Computer Aided Drug Design (CADD) Arie BS Farmasi UGM

Transcription

1 Computer Aided Drug Design () Arie BS Farmasi UGM

2 Drug Research in silico in vivo Clinical trials Model organisms Gene knockouts in cerebro Computer Aided Drug Design vhts Protein ligand docking ADMET / QSAR Computational chemistry in vitro NMR X ray crystallography Mass spec 2 D gel Gene/protein microarrays

3 Drug Design Strategies Combichem, HTS, virtual screening De novo design (protein flexibility) No protein 3D structure Protein 3D structure No ligand No ligand No protein 3D structure Protein 3D structure Ligand Ligand Pharmacophore mapping, 3D similarity, QSAR LBDD Docking SBDD

4 The Design Bicycle Proteins from Natural rganisms Preparative Biochemistry Assay, Characterization Expression Site Directed Mutagenesis Ligands from Natural Sources or Synthesis Protein Ligand Complex Gene Cloning Crystallization X Ray Gene synthesis Medicinal Chemistry CD, NMR Computer Graphics Simulation by DG, EM, MD Sequence Database 3 D Structure Knowledge Based Modelling and Design Biophysics Molecular Biology Biocomputing rganic chemistry

5 How Drugs Work + Enzyme Substrate Lock and key model Enzyme substrate complex

6 Structure Based Drug Design Determine Protein Structure Discovery or design of molecules that interact with biochemical targets of known 3D structure Identify Interaction Sites De Novo Design 3D Database Evaluate Structure Synthesise Candidate Test Candidate Lead Compound

7 Structural Targets 3D structure of target receptors determined by X ray crystallography NMR homology modelling Protein Data Bank archive of experimentally determined 3D structures of biological macromolecules currently entries 50 new structures per week

8 Rational Drug Design Leads in rational drug design are the natural: agonists, enzyme substrates, chemical messengers NATURAL LEAD S Y N T H E T IC D R U G N A T U R A L LE A D ral C o n ctrace p tiv es A n ti inflam m a tory NH2 H H H H H H pre g este ro ne H H (+) no rg estre l H H 17 β estra d io l H M e N Me serotonin H H H N H H H S Y N T H E T IC D R U G H H 1 7α eth ynyle strad io l Cl indom eth acin

9 What s involved in Rational Design Computational chemistry Molecular modeling (MM, ab initio, AM1 ) Property prediction (pka, log P ) Statistical modeling, QSAR, etc. In combination with experimental data: X ray diffraction 2D NMR Structure Based Ligand Design (SBLD) Data mining

10 Protein Based Drug Design or Virtual Screening 1. Docking (rigid, flexible)?? F 3C? 2. de novo Building N N Cl? Gbind (IC50, Ki) 3. Free energy scoring

11 Modeling the Cell

12 Lead Finding: Docking rigid ligand, protein database diversity ranking the hits expensive, long patenting rigid protein ranking the hits rapid energy check limited ligand flexibility Flexible Docking Rigid Docking uses existing molecules? systematic scan fast, cheap?? 3D database ( > 200,000 molecules) Known Protein No Ligand Single molecule

13 Docking algorithms Molecular flexibility both ligand and protein rigid flexible ligand and rigid protein both ligand and protein flexible search algorithm use to explore optimal positions of the ligand within the active site scoring function value should correspond to preferred binding mode efficiency very important for database searching

14 What Are Docking & Scoring? To place a ligand (small molecule) into the binding site of a receptor in the manners appropriate for optimal interactions with a receptor. To evaluate the ligand receptor interactions in a way that may discriminate the experimentally observed mode from others and estimate the binding affinity. complex ligand receptor docking scoring X ray structure & G

15 Scoring Functions A fast and simplified estimation of binding energies scores < > Gbinding Gbinding = RT ln ( K affinity ) = Gcomplex / solv Gligand / solv G protein / solv + Ginteraction T S + λ scores X ray structure? configurations of the complex

16 Types of Scoring Functions Force field based: nonbonded interaction terms as the score, sometimes in combination with solvation terms Empirical: multivariate regression methods to fit coefficients of phys ically motivated structural functions by using a training set of ligand re ceptor complexes with measured binding affinity Knowledge based: statistical atom pair potentials derived from structural databases as the score ther: scores and/or filters based on chemical properties, pharma cophore, contact, shape complementary Consensus scoring functions approach

17 Drug and Target : Lock and Key? Most drugs have to FIT well to their targets

18 Lead Finding: de novo Design Iterative building of a ligand from the structure of a protein active site? F 3C N N Cl

19 De novo design Ligand receptor interactions are determined by key groups at the surface of the active site give rise to ligand points de novo design tries to connect ligand points to give structures with complementary shape and functionality

20 De novo design advantages Compounds built directly into active site structures are not limited to set of pre existing compounds conformational space can be better explored possible to generate 1000 s of structures and hence explore molecular diversity

21 De novo design disadvantages Huge combinatorial explosion of possible structures difficult to build sensible structures additional tools required to manage large number of structures (eg. Ranking, clustering)

22 Components for de novo design Analysis of receptor site method of generating structures steric and electrostatic constraints energy based methods rule based building blocks clustering and analysis of structures synthetic accessibility?

23 Lead Finding: de novo Design Protein Building Fragments Linking new molecules fast Combinatorial explosion complexity hit ranking hit synthesis

24 Protein based Design of Combinatorial Libraries N H2 N NH2 N NH2 1. Docking scaffolds 2. Add reactants (substituents) N NH2 N NH2 3. Find conformation 4. Minimize & Score

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