This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
4. STRUCTURE BASED AND
LIGAND BASED DRUG
DESIGNING
STRUCTURE
BASED
Don’t know ligands
Know receptor
structures
LIGAND BASED
Don’t know receptors
Know ligands
7. STRUCTURE BASED DRUG DESIGNING
Three dimensional structure of the biological
target
Obtained through x-ray crystallography or NMT
spectroscopy
If experimental structure is not available, create a
homology model of the target, based on the
experimental structure of a related protein
Various automated computational procedures
may be used
8. STRUCTURE BASED DRUG DESIGNING
Protein structure determination
Docking
Binding free energy
Flexibility of protein-ligand complex
De novo evolution
9. PROTEIN STUCTURE DETERMINATION
HOMOLOGY MODELING
Fast method to obtain protein structures
To ensure the rationality of modelled structures,
checks on stereochemistry, energy profile,
residue environments, and structure similarity
are needed
10. PROTEIN STUCTURE DETERMINATION
FOLDING RECOGNITION
Threading
Calculates the probabilities of 3D structures
could form by given protein sequences
Both environment of residues interactions and
protein surface area are considered in the
threading protocol
Structure with highest probability is
recommended to construct the protein model
11. PROTEIN STUCTURE DETERMINATION
Ab initio PROTEIN MODELING
Based on physical principles, residue interaction
center and lattice representation of a protein to
build the target
Used when other protocols fail to predict and
unknown protein structure
Identity and accuracy given by this modelling is
lower than others
12. PROTEIN STUCTURE DETERMINATION
HOT SPOT PREDICTION
One big issue in SBDD is to determine ligand
active site
Determined using X-ray crystallography
But not possible for proteins that cannot be
crystallised
Several binding site determination methods
have been invented
13. DOCKING
A method which predicts the preferred
orientation of one molecule to a second when
bound to each other to form a stable complex
15. DOCKING TOOLS
AUTODOCK
Software AutoDock, developed by Olsen’s
Laboratory
A program for docking small flexible ligands into a
rigid 3D structure
16.
17. DOCKING TOOLS
CDOCKER
This protocol is a docking algorithm and retain all
the advantages of full ligand flexibility
Uses a sphere to define an active site, so the
knowledge of the binding site is not required
CHARMM based docking algorithm
18. DOCKING TOOLS
FLEXIBLE DOCKING
Retains receptor flexibility during docking of flexible
ligands
ChiFlex algorithm
LibDock program
Indicates the binding site where ligand polar and
non-polar groups may be bound to the favourable
positions of protein
19. DOCKING TOOLS
LIGAND FIT
A grid-based method for calculating receptor-
ligand interaction energies, which is crucial in initial
ligand shape match to the receptor binding site
Consists of
Definition of active site
Analysis of ligand conformations
Docking of ligands to a selected site
Scoring of predicted poses
20.
21. DOCKING TOOLS
TRANSMEMBRANE PROTEIN MODELING
There are many medicines that target
transmembrane protein (HER2 and GABA receptor)
Due to difficulties of crystallisation accurately
analysing transmembrane protein is difficult
Since there is influence of phospholipid bilayer a
membrane force field option can be included
23. BINDING FREE ENERGY
All the docking protocols discussed above do not
include functions for calculating binding free
energy
energy of binding = energy of complex –
energy of ligand –
energy of receptor
24. FLEXIBILITY OF PROTEIN-
LIGAND COMPLEX
Flexibility of complex must be studied
The difference in result of flexible docking and
LigandFit is due to difference in flexibility of
molecules, such that:
Flexibility = score of LigandFit –
score of flexible docking
The result of molecular simulation is related to
flexibility, and a positive relationship can be
obtained in flexibility vs. molecular dynamics.
25. De novo EVOLUTION
After docking program, we can modify ligands by
two methods
Based on active site features to identify functional
groups that can establish strong interactions with
the receptor. Then, functional groups can be linked
Using the original ligand scaffolds to develop
derivatives that can complement the receptor
27. LIGAND-BASED DRUG DESIGN
Relies on knowledge of other molecules that bind
to the biological target of interest
These other molecules may be used to derive a
pharmacophore model
Alternatively, a QSAR relationship, in which a
correlation between calculated properties of
molecules and their experimentally determined
biological activity, may be derived
QSAR may be used to predict the activity of new
analogues
29. QUANTITATIVE STRUCTURE-
ACTIVITY RELATIONSHIP
Employs statistics and analytical tools to
investigate the relationship between the
structures of ligands and their corresponding
effects.
Mathematical models are built based on
structural parameters to describe
Earlier 2D-QSAR, but 3D-QSAR have been
adopted
3D-QSAR methodologies: CoMFA, CoMSIA
30. CoMFA
Comparative molecular field analysis
Biological activity of a molecule is dependent of
the surrounding molecular fields (Steric and
electrostatic fields)
Has several problems
31. CoMSIA
Comparative molecular similarity index analysis
Includes more additional field properties
Steric
Electrostatic
Hydrophobic
Hydrogen bond donor
Hydrogen bond acceptor
Can offer a more accurate structural-activity
relationship than CoMFA
32. CONCLUSION
Molecular simulation has a vital role in drug
design and CADD
Fast, efficient and inexpensive tool to
Discover new possible ligands against a
macromolecular target
Test library design ideas
Identify most promising scaffolds and R groups prior
to synthesis