Designing a Tri-Peptide based HIV-1 protease inhibitor Presented by, Sushil Kumar Singh IBAB,Bangalore Submitted to Dr. Indira Ghosh AstraZeneca India Research Center, Bangalore

1
Designing a Tri-Peptide based HIV-1 protease
inhibitorPresented by, Sushil Kumar
SinghIBAB,BangaloreSubmitted to Dr. Indira
GhoshAstraZeneca India Research Center, Bangalore
2
Objective

• The objective of my project was to come up with a
  certain number of lead molecules which can be
  potential HIV1 Protease inhibitors.
• Based on the assignments I have tried to come up
  with different ways of designing drug molecules
  for the same target.
• In the end I expected to have a library of
  molecules having a high possibility of activity
  against the target.
• This information is vital for the future of
  rational drug designing, leading to more
  effective drugs with minimal side effects.

3
Flowchart of the path taken for the project
4
Protocol

• Built the model using Homology from InsightII.
• Analog-Based Drug Designing
• Used Analog Builder to generate both product and
  reagent based libraries using a scaffold from
  published literature.
• The library was annotated using the ' Lipinski's
  Rule of Five '.
• The resulting compounds were tested for docking
  score using Ligand-Fit and then their activity
  was predicted using Hypogen as well as QSAR.
• Structure-Based Drug Design
• Structure of active site of receptor were
  analysed and analog were generated ,but
  unfortunately we got some negative score.

5
Pharmacophore-Based Drug Design

• This was achieved using 3 different methods
• 1.A pharmacophore was built based on the
  structure of receptor.
• 2.Pharmacophore based on the common feature of
  known drugs.
• (Hip-Hop)
• 3.Based on the activity of the known potential
  hiv protease inhibitors (training set of data)-
  Hypo-gen
• - Using the best hypothesis from Hip-Hop the
  generated compounds were analysed.
• - The best hypothesis resulting from Hypo-gen was
  used to predict the activity of the lead
  molecule.
• QSAR Activity prediction - The activity of the
  lead compounds generated earlier was predicted
  using QSAR.

6
Analysis

• The analog library which was generated, many of
  the compounds were not following the ' Lipinski's
  Rule of 5'. On analysis I found that the
  fragments which I selected for library building
  were of high mol wt, more no. of HB
  donor/acceptor and in some cases no. of
  rotatable bonds were also more. To avoid this I
  generated library using fragments with low mol
  wt. And less no. of rotatable bonds.
• Hypothesis generation
• For a hypothesis to be good
• - the range between Fixed cost and Null cost
  should be high.
• ( F.c- N.c gt 85 )
• - the total cost should be closer to the fixed
  cost.
• - The Config. Cost factor should be less than 17.

7
Hypothesis Analysis

• I faced difficulty in deciding which hypothesis
  to take out of 10. Because many of the hypothesis
  were showing same type of variations with their
  values.
• To come out with the best hypothesis I did
  clustering, that also didn't give good result as
  most of the hypothesis were of same nature.
• Then we analysed the training set of data, which
  we used for hypothesis generation and found that
  most of the fragment were of common character,
  which might be the reason for not getting much
  variation.
• To proceed further, we took one hypothesis, but
  we couldn't validate our result using this
  hypothesis as it is not following the criteria of
  config and costs.

8
Score comparision

• Null Hypothesis dumping score for the null
  hypothesis
• Total cost57.9634 RMS1.7127 correl0Cost
  components Error57.9634 Weight0 Config0
  Mapping0 Tolerance 0
• Hypothesis Taken
• Total cost64.0193 RMS0.634962
  correl0.937511Cost components Error42.7823
  Weight1.58709 Config19.6499 Tolerance0
• Fixed CostTotal cost61.1381 RMS0 correl0
  Cost components Error40.3633 Weight1.12491
  Config19.6499 Tolerance0
  

9
Activity (Ic 50)
10
QSAR

• Molecules for the training set of data were
  generated into catalyst and imported to Cerius2.
  They were added to the study-table.
• Descriptors taken
• Topological descriptors
• Hosoya index, Zagreb index, Chi index,
  Winner index etc.
• Fragments constant descriptors
• HB acceptors, HB donor etc.
• Charge desriptors
• Charge, dipole Apo l etc.
• Every descriptor adds a dimension to the chemical
  space, to reduce the dimensionality without
  loosing any information we did PCA ( Principal
  Component Analysis).
• Using GFA we predicted the activities of the
  compound which we generated.

11
QSAR analysis

• By QSAR analysis I tried to find contribution of
  active fragments in the activity of the compound.
• While analysing the QSAR equations I found many
  terms with negative sign which were not good for
  the activity, so I substituted the groups to
  nullify the effect.
• By making the substitution I noticed that
  activity of the compound is changing.
• e.g
• If we replace ester group by an amide group we
  found activity increased by 300 fold.
• On introducing HB donor or acceptor, the
  activity of the compound decreased as we found
  that replacement of methyl group with hydroxyl
  group led to 2500 times lower activity.

12
Conclusion
13
(No Transcript)
14
(No Transcript)
15
(No Transcript)