MOLECULAR MODELING IN COMPUTER AIDED DRUG DESIGN

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

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

3. Discovery and Development of Drugs Biology 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 Chemistry Pharmacology

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

5. Database clustering pharmacophore Similarity analysis QSAR Target Selected Assay developed HTS Chemistry begins Target structure obtained Candidate taken forward Screening and Optimization Cycle within-silicocomponents Structure based design

6. In-silico Drug Discovery Docking CHEMICAL SYNTHESIS CLINICAL TRIALS Indirect Drug Design Protein Modeling Protein Analysis Nucleotide Sequence Analysis METABOLIC PATHWAYS DATABASES BI &

7. Virtual Screening 106 small-molecule compounds vHTS: MM + scoring functions N x 102 leads Filters: ADMET / QSAR M x 101 leads Filters: synthesis / manufacturing / IP / patent / biological assays 1 - 5leads

8. SmallMolecules Computational chemistry Genomic Biology Large MoleculeTargets Assays High ThroughputScreening Bioinformatics Cheminformatics In silico Integration of Chemoinformatics and Bioinformatics

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

10. 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 • 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

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

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

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

17. 3D structure Organism Cell Biological Structure Sequence Structural Scales MESDAMESETMESSRSMYNAMEISWALTERYALLKINCALLMEWALLYIPREFERDREVILMYSELFIMACENTERDIRATVANDYINTENNESSEEILIKENMRANDDYNAMICSRPADNAPRIMASERADCALCYCLINNDRKINASEMRPCALTRACTINKARKICIPCDPKIQDENVSDETAVSWILLWINITALL polymerase SSBs Complexes helicase primase Assemblies Cell Structures System Dynamics

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 Bond vibration Isomeris- ation Water dynamics Helix forms Fastest folders typical folders slow folders 10-15 femto 10-12 pico 10-9 nano 10-6 micro 10-3 milli 100 seconds MD step long MD run where we need to be where we’d love to be Protein folding Conformational transitions Enzyme catalysis Ligand binding

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 Ligand based Structure 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 • Ca2+ 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 We strongly believe that while chemistry and biology are experimental sciences THEORY-EXPERIMENT INTERPLAY IS INDISPENSABLE • 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)

32. Non-availability of the receptor structure is a bottleneck… 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’.

33. MEMBRANE PROTEINS – What are they • 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! 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! Catch: They constitute ~ 70% of drug targets!

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. HUMAN AROMATASE: A PERIPHERAL MP PLAY A MAJOR ROLE IN STEROID ANDINHIBITOR BINDING. ACIDIC RESIDUES HEME HOMOLOGYMODEL • 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? 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 No H-bond interaction High conformational flexibility ACTIVE SITE ACIDIC RESIDUES

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

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

43. Human 5-lipoxygenase (5-LO)-Peripheral MP Catalytic domain Non-heme iron MODEL Ca(2+) binding Mg(2+) binding Tryptophan residues 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. 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. Ca(2+) location Important residues which affect activity are marked.

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

46. Inhibitor Binding in TM region Inhibitor binding sites CYS323 Covalent linkage CYS815 Omeprazole 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 T825 Q941 D826 E345 E822 H3O+ H3O+ N794 Cα – carbons of arenes in the pump. Regular disposition aids hydronium transport. V343 A341 V340 E797 Proposed hydronium binding.

48. Amino acid ligands (D,E,N,Q) that bind to metal ions in proteins # of Binding structures for metals PDB (June 2004) Ca2+ : 2020; Cu(II) : 298 Ni(II) : 118 Na+ : 678; Mn (II): 454 Co(II) :101 K+ : 258; Fe (II) : 100 Fe(III) :269 Mg2+ : 1167; Zn (II): 1545 Typical non-covalent binding to cations (from PDB). The distances between the ligating atoms and ion vary for different cations. Gln Asp Glu Asn 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.

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

50. 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 determinations methods