1. Structure Based Drug Design
G. Narahari Sastry
Molecular Modeling Group
Organic Chemical Sciences
Indian Institute of Chemical Technology
Hyderabad
INDIA
Seminar on Systems Approach to Bioinformatics, Feb., 18-20, 2004
Bioinformatics centre, Pondicherry University

2. Integration of Chemoinformatics and Bioinformatics
Computational chemistry
Small Molecules
Large Molecule Targets
Genomic Biology
Bioinformatics
Cheminformatics
In silico
High
Throughput Screening
Assays

3. Biological Structure Structural Scales System Dynamics Sequence 3D
MESDAMESETMESSRSMYNAMEISWALTERYALLKINCALLMEWALLYIPREFERDREVILMYSELFIMACENTERDIRATVANDYINTENNESSEEILIKENMRANDDYNAMICSRPADNAPRIMASERADCALCYCLINNDRKINASEMRPCALTRACTINKARKICIPCDPKIQDENVSDETAVSWILLWINITALL
polymerase
SSBs
Complexes
helicase
Organism
primase
Assemblies
Cell
Cell
Structures
System Dynamics

4. Much About Structure Structure Function Structure Mechanism
Structure Origins/Evolution
Structure-based Drug Design

5. Bottlenecks in developing Structure – Function Relationships
Structures determined by NMR, computation, or X-ray crystallography are static snapshots of highly dynamic molecular systems
Biological processes (recognition, interaction, chemistry) require molecular motions and are time dependent.
To comprehend and facilitate thinking about the dynamic structure of molecules is crucial.

6. What is Molecular Modeling?
A science that elucidates and validates experimental evidence
through imagination, visualization, and rationalization
Applied in many areas of research (Academic/Industrial)
Caveat: Is the interpolation and extrapolation reliable?

7. Capabilities of Molecular Modeling
Pharmacophore
Development
Hits from
Database Searches
Prioritization of Hits

8. Drug Design
Structure based
Ligand based ?

9. Ligand (analog)
based drug design
Receptor structure is not known
Mechanism is known/ unknown
Ligands and their biological activities are known
Target (structure) based drug design
Receptor structure is known
Mechanism is known
Ligands and their biological activities are known/ unknown

10. High Resolution Structural Biology
Determine atomic structure
Analyze why molecules interact

11. The Reward: UnderstandingControl
Anti-tumor activity
Duocarmycin SA
Atomic interactions
To get a glimpse of the Structural Biology approach, I will now show you an example from the cancer research in our laboratory.
2nd SLIDE- TITLE
I will describe how taking a snapshot of the atomic structure of two molecules at the moment when they are communicating can be used to understand how an extremely potent anticancer agent is targeted to a precise location on a molecule of DNA.
 First item
In this picture the green and pink sticks represent the atomic structure of one small portion of a DNA sequence from a gene. If you look carefully, you can see the well-known features of the intertwined DNA double helix in these atoms.
2nd and 3rd item
The blue sticks that are placed within the cloud represent the atoms of the anticancer drug duocarmycin SA. The simple stick representation is used to help visualize the molecules but in fact, the cloud is a much more realistic representation of the atoms.
4th item
This picture show how well the drug and the DNA fit together, like a key in a lock. This gives a graphic representation of one of the key elements of designing a drug: the need to make the shape of the drug complementary to the target. Many of the other key elements needed for drug design require an even deeper level of inspection of the structure of the molecule.
Next items- show small box and build the new larger box including the picture inside
We will zoom in closer to the atoms for a better view. At this level of magnification we can examine the details of the interactions between each atom in the drug and DNA target.
Next item- label “atomic interactions”
In this picture, the critical information is where the surface of the clouds come close to the sticks. It is here at this level of ultra magnification where we can really fine tune the details of the drug to generate the specificity that is needed to ensure there are no side effects to a drug.
Lights up to talk
Structural Biology is providing a whole new strategy for the design of drugs with higher specificity and fewer side effects than has been achievable with traditional approaches where hundreds or even thousands of random drug candidates must be scanned.
There is tremendous excitement in univeristies and the pharmaceutical industry because this structure-based drug design strategy will save huge amounts of time and money in the effort to develop new therapeutics for the clinic.
I look forward with anticipation to the opening of the new building when we will be placed in close juxtaposition with many of the most exciting laboratories on campus. I hope I have given you a sense of our excitement over the vast possibities of using the structural biology approach for advancing medicine and biology.
Shape

12. CAUTION….
Don't be a naive user!?!
When computers are applied to biology, it is vital to understand the difference between mathematical & biological significance
computers don’t do biology, they do sums quickly
macromolecular structure
methods
protocols
Structure determination methods

14. Structure Based Drug Design
Compound databases,
Microbial broths,
Plants extracts,
Combinatorial Libraries
Target Enzyme
(or) Receptor
Random screening synthesis
3D structure by
Crystallography,
NMR, electron
microscopy (or)
Homology Modeling
Lead molecule
Receptor-Ligand
Complex
Testing
Docking
Linking or
Binding
Redesign
to improve
affinity,
specificity etc.
3D QSAR
3D ligand
Databases

15. Drug and Target : Lock and Key ?
Most of the drugs “FIT” well to their targets

16. Some “Locks” are known but not all !!
Study of protein crystals give the details of the “lock”. Knowing the “lock” structure, we can DESIGN some “keys”.
This is achieved by COMPUTER Algorithms
This is called “STRUCTURE BASED DRUG DESIGN”
Algorithms
“Key”constructed
by computer
“Lock” structure
(from experiment)

17. Variations on the Lock and Key Model
1- Which structure of the lock should be targeted?
2- Is the binding pocket a good target?
3- Is structure-based design relevant for my receptor?
-Is the 3D structure reliable?
-Is the binding pocket static enough?
4- Which key fits best?
5- What are the prerequisite physicochemical properties for the key for better binding?

18. The ligand has been identified

19. Docking of Ligand to the Active site of Protein

20. 3D Structure of the Complex
Experimental Information: The active site can be identified based on the position of the ligand in the crystal structures of the protein-ligand complexes
If Active Site is not KNOWN?????

21. Building Molecules at the Binding Site
Identify the binding regions
Evaluate their disposition in space
Search for molecules in the library of ligands for similarity

22. Structure Based Ligand Design

23. Structure based drug design
Define Pharmacophore O
Ligand
Design O H H H O O O O H O H O O O H
DB Search

25. Molecular Docking
The process of “docking” a ligand to a binding site mimics the natural course of interaction of the ligand and its receptor via a lowest energy pathway.
Put a compound in the approximate area where binding occurs and evaluate the following:
Do the molecules bind to each other?
If yes, how strong is the binding?
How does the molecule (or) the protein-ligand complex look like. (understand the intermolecular interactions)
Quantify the extent of binding.

26. Molecular Docking (contd…)
Computationally predict the structures of protein-ligand complexes from their conformations and orientations.
The orientation that maximizes the interaction reveals the most accurate structure of the complex.
The first approximation is to allow the substrate to do a random walk in the space around the protein to find the lowest energy.

27. Algorithms used while docking
Fast shape matching (e.g., DOCK and Eudock),
Incremental construction (e.g., FlexX, Hammerhead, and SLIDE),
Tabu search (e.g., PRO_LEADS and SFDock),
Genetic algorithms (e.g., GOLD, AutoDock, and Gambler),
Monte Carlo simulations (e.g., MCDock and QXP),

28. Some Available Programs to Perform Docking
Affinity
AutoDock
BioMedCAChe
CAChe for Medicinal Chemists
DOCK
DockVision
FlexX
Glide
GOLD
Hammerhead
PRO_LEADS
SLIDE
VRDD

29. Different views of Docking

30. Ligand in Active Site Region
Active site residues
Histidine 6; Phenylalanine 5; Tyrosine 21; Aspartic acid 91; Aspartic acid 48; Tyrosine 51; Histidine 47; Glycine 29; Leucine 2; Glycine 31; Glycine 22; Alanine 18; Cysteine 28; Valine 20; Lysine 62

31. Examples of Docked structures
HIV protease inhibitors
COX2 inhibitors

32. Approaches to Docking Qualitative Quantitative Hybrid Geometric
shape complementarity and fitting
Quantitative
Energy Calculations
determine minimum energy structures
free energy measure
Hybrid
Geometric and energy complementarity
2 phase process: rigid and flexible docking

33. Rigid Docking
Shape-complementarity method: find binding mode(s) without any steric clashes
Only 6-degrees of freedom (translations and rotations)
Move ligand to binding site and monitor the decrease in the energy
Only non-bonded terms remain in the energy term
Try to find a good steric match between ligand and receptor

34. Describe binding site as set of overlapping spheres
Both the macromolecule and the ligand are kept rigid; the ligand is allowed to rotate and translate in space
In reality, the conformation of the ligand when bound to the complex will not be a minima.
binding site
overlapping spheres

35. The DOCK algorithm in rigid-ligand mode
Define the target binding site points.
Match the distances.
Calculate the transformation matrix for the orientation.
Dock the molecule.
Score the fit.

36. Flexible Docking
Dock flexible ligands into binding pocket of rigid protein
Binding site broken down into regions of possible interactions
hydrophobic
H-bonds
binding site from X-ray
parameterised binding site

37. Then dock the molecule into pocket by matching up interactions with ligand
Uses “random” translation, rotation, and torsion, and look for a better binding mode.
parameterised binding site
docked ligand

38. Even though we have considered the ligand to be flexible, the active site was kept as a rigid structure.
The side chains of the protein in the vicinity of the active site should be flexible, but computationally more expensive.

39. Incremental Construction (FlexX)
A piecewise assembly of ligand within the active site.
Generate rigid fragments by scissoring the rotatable bonds of known ligands.
Dock the fragments one by one starting from the larger fragment
Assemble the whole ligand by reconnecting them and repeat the docking process

40. Free Energy of Binding
Dock ligand into pseudo-intercalation site
Manual, automatic, and flexible ligand docking
Energy minimize to determine DG complex
Determine DGligand
_=interaction energy of ligand with surroundings when explicitly solvated
DGbinding = DHinteraction - T DSconformation+ DGsolvent

41. Need for Scoring
Detailed calculations on all possibilities would be very expensive
The major challenge in structure based drug design to identify the best position and orientation of the ligand in the binding site of the target.
This is done by scoring or ranking of the various possibilities, which are based on empirical parameters, knowledge based on using rigorous calculations

42. Exact Receptor Structure is not always known?

43. Receptor Mapping
The volume of the binding cavity is felt from the ligands which are active or inactive. This receptor map is derived by looking at the localized charges on the active ligands and hence assigning the active site.

44. Receptor Map Proposed for Opiate Narcotics (Morphine, Codeine, Heroin, etc.)*
6.5Å Å
Flat surface for aromatic ring
Cavity for part of piperidine ring
Focus of charge
Anionic site R1

45. Homology modeling
Predicting the tertiary structure of an unknown protein using a known 3D structure of a homologous protein(s) (i.e. same family).
Assumption that structure is more conserved than sequence
Can be used in understanding function, activity, specificity, etc.

46. Structure modeling (Structure vs. Sequence)
- Homology modeling
- Fold recognition/ Threading
The 3D structures are used to understand protein function and to design new drugs

48. Key step in Homology Modeling
Alignment
Multiple possible alignments
Build model
Refine loops
Database methods
Random conformation
Score: best using a real force field
Refine sidechains
Works best in core residues

49. Structure Prediction by Homology Modeling
Structural Databases
SeqFold,Profiles-3D, PSI-BLAST, BLAST & FASTA
Reference Proteins
C Matrix Matching
Conserved Regions
Protein Sequence
Sequence Alignment
Coordinate Assignment
Predicted Conserved Regions
Loop Searching/generation
MODELER
Initial Model
Structure Analysis
Sidechain Rotamers
and/or MM/MD
Refined Model
WHAT IF, PROCHECK, PROSAII,..

50. Generating a framework
Fragments which have the right conformation to properly connect the stems without colliding with anything else in the structure
Framework for just the target backbone is shown in yellow against the template structures

51. Typical Contributions of CADD to Drug Discovery Projects
Suggestions of structures which, when retrieved from a compound collection or synthesized, were found upon testing to be active or inactive as predicted
Development of structure-activity relationships
Visualization of receptor models, pharmacophoric models, molecular alignments, or data models
Reanalyzing available data to achieve new insights
Creative search of available structures to find new leads

52. Identification of preferred sites for structure elaboration
Development of models to improve drug transport, specificity, safety or stability
Development of mechanistic insights
Use of leads in one area to derive new leads in a related assay
Establishment of useful databases of project structures and properties
Computation of physical or chemical properties to correlate with activities

53. Kinds of Computational approaches for the discovery of new ligands
The search in 3D databases of known small molecules
De novo design

54. 2D Substructure searches
Structure Searching
2D Substructure searches
3D Substructure searches
3D Conformationally flexible searches
2D Substructure searches
Functional groups
Connectivity

55. De Novo Design 1) Define Interacting Sites
HB donor/acceptor regions, Hydrophobic domain,
Exclusion volumes
2) Select Sites
3) Satisfy Sites
4) Join Functional Groups
5) Refine Structure

56. Virtual screening: Target structure based approaches
Protein-ligand docking
The most promising route available for determining which molecules are capable of fitting within the very strict structural constraints of the receptor binding site and to find structurally novel leads.
The most valuable source of data for understanding the nature of ligand binding in a given receptor
Active site-directed pharmacophores
Pharmacophore
Excluded volumes
A Pharmacophore based method along with the utilisation of the geometry of the active site for enzyme inhibitors, represented by 'excluded volumes' features,
Produces an optimised pharmacophore with improved predictivity compared with the corresponding pharmacophore derived without receptor information
Greenidge et. al. J Med Chem. 1998, 41, 2503

57. Pharmacokinetics play an extremely important role in drug development
ADMET
Absorption
Distribution
Metabolism
Excretion
Toxicity

58. Outlook
Molecular modeling first introduced in the pharmaceutical industries in the early 70’s have raised probably unrealistic hopes such as it can “do it all”. But it took quite a while before it could deliver
No doubt, with the ever-expanding new powerful methods available, today’s modelers have the requisite potential to bring real benefits to pharmaceutical industry.

59. Molecular Modeling and Computational Chemistry are essential to understand the molecular basis for biological activity and has Tremendous Potential to aid Drug Discovery
A healthy interaction between computational chemists and pharmaceutical industry seem indispensable.

60. Structure Based Drug Design is an extremely important tool in the computer aided drug design.
I Hope that you are convince!

61. Molecular Modeling Group
IICT
January, 04