based drug
designing)

18. A schematic diagram of a typical CADD for agrochemicals
Similarity search for
template
Homology modelling
If there is no 3D structure information of the
target
Homology modelling
Based on primary sequence similarity of
the target to homologous proteins, of
which 3D structure is empirically known
The target-template alignment leads to
the modelling of 3D structure of target
protein and validated by Ramachandran
plot
The accuracy of the built model depends on
the choice of template, alignment accuracy
and refinement of the model (Rost, 1999).
>70%

19. Molecular Docking
A schematic diagram of a typical CADD for agrochemicals
Molecular Docking
Principle
Degree of stability of interaction between
molecules is the key factor to determining
biological consequences of the interaction
Criteria For Selection Of Drugs
Based on the energy value obtained through
docking
lead molecules with maximum interaction
having high negative e-value
1) Correct
conformati
on
2) Binding
free energy
Docking is the binding orientation of
small molecules to their protein
targets in order to predict the affinity
and activity of the small molecule
 Anticipate the most favorable binding
mode(s) of a ligand to the target of
interest
 Predicts energetically stable orientation
of a ligand with a recognised 3D structure
of a protein
Applications
Structure–activity studies
Lead optimization
Finding potential leads by
virtual screening
Providing binding hypotheses to
facilitate predictions for
mutagenesis studies,
Assisting x-ray crystallography in
the fitting of substrates and
inhibitors to electron density,
Chemical mechanism studies
Combinatorial library design

20. Ligand based drug design
LBDD (Ligand based drug
designing)
A schematic diagram of a typical CADD for agrochemicals
LBDD relies on knowledge of structural
and chemical characteristics that
molecules must have for binding to the
target of interest
Difficulties in protein crystallisation of
Cell membrane proteins
Non availablity of 3D structure of target protein
or its homolog
Pharmacophore
Model
QSAR

21. A schematic diagram of a typical CADD for agrochemicals
Pharmacophore
Model
Pharmacophore
An abstract description of minimum, steric and
electronic features that are required for
interaction of target protein with ligand(s).
Pharmacophore modelling
Inference of pharmacophore using knowledge
on a set of ligands (training set) that can bind
to the target is called pharmacophore
modelling
Pharmacophore Modelling
• Alignment of multiple ligands (training
set by superimposing a set of active
molecules
• Transformed into abstract
representation of different features.
Pharmacophore model explains why
molecules of structural diversity can bind to
the common sites and have the same
biological effects

22. A schematic diagram of a typical CADD for agrochemicals
QSAR
Regression or classification models used to
predict activities of new chemical compounds
based on their physico-chemical properties.
Quantitative structure-activity
relationship (QSAR).
It relates a set of ‘predictor’ variables (X) such as
physico-chemical properties and molecular
descriptors to the potency of the ‘response’ variable
(Y) such as biological activity of the compound.

23. Virtual Screening
Virtual Screening
Computational method that evaluates large
libraries of compounds and subsequently
identifies putative hits (leads) through
comparison of 3D structures of ligands with
the putative active site of the target
Virtual Screening
A schematic diagram of a typical CADD for agrochemicals

24. Using 3D structure information of the target,
ligand can be designed de novo usually
carried with the placement of pseudo-
molecular probe molecule and then addition
of functional groups to satisfy the spatial
constraints of target binding site.
Also, the molecule will be grown fragment
by fragment to occupy the active site of
target molecules.
De novo ligand design (DnLD).
A schematic diagram of a typical CADD for agrochemicals

25. Comparison between types of virtual screening
Methods Molecular Docking Pharmacophore Model
Theory basis Molecular mechanics
Quantum mechanics
Statistics
Overview Obtain receptor structure information
and locate its binding site, mimic the
interaction between the receptor and
its ligands
Establish pharmacophore model,
evaluate the maching degree between
ligands 3D conformation and
pharmacophore models
Advantages 1.Algorithm is mature 1.High accuracy and efficiency
2.A variety of optional softwares 2.Several commercial pharmacophore
database
Disadvantages 1.Relatively large amount of
calculation
1.Low accuracy and rough results
2.Huge data preparation workload 2. Require operator able to develop
chemical software
3.Results analysis takes a long time

26. List of the important resources used in CADD

27. Case Study No : 1

28. Amir et al., 2018

29. General mechanism of DHN melanin biosynthesis pathway in fungi
Amir et al., 2018
Short-chain dehydrogenases/reductases (SDRs) are NADP(H)-dependent oxidoreductases
characterized by conserved catalytic tetrad (N-S-Y-K) and cofactor binding site (TGxxxGxG)
Tetrahydroxynaphthalene
reductase (T4HNR)
Scytalone
vermelone
THN
DHN
Naphthol reduction reactions in melanin biosynthetic
pathway.
THN
Novel fungal SDR gene
plays a crucial role in
virulence of Fusarium
wilt pathogen in
tomato
(Corrales et al., 2011).

30. 1.Retrieval of T4HNR protein primary sequence
2.Similarity search across several protein database
to find a suitable template for T4HNR protein
Blastp server
5. Validation of sequence alignment results Bioedit
1.Database Search
JGI genome portal
4. Represention of the consensus and conserved residues
present in the T4HNR protein across the different
members .
3.Multiple sequence alignment between the template
andThe target protein
Clustalw
CLC BIO workbench
Amir et al., 2018

31. Establishment of the phylogenetic relationship between the
different homolog and orthologs using the neighbor-joining (NJ)
and maximum parsimonious the at 1000 replication bootstrap values.
Searching of the functional domain of the identified
protein ExPASy-PROSITEscan4
Further searching of identified protein for finding the functional signature
sequences against the InterPro protein signature database InterProScan 5.05
2.Comparitive Phylogeny and Functional Domain Analysis
MEGA6 suite2
Visualisation of the similarities and differences in the T4HNR proteins in between
different homologous and orthologous fungal partners based on the comparison
of their protein sequences retrieved through the conservation
of Genomic segments at 50% cutoff filter values
circos
visualization tool3
Amir et al., 2018

32. Retrieval of FOXG_04696 (T4HNR like)
protein primary sequence
Similarity search against PDB to find a suitable
Template for FOXG_04696 (T4HNR like)
Blastp server
Modeller v9.19
Validation of resultant model Procheck
Qualitative Assessment methods ProSA analysis
2.Homology modelling
PDB database
Prediction of homology model
The sequence alignment between the template and
The target protein
Clustalw
Visualisation of predicted model Discovery Studio 3.0

33. Checking of the compatibility of atomic models (3D)
withits own primary amino acid sequences (1D).
VERIFY3D
The quantitative evaluation of the model VADAR
.
S
Superimposition of the modeled FOXG_04696
protein over the template T4HNR (SDR) protein of M. grisea
(1JA9) to compare their structural alignment
and similarities using the
AustrAlis
• The quality was verified using the ERRAT score and the overall quality assessment of
predicted model was done through ProTSAV score values
Submission of the final model to an online repository protein
modeling databases
PMDB
Amir et al., 2018

34. Predicted structure of the FOXG_04696 modeled
Putative Binding Sites
Amir et al., 2018
Cavities surrounding active sites
Homology modeling using Modeller v9.19

35. Retrieval of structure of ligands
Import of selected target receptor FOXG_04696 protein
Pubchem database of
NCBI
Generation of 3 dimensional cordinates of ligand molecules
v
Maestro v11
Schrödinger
2.Retrieval and preparation of ligand molecules
Generation of receptor grid of ligand molecules
v
Glide v7.1
Amir et al., 2018

36. Yet Another Scientific Artificial Reality Application)
3. Molecular Docking
Prediction of the scoring and binding interactions between the FOXG_04696
and the ligand using Xtra precision Glide score (XPGScore). The Glide XP
Amir et al., 2018
The molecular docking of the FOXG_04696 with fungicides for the comparative
binding energies and dissociation constant (Kd) of the docked molecular complexes
YASARA
where vdW: van der Waals energy
Coul :Coulomb energy,
Lipo:lipophilic contacts,
BuryP: penalty for buried polar group
H bond, hydrogen-bonding
metal : metal bonding
Rot B :penalty for freezing the rotatable bonds
Site :polar interactions with the residues in the
active site
a = 0.065 and b = 0.130 are coefficient constants
of van der Waalsenergy and Coulomb energy,
respectively.

37. 4.Molecular Mechanics and Binding
Energy Assessment
Analysis of the protein–fungicide docked complexes to predict the
free binding energies of the protein–fungicide docked complexes
(MM/GBSA)
analysis
Analysis of molecular dynamics simulations analysis up to 50
ns through to analyze the conformational stability Desmond v 4.2

38. General view of protein-ligand interaction
Residues in active sites making interaction with ligand
Amir et al., 2018

39. Comparative evaluation of protein-ligand (fungicide) docking interactions from YASARA programme and XP Glide score
(docking score) values.
Amir et al., 2018
 Maximum YASARA score
 Highest MM/GBSA
 Least dissociation constant
Glide XP: on an accurate pose prediction(the ligand ability to bind for a specific receptor
conformation) for each-protein—fungicide complex :
 Higher XPG Score-Lower ranking
Oxathiapiprolin was found to docked in an alternative conformation docked with the residues
that were either absent from any major or minor binding site, or were present beyond the limit
required for an accurate docking pose prediction

40. Interaction of Famoxadone
with FOXG_04696
3D Surface view of
FOXG_04696
3D representation
Oxathiapiprolin
3D representation of the
ligand Famoxadone
• Famoxadone docked with FOXG_04696 in an accurate and flexible docking pose with
residues that constituted the major binding site(GLY13, SER15, ARG16, VAL38, SER39, SER40,
ASP64, VAL65, SER66, SER92, GLY93, ILE94, LYS110, and VAL114)
• No stable docking conformation for stable binding of the Oxathiapiprolin at that particular
specified docking site . Oxathiapiprolin bounded with LEU100, VAL103, ILE108, LEU112,
VAL116, TRP146, GLY147, VAL148, PRO149, ARG150, HIS151, ALA152,LEU153, SER155,
ALA156, SER157, and ALA160 rather than the specified docking sites.
Conclusion
The study reported the interaction of Famoxadone with FOXG_04696 (T4HNR like)
with best protein ligand contacts through the core residues from major binding site of
receptor protein

41. In vitro Assessment of Fungicides
The growth inhibition recorded on the 4th and 8th days at different concentrations of
fungicides
Amir et al., 2018
4th
8th
The maximum growth inhibition was recorded on eighth day post inoculation

42. In vitro Assessment of Fungicides
The statistical data for growth measured at different concentrations and on even days
90.53%
74.42%
63.04%
44.36% 25.73%
19.99%
11.22%
7.04%
Amir et al., 2018

43. In silico Toxicity Assessment
Computational prediction of some ADME-Tox properties
(adsorption, distribution, metabolism, excretion, and toxicity)
for Famoxadone
FAF-drugs4.0 tool
it was found that the drug is non-
carcinogenic and acceptable
admetSAR results
• Non-carcinogen 0.7751
• Non-ames toxic 0.5395
• Non-inhibitor 0.8941
• Weak herg 0.9732
• Non-required
carcinogenetic
0.4799
Lipinski Rule of five for drug likeness
• molecular mass 374.000000
(<500 Da),
• hydrogen bond
donor
1
• hydrogen
bound acceptor
6
• Log P score
values
4.699
• Molar refractivity 103.70

44. Integration of knowledge from diverse disciplines such as chemo-informatics, plant pathology,
bioinformaticsand genomics with computational algorithms and technologies enable discovery of
new knowledge – discovery/development of new drugs

45. Conclusion
Considering current pace at which virulence/ pathogenicity factors are uncovered with aid
of genomics,
CADD will be playing important roles in narrowing widening gaps between our
understanding of molecular pathogenesis mechanisms and translation of such knowledge
into development of disease management strategies

46. Future Thrust
Removal of barrier in the accessiblity of CADD
to researchers from other disciplines
Need for more user-friendly interface and
configuration of overwhelming number of
parameters
Cost effective synthesis of lead molecule by
chemical companies.
Need for proper wet lab validation techniques