1. G. Narahari Sastry Molecular Modelling Group Organic Chemical Sciences Indian Institute of Chemical Technology Hyderabad – 500 007 Gnsastry@iict.res.inGnsastry@iict.res.in; gnsastry@yahoo.comgnsastry@yahoo.com http://203.199.182.73/gnsmmg National Seminar on BioInformatics - Pondicherry

2. Drug Discovery & Development It starts with disease identification Isolate protein involved in disease (2-5 years) Find a drug effective against disease protein (2-5 years) Preclinical testing (1-3 years) Formulation Human clinical trials (2-10 years) Scale-up FDA approval (2-3 years)

3. Discovery and Development of Drugs Discover mechanism of action of disease Identify target protein Screen known compounds against target or Chemically develop promising leads Find 1-2 potential drugs Toxicity, pharmacology Clinical Trials

4. Genomic Approach to Drug Discovery Target Discovery Existing Chemical and biochemical knowledge Target gene annotation Literature Functional & comparative Genomics Functionally validated target ACB Target Prioritization Biochemical & Cell Based Assays Drug Development Small molecule lead Screening and improvement HTS+/- in silico SBDD Therapeutic Application Translated gene products ABC Sequence-structure analysis Experimental Validation Comparative Proteomics Genome data GO terms 1. Molecular Function 2. Biological process 3. Cellular component Role of targets in disease

5. Screening and Optimization Cycle with in-silico components Structure based design Target Selected Assay developed HTSChemistry begins Target structure obtained Candidate taken forward Database clustering Similarity analysis QSAR pharmaco phore

6. Nucleotide Sequence Analysis Protein Analysis Protein Modeling Indirect Drug Design Docking BI

7. Virtual Screening 10 6 small-molecule compounds vHTS: MM + scoring functions N x 10 2 leads Filters: ADMET / QSAR M x 10 1 leads Filters: synthesis / manufacturing / IP / patent / biological assays 1 - 5 leads

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

9. Much About Structure Structure Function Structure Mechanism Structure Origins/Evolution Structure Anything!!!

10. Exact solutions are available only for Hydrogen atom. Modeling any realistic system needs approximations (mathematically not solvable) Plenty of approximations were put forward to tackle mathematic complexity Quantum Mechanics “The underlying physical laws necessary for the mathematical theory of…the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.” -P. A. M. Dirac

11. Chemistry is an experimental science Experimental X-Ray NMR Structure, Stability and Reactivity Thermochemistry … … Computational Semiempirical Ab Initio DFT Molecular Dynamics Simulations Monte Carlo … Results Factual Data!!! Understanding, Patterning and Predicting Qualitative theory, Concepts, Rules, Correlations Basis for Doing Science and Doing it Better But alternative routes are attractive at times!!!

12. The Jargon of nomenclature Molecular Modeling Computational Chemistry Theoretical Chemistry Simulations Quantum Chemistry Computational Biology Molecular Dynamics Mathematical Chemistry Central Paradigm: Deriving information on molecular systems without really synthesizing them.

13. Computational Chemistry Quantum Mechanics (QM) Molecular Mechanics (MM) Hybrid QM / MM Semi-empirical (SE)

14. The current scenario in chemistry Computation has become an effective alternative to explore the structural, energetic, mechanistic and other properties of small molecules (say less than 8-10 atoms). SOMETIMES THE COMPUTATIONAL ACCURACY SUPERCEDES THE EXPERIMNTAL ACCURACY

15. Every Computational Experiment at Any Level of Theory Yields an Answer… Usually Answers for Many Questions Judging the Reliability is the Crucial Task Just Like Experiments Fail, Computations Fail

16. However, the challenges are of different kind in modeling chemistry and biology!! It is not only the size but the philosophy!!!..!!! The paradigm shift …

17. MESDAMESETMESSRSMYN AMEISWALTERYALLKINCAL LMEWALLYIPREFERDREVIL MYSELFIMACENTERDIRATV ANDYINTENNESSEEILIKENM RANDDYNAMICSRPADNAPRI MASERADCALCYCLINNDRKI NASEMRPCALTRACTINKAR KICIPCDPKIQDENVSDETAVS WILLWINITALL 3D structure Biological Structure Organism Cell System Dynamics Cell Structures SSBs polymerase Assemblies helicase primase Complexes Sequence Structural Scales

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

19. Relevant timescales 10 -15 femto 10 -12 pico 10 -9 nano 10 -6 micro 10 -3 milli 10 0 seconds Bond vibration Isomeris- ation Water dynamics Helix forms Fastest folders typical folders slow folders long MD run where we need to be MD step where we’d love to be Conformati onal transitions Enzyme catalysis Ligand binding Protein folding

20. How does the drug differ from an inhibitor? Selectivity Less toxicity Bioavailability Reach the target Ease of synthesis Low price Slow (or) no development of resistance Stability upon storage as tablet or solution Pharmacokinetic parameters No allergies

21. Bioavailability (ADMET) ADMET Adsorption Distribution Metabolism Excretion Toxicity Model and Predict: Biotransformations & metabolites Catalytic reactions Drug-receptor interactions GI physiology Transepithelial transport Epithelial permeability Solubility Toxicity

22. Which Strategy? Do you have a validated target? Do you have active ligands? Do you have both?

23. Computer Aided Drug Design

24. Drug Design Structure based Ligand based

25. 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

26. Various Steps Involved Get the structure of the receptor Identify the active site Build a library of possible ligands Docking & Scoring Understand receptor-ligand interactions Design new ligands

27. Structure Based Ligand Design

29. CADD Success Stories FKBP Ligand docking and scoring P. Burkhard et al., J. Mol. Biol. 287, 853-858, 1999 K + ion channel blocker fragment-based evolutionary design G. Schneider et al., J. Computer-Aided Mol. Design 14, 487-494, 2000 Ca 2+ antagonist / T-channel blocker pharmacophore similarity search G. Schneider et al., Angew. Chem. Int. Ed. Engl. 39, 4130-4133, 2000 Glyceraldehyde-phosphate DH inhibitors combinatorial docking J.C. Bressi et al., J. Med. Chem. 44, 2080-2093, 2001 Thrombin inhibitor docking, de-novo design H.J. Bohm et al., J. Computer-Aided Mol. Design 13, 51-56, 1999 HIV-1 RNA TAR inhibitor docking, database search A.V. Filikov et al., J. Computer-Aided Mol. Design 14, 593-610, 2000 Aldose reductase inhibitors 3-D database searching Y. Iwata et al., J. Med. Chem. 44, 1718-1728, 2001 DNA gyrase inhibitor structure-based virtual screening H.J. Boehm et al., J. Med. Chem. 43, 2664-2674, 2000

30. Let us look at some of recent interests

31. Broad Objectives: Aiding the experimentalists in Drug/Molecule/Reaction design Theoretical/computational approaches to bring insights which might trigger interest of the prospective experimental groups (Usually with no collaboration with experimentalists) Rationalizing the experimental finding with computations and participate in the designing of experiments (In collaboration with experimentalists or groups of experimentalists) We strongly believe that while chemistry and biology are experimental sciences THEORY-EXPERIMENT INTERPLAY IS INDISPENSABLE

32. In our pursuit to engage with experimentalists for lead discovery or optimization, our efforts become restricted in the absence of an experimental structure of the receptor protein/enzyme. When we analyze, it occurred to us that most of these ‘important target receptors’ whose structures are not available belong to the class of ‘membrane proteins’. Non-availability of the receptor structure is a bottleneck…

33. Membrane proteins are those that exist in cell membranes. They can serve as structural supports, as both passive and active channels for ions and chemicals, or serve more specialized functions such as light reception. Membrane proteins form about 25% of all protein sequences. (They constitute close to 70% of drug targets) Only 2% of PDB structures belong to membrane proteins! MEMBRANE PROTEINS – What are they Sastry et al, Computational Biology and Chemistry, 2006, in press

34. Membrane proteins form about 25% of all protein sequences. Only 2% of PDB structures belong to this class!

35. Membrane Proteins: Classification… Receptors for extracellular ligands Ex :- G-Protein coupled receptors Tyrosine kinase receptors Transport proteins Ex :- Molecular translocators Ion channels Membrane-bound enzymes Ex :- Lipid synthases Cytochrome P-450 enzymes Proteins associated with cytoskeletal network Ex :- Cytoskeletal attachments Proteins associated with energy production Ex :- Photosynthetic complexes Respiratory chain complexes

36. Challenges in computer simulations of membrane proteins. Heavy molecular weight and size. Their association with lipid bilayer. Technical limitations related to the accuracy of the empirical potential function. Difficulties with accurately incorporating important variables such as pH, transmembrane potential. Starting configuration of a simulation may also bias the results in undesirable ways. Comparative protein modelling approaches are very essential Sastry et al, Computational Biology and Chemistry, 2006, in press

37. Membrane bound microsomal cytochrome P450 enzyme. Converts androgens to estrogens by aromatisation of A-ring of steroids. Estrogens and their carcinogenic metabolites are responsible for progression of breast cancer. WHAT IS THE ROLE OF THESE ACIDIC RESIDUES IN THE AROMATIZATION MECHANISM? HUMAN AROMATASE: A PERIPHERAL MP PLAY A MAJOR ROLE IN STEROID AND INHIBITOR BINDING. HEME ACIDIC RESIDUES HOMOLOGY MODEL Sastry et al, J. Com. Aided Mol. Design, 2006, in press

38. Our Attempts of Modeling Aromatase A protein model is constructed (based on CYP 2C5 (pdb code: 1NR6, sequence identity is found to be 28%) The role of acidic residues in controlling the function(substrate binding with androstenedione, testosterone and nor- androgens) is studied. Studies help in designing putative inhibitors to control the aromatase activity. Sastry et al, J. Com. Aided Mol. Design, 2006, in press

39. PROPOSED AROMATIZATION MECHANISM A-ring of ANDROGENS ANDROGEN

40. MOLECULAR DYNAMICS SIMULATIONS Before complexation to steroidal substrates Environment suitable for carboxylate formation High conformational flexibility No H-bond interaction ACTIVE SITE ACIDIC RESIDUES

41. MOLECULAR DOCKING After complexation to steroidal substrates A MOLECULE WHICH ARRESTS THESE PROPERTIES IS PROPOSED TO BE AN INHIBITOR Flexibility decreases. Environment suitable for carboxylate formation. CLAMPED ! H-Bond formation Repulsive interaction predicted.

42. Inhibition of aromatase activity by 4-hydroxy androstenedione (formestane) Critical H-bond between inhibitor and T310 hampering its’ role in the mechanism. ACTIVE SITE ONE COULD DESIGN A MOLECULE BY ADDING OR DELETING A GROUP FROM ANDROGEN SKELETON TO ARREST THE PROPERTIES OBSERVED FOLLOWING COMPLEXATION. OH ANDROSTENE- DIONE (Substrate) FORMESTANE (Inhibitor)

43. Human 5-lipoxygenase (5-LO)-Peripheral MP MODEL Catalytic domain β-barrel domain 5-LO catalyses the rate limiting steps in leukotriene synthesis. Calcium binds reversibly to 5-LO, triggering its translocation from the cytoplasm to the nuclear membrane. Ca(2+) binding Mg(2+) binding Tryptophan residues Non-heme iron Sastry et al, Biophys. Biochem. Res. Comm, 2004, 320, 461-467

44.  - barrel domain Two calcium binding sites are identified ; ligating residues: F14, A15, G16, D18, D19, L76 and D79.Two calcium binding sites are identified ; ligating residues: F14, A15, G16, D18, D19, L76 and D79. Ca(2+) location Important residues which affect activity are marked.

45. Transmembrane Lumenal Cytoplasmic ATP binds here PhosphorylationPhosphorylation. Inhibitor binding sites. E1 E2 Expose ion binding sites sequentially to each side of the membrane.Expose ion binding sites sequentially to each side of the membrane. Cation binding sites Sastry et al, Biophys. Biochem. Res. Comm, 2004, 319, 312-320; Biophys. Biochem. Res. Comm, 2005, 336, 961-966 Gastric Proton Pump H(+)K(+)-ATPase – Integral MP ANTI-ULCER TARGET

46. Inhibitor binding sites CYS323 CYS815 Omeprazole Covalent linkage Inhibitor Binding in TM region However, the large SBA in E2 precludes the covalent binding of Cys815 to omeprazole. This suggested another intermediate conformation with slightly more exposed Cys815. The existence of stable intermediate structures has been proved in 2004.

47. Cation binding in E1 conformation H3O+H3O+ H3O+H3O+ Cα – carbons of arenes in the pump. Regular disposition aids hydronium transport. T825 Q941 E797 N794 A341 V340 V343 E345 E822 D826 Proposed hydronium binding.

48. Amino acid ligands (D,E,N,Q) that bind to metal ions in proteins In general, the acidic amino acid or their amides (ASP, GLU, ASN, GLN) are present in the ligating sphere of the cations (Ca, Na, K, Mg, etc.). Additional ligating amino acid residues: Ala, Val, Thr, Leu, Phe etc. Typical non-covalent binding to cations (from PDB). The distances between the ligating atoms and ion vary for different cations. # of Binding structures for metals PDB (June 2004) Ca 2+ : 2020; Cu(II) : 298 Ni(II) : 118 Na + : 678; Mn (II) : 454 Co(II) :101 K + : 258; Fe (II) : 100 Fe(III) :269 Mg 2+ : 1167; Zn (II) : 1545 Asp GluAsn Gln

49. An investment in knowledge pays the best interest. pays the best interest. Benjamin Franklin

50. CAUTION…. macromolecular structure protocols methods Structure determinations methods 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

51. Traditional ApproachRational Approach

52. Done 999897969594939291 81828384858687888990 80797877767574737271 61626364656667686970 60595857565554535251 41424344454647484950 40393837363534333231 21222324252627282930 20191817161514131211 12345678910 It’s like a game of LUDO

53. Drug Discovery “This isn’t rocket science. This is much harder.” This is much harder.” -- Alan Holmer -- President, PhRMA

55. GNS, Dr. G. Madhavi Sastry, Dr. Y. Soujanya, Srinivas Reddy, Punnagai, Gayatri, Srivani, Sateesh, Nagaraju, Dolly, Srinivasa Rao, Prasad, Mukesh, Murty, Usha Rani, Srinivas, Janardhan, Bharat, Upendra. Past Ph.D. students: Dr. U. Deva Priyakumar, Mr. T.C. Dinadayalane