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Objective

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.

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Objective

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

  2. 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. Objective

  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 > 85 ) - the total cost should be closer to the fixed cost. - The Config. Cost factor should be less than 17.

  7. 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. Hypothesis Analysis

  8. Null Hypothesis: dumping score for the null hypothesis: Total cost=57.9634 RMS=1.7127 correl=0Cost components: Error=57.9634 Weight=0 Config=0 Mapping=0 Tolerance= 0 Hypothesis Taken: Total cost=64.0193 RMS=0.634962 correl=0.937511Cost components: Error=42.7823 Weight=1.58709 Config=19.6499 Tolerance=0 Fixed Cost:Total cost=61.1381 RMS=0 correl=0 Cost components: Error=40.3633 Weight=1.12491 Config=19.6499 Tolerance=0 Score comparision

  9. Activity (Ic 50)

  10. 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. QSAR

  11. 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. QSAR analysis

  12. Conclusion

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