1. Rational Drug Design
Using the 3D Shape of Proteins to Design Drugs that
inhibit Protein Function
Download Cn3d software from NCBI

2. Why is protein structure important?
Each protein molecule has a characteristic 3D shape that results from coiling and folding of the polymer chain.
The function of a protein depends upon the shape of the molecule.

3. Protein chains
Each protein has a specific sequence of amino acids that are linked together, forming a polypeptide

4. The protein chain folds
Interactions between amino acids in the chain form:
Alpha helices
Beta sheets
Random coils
Together usually form the binding and active sites of proteins

5. And folds again..
After folding, amino acids that were distant can become close
Now the protein chain has a 3D shape that is required for it to function correctly

6. The final protein…3D structure
The final protein may be made up of more than one polypeptide chain.
The polypeptide chains may be the same type or different types.

7. Examples of Protein Function
Hormones
Insulin binds to receptors on cell membranes
signalling cells to take up glucose from the blood
Protein Channels Regulate movement of substances across the plasma membrane. E.g. The CFTR (cystic fibrosis transmembrane conductance regulator) protein pumps ions across membranes
Transport
Haemoglobin (far right) in red blood cells transports oxygen to cells around the body

8. Catalase - enzyme action
Hydrogen peroxide, a natural product of metabolism in your cells, is highly toxic in high concentrations and must be removed quickly!
Reactants
Products
oxygen
Add ferric ions (Fe 3+)
Rate increases fold
Add Catalase
Rate increases fold
Hydrogen peroxide
water
Location of active site where
Hydrogen peroxide binds

9. How enzymes do it?
Enzyme proteins have specific sites where all the action happens. We call this the active site. Molecules that need to be ripped apart or put together enter the active site.
Each protein has a specific shape so it will only perform a specific job.
Joining things together
Ripping things apart

10. Influenza Pandemics
The Spanish Flu in 1918, killed approximately 50 million people. It was caused by the H1N1 strain of influenza A.
The Asian Flu in 1957 was the H2N2 influenza A strain. Worldwide it is estimated that at least one million people died from this virus.
The Hong Kong Flu in 1968 evolved into H3N2. 750,000 people died of the virus worldwide

11. Designing a Flu Drug Step 1: looking for protein targets
Influenza viruses are named according to the proteins sticking out of their virus coat.
(H)
There are two types of protein = N and H.
N and H have special shapes to perform specific jobs for the virus.
(N)

12. N cuts the links between the viruses and the cell surface so virus particles are free to go and infect more cells.
H attaches to cell surface proteins so virus can enter cell
Virus
Proteins on cell surface
Virus genes are released into the cell.
The lung cell is ‘tricked’ into using these genes to make new virus particles.
Human Lung Cell

13. Blocking the active site
Neuraminidase
in Cn3D
RELENZA
This link will open a Cn3D file of Neuraminidase with the drug relenza blocking its active site
Australian team did it

14. Some facts…
Calcium, sodium and potassium ions control essential functions inside cells: calcium, for example, helps regulate the contraction of muscle cells.
Ion channels control the entry and exit of ions into and out of cells.
Some conotoxins act as analgesics, interacting with ion channel receptors in nerves so the ion channel cannot open. Blocking ion channels stops ions from entering a neighbouring nerve fibre. No electrical impulse is set off so the ‘pain’ message is switched off!

15. The nerve impulse Sodium ion Calcium ion Acetylcholine
3. Influx of Calcium causes acetylcholine to be released into synaptic junction.
Synaptic Junction
Na+
Ca2+ + + - -
2. Sodium ions accumulate causing Calcium ion channels to open. - - + +
4. Acetylcholine binds with receptor proteins changing the shape of the ion channel.
5. This opens the sodium ion channel to let in sodium.
6. Sodium ions set off an electrical impulse along the next nerve cell.
7. The pain message is working.
1. Electrical impulse generated along axon – sodium ions (red) rush in and Potassium ions (green) rush out
To block pain we can try to target the ion channels.

16. Na+ ion channel You will explore this part of the ion channel.
This is the section that binds acetylcholine &/or drug molecules causing the ion channel to change its shape.
Outside neuronal cell
Cell membrane (Phospholipid bylayer)
Inside neuronal cell
Some conotoxins block acetylcholine (nACh) receptors that stud the surface of neurons.

17. Chemoinformatics
QSAR
Quantitative Structure Activity Relationship (QSAR) is a set of methods that tries to find a mathematical relationship between a set of descriptors of molecules and their activity.
The descriptors can be experimentally or computationally derived. Using regression analysis, one can extract a mathematical relationship between chemical descriptors and activity.

18. Descriptors in QSAR study
Constitutional Descriptors
Topological Descriptors
Geometrical Descriptors
Electrostatic Descriptors
Quantum Chemical Descriptors
Thermodynamic Descriptors
Reactivity Descriptors

19. Constitutional Descriptors
Total number of atoms in the molecule
Absolute and relative numbers of atoms of certain chemical identity (C, H, O, N etc.) in the molecule
Absolute and relative numbers of certain chemical groups and functionalities in the molecule
Absolute and relative numbers of single, double, triple, aromatic or other bonds in the molecule
Total and relative number of 6 membered aromatic rings
Molecular weight and average atomic weight

20. Topological Descriptors
Molecular connectivity index
Valence connectivity indices
Shape indices
Flexibility index
Structural information content index
Bonding information content index

21. Geometrical Descriptors
Molecular surface area
Solvent-accessible molecular surface area
Molecular volume
Solvent-excluded molecular volume
Principal moments of inertia of a molecule
Shadow areas of a molecule
Relative shadow areas of a molecule

22. Electrostatic Descriptors
Atomic partial charges
Minimum (most negative) and maximum (most positive) atomic partial charges
Polarity parameters
Dipole moment
Average ionization energy
Minimum and maximum electrostatic potential at the molecular surface
Local polarity of molecule
Total variance of the surface electrostatic potential
Electrostatic balance parameter

23. Quantum Chemical Descriptors
Total energy of the molecule
Standard heat of formation
Electron-electron repulsion energy for a given atomic species
Nuclear-electron attraction energy for a given atomic species
Nuclear repulsion energy between two given atoms
Total intramolecular electrostatic interaction energy
Electron kinetic energy density

24. Problems with QSAR descriptors
Ideally, chemicals with similar descriptors should show similar activity but similar in one context may not mean similar in another.
Difficult to represent interactions between descriptors
With many different types of descriptors, how do you compare them (e.g. what does it mean if the shapes are highly similar, but the charge distributions are very different?)

25. PubChem Compound

30. Sanjeevini Software
Sanjeevini software has been developed to provide a computational pathway for automating lead design.
The user can upload a bimolecular (protein) target and a candidate drug.
The software identifies the potential active sites, docks and scores the candidate drug and returns four structures of the candidate drug bound to protein target together with binding free energies.
The Ligand and Protein should be provided in pdb format for all the modules.

31. Modules of Sanjeevini software
NRDBSM database
Aimed specifically at virtual high throughput screening of small molecules and their further optimization into successful lead-like candidates.
It has been developed giving special consideration to physico-chemical properties and Lipinski's rule of five, which determines the solubility, permeability and transport characteristics across membranes.
Some of these are molecular weight, number of hydrogen bond donors and acceptors, and molar refractivity. Fixed precincts for these properties have been employed as filters to assemble the database.
Currently holds close to 17,000 molecules with simple structures, low molecular weight, less number of rings and rotatable bonds, low hydrophobicity such that after screening, optimization and consequent increase in molecular complexity, they would show a drift towards 'drug-like' property space.

32. Weiner Index
The ability of protein to bind to its substrates and to inhibitors in a highly specific manner is an important feature of many biological processes.
For example, determination of binding free energy of the target (protein) with a ligand (any small molecule that binds protein) is a very important step to design new drugs, determination of toxicity of certain chemical materials on living organisms, and many other pharmaceutical and biochemical application developments.
The most crucial bottleneck to finding binding free energy of the target with a ligand is the computational time. For example, it requires more than a minute to find the binding free energy of a target (protein) with only a single ligand in rigid docking method.
For flexible (non-rigid) docking method, it may require several minutes for a single candidate only. There are millions of such ligands which need to be tested to determine the good candidate(s) for a particular target protein.

33. a) Molecular mass less than 500 Dalton b) High lipophilicity
Lipinski Rule of Five
Lipinski rule of 5 helps in distinguishing between drug like and non drug like molecules.
It predicts high probability of success or failure due to drug likeness for molecules complying with 2 or more of the following rules -
a) Molecular mass less than 500 Dalton
b) High lipophilicity
c) Less than 5 hydrogen bond donors
d) Less than 10 hydrogen bond acceptors
e) Molar refractivity should be between
- Molar refractivity is a measure of the total polarizability of a substance and is dependent on the temperature and the pressure.
These filters help in early preclinical development and could help avoid costly late-stage preclinical and clinical failures

34. Charge Derivation
Partial atomic charge is very crucial for computing physical, chemical and biological properties, and reactivity of molecules.
Through the information of the atomic charge in a given species it is possible to predict the stability, solvation energetics of various molecules, and course of a particular reaction, determine its interaction with biological molecules and so on.
The usefulness, notwithstanding, there is no direct method to determine the partial atomic charges from experiment. During the last few decades various methods have been developed to determine the partial atomic charges, but all these methods have their limitations.
Two methods can be used for the partial charge derivation: TPACM4 or AM1BCC

35. TPACM4
Transferable Partial Atomic Charge Model – up to 4 bonds is used for deriving the partial atomic charges of small molecules for use in protein/DNA-ligand docking and scoring.
The main idea of TPACM4 is based on a look up table of template fragments consisting of 4-bond paths around the atom being charged.
This method overcomes the limitations of time complexity of assigning the partial atomic charges of a given molecule. The low value of average error against an experimentally observable physico-chemical properties indicates the reliability comparable to the RESP/AM1BCC results.

36. Active site finder:
The active site finder finds all the possible cavity points in a protein biomolecule along with the residues which are lining the cavity. The user can identify the cavity of interest on the basis of the amino acid lining the cavity and dock the candidate molecule in the cavity.

37. 1. SVMProt: Protein function prediction software
Software developed
1. SVMProt: Protein function prediction software
2. INVDOCK: Drug target prediction software
3. MoViES: Molecular vibrations evaluation server

38. Bioinformatics database developed
1. Therapeutic target database
2. Drug adverse reaction target database
3. Drug ADME associated protein database
4. Kinetic data of biomolecular interactions database
5. Computed ligand binding energy database