Bioinformatica t9-t10-biocheminformatics


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Bioinformatica t9-t10-biocheminformatics

  1. 1. FBW 11-12-2012Wim Van Criekinge
  2. 2. Inhoud Lessen: Bioinformatica GEEN LES
  4. 4. Examen <html> <title>Examen Bioinformatica</title> <center> <head> <script> Date();; function rnd() { rnd.seed = (rnd.seed*9301+49297) % 233280; return rnd.seed/(233280.0); }; function rand(number) { return Math.ceil(rnd()*number); }; </SCRIPT> </head> <body bgcolor="#FFFFFF" text="#00FF00" link="#00FF00"> <script language="JavaScript"> document.write(<table>); document.write(<tr>); document.write(<td><a href="index.html" ><img border=0 src=" + rand(713) + .jpg" width="520" height="360"></a></td>); rand(98); document.write(<td><a href="index.html" ><img border=0 src=" + rand(713) + .jpg" width="520" height="360"></a></td>); rand(98); document.write(<td><a href="index.html" ><img border=0 src=" + rand(713) + .jpg" width="520" height="360"></a></td>); rand(98); document.write(<td><a href="index.html" ><img border=0 src=" + rand(713) + .jpg" width="520" height="360"></a></td>); rand(98);
  5. 5. Comparative Genomics: The biological Rosetta • The keywords can be – genome structure – gene-organisation – known promoter regions – known critical amino acid residues. • Combination of functional modelorganism knowledge • Structure-function • Identify similar areas of biology • Identify orthologous pathways (might have different endpoints)
  6. 6. Example: Agro Sequence Genome Known “lethal” genes from worm, drosphila Filter for drugability”, tractibility & novelty
  7. 7. Example: ExtremophilesLook for species Sequence Genomewith interestingphenotypesFunctional FoodsConvert Highly Energetic Monosaccharides to DextraneWashing Powder additives Known lipases Filter for “workable”lipases at 90º C Clone and produce in large quantities
  8. 8. Drug Discovery: Design new drugs by computer? Problem: pipeline cost rise linear, NCE steady Money: bypassing difficult, work on attrition Every step requires specific computational tools
  9. 9. Drug Discovery: What is a drug ? • Drugs are generally defined as molecules which affect biological processes. • In order to be effective, the molecule must be present in the body at an adequate concentration for it to act at the specific site in the body where it can exert its effect. • Additionally, the molecule must be safe -- that is, metabolized and eliminated from the body without causing injury. • Assumption: next 50 years still a big market in small chemical entities which can be administered orally in form of a pill (in contrast to antibodies) or gene therapy …
  10. 10. • Taxol a drug which is an unmodified natural compound, is the exception• Most drugs require “work” -> need for target driven pipeline• Humane genome is available so all target are identified• How to validate (within a given disease area) ?
  11. 11. Drug Discovery: What is a target ? • target - a molecule (often a protein) that is instrumental to a disease process (though not necessarily directly involved), which may be targeted with a potential therapeutic. • target identification - identifying a molecule (often a protein) that is instrumental to a disease process (though not necessarily directly involved), with the intention of finding a way to regulate that molecules activity for therapeutic purposes. • target validation - a crucial step in the drug development process. Following the identification of a potential disease target, target validation verifies that a drug that specifically acts on the target can have a significant therapeutic benefit in the treatment of a given disease.
  12. 12. Functional Genomics ? More than running chip experiments ! Phenotypic Gap Total # genes Proposal to prioritize hypothetical protein Number of genes without annotation, nice for bioinformatics and biologist # genes with known function 1980 1990 2000 2010
  13. 13. Where is optimal drug target ? “Optimal” drug target Predict side effectHow to correct disease stateSide effects ?
  14. 14. Genome-wide RNAi RNAI vector proprietary nematode20.000 genes insert bacteria producing ds RNA for responding to RNAi library each of the 20.000 genes 20.000 responses
  15. 15. Type-II DiabetesNormal insulin signaling fat storage LOWReduced insulin signaling fat storage HIGH
  16. 16. Industrialized knock-downs proprietary C.elegans strains • sensitized to silencing • sensitized to relevant pathway20,000 bacteriaeach containingselectedC. elegans gene select genes with desired phenotypes
  17. 17. Pharma is conservative
  18. 18. Structural Genomics Molecular functions of 26 383 human genes
  19. 19. Lipinsky for the target ? Database of all “drugable” human genes
  20. 20. Drug Discovery: Design new drugs by computer?
  21. 21. Drug Discovery: Screening definitions screening - the automated examination and testing of libraries of synthetic and/or organic compounds and extracts to identify potential drug leads, based on the compounds binding affinity for a target molecule. screening library - a large collection of compounds with different chemical properties or shapes, generated either by combinatorial chemistry or some other process or by collecting samples with interesting biological properties. High Throughput Screening: Quick and Dirty… from 5000 compounds per day
  22. 22. Drug Discovery: Screening Throughput • At the beginning of the 1990s, when the term "high-throughput screening" was coined, a department of 20 would typically be able to screen around 1.5 million samples in a year, each researcher handling around 75,000 samples. Today, four researchers using fully automated robotic technology can screen 50,000 samples a day, or around 2.5 million samples each year.
  23. 23. Drug Discovery: HTS – The Wet Lab Distribution 96 / 384 wells Read-out Fluorescence / luminescence Robotic arm Optical Bank for stability
  24. 24. Drug Discovery: Chemistry Sources • Available molecules collections from pharma, chemical and agro industry, also from academics (Eastern Europe) • Natural products from fungi, algae, exotic plants, Chinese and ethnobotanic medicines • Combinatorial chemistry: it is the generation of large numbers of diverse chemical compounds (a library) for use in screening assays against disease target molecules. • Computer drug design (from model substrates or X-ray structure)
  25. 25. Drug Discovery HIT LEAD
  26. 26. Drug Discovery: HIT • initial screen established • Compounds screened • IC50s established • Structures verified • Minimum of threeindependent chemicalseries to evaluate • Positive in silico PKdata
  27. 27. Drug Discovery: Hit/lead computational approaches • When the structure of the target is unknown, the activity data can be used to construct a pharmacophore model for the positioning of key features like hydrogen-bonding and hydrophobic groups. • Such a model can be used as a template to select the most promising candidates from the library.
  28. 28. Drug Discovery: Lead ? • lead compound - a potential drug candidate emerging from a screening process of a large library of compounds. • It basically affects specifically a biological process. Mechanism of activity (reversible/irreversible,kinetics) established • Its is effective at a low concentration: usually nanomolar activity • It is not toxic to live cells • It has been shown to have some in vivo activity • It is chemically feasible. Specificity of keycompound(s) fromeach leadseriesagainst selectednumber ofreceptors/enzymes • Preliminary PK in vivo(rodent) to establishbenchmark for in vitroSAR • In vitro PK data goodpredictor for in vivoactivity • Its is of course New and Original.
  29. 29. Lipinski: « rule of 5 »"In the USAN set we found that the sum of Ns and Os in the molecular formula was greater than 10 in 12% of the compounds. Eleven percent of compounds had a MWT of over 500. Ten percent of compounds had a CLogP larger than 5 (or an MLogP larger than 4.15) and in 8% of compounds the sum of OHs and NHs in the chemical structure was larger than 5. The "rule of 5" states that: poor absorption or permeation is more likely when:A. There are less than 5 H-bond donors (expressed as the sum of OHs and NHs);B. The MWT is less than 500;C. The LogP is less than 5 (or MLogP is < 4.15);D. There are less than 10 H-bond acceptors (expressed as the sum of Ns and Os).Compound classes that are substrates for biological transporters are exceptions to the rule."Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, Paul J. Feeney"Experimental and computational approaches to estimate solubility andpermeability in drug discovery and development settings":
  30. 30. • A quick sketch with ChemDraw, conversion to a 3D structure with Chem3D, and processing by QuikProp, reveals that the problem appears to be poor cell permeability for this relatively polar molecule, with predicted PCaco and PMDCK values near 10 nm/s.• Free alternative (Chemsketch / PreADME)
  31. 31. (Celebrex)Methyl in this position makes it a weaker cox-2 inhibitor,but site of metabolic oxidation and ensures an acceptable clearance Drug-like-ness
  32. 32. To assist combinatorial chemistry, buy specific compunds
  33. 33. Structural Descriptors: (15 descriptors)Molecular Formula, Molecular Weight, Formal Charge, The Number of Rotatable Bonds, The Number of Rigid Bonds, The Number of Rings, The Number of Aromatic Rings, The Number of H Bond Acceptors, The Number of H Bond Donors, The Number of (+) Charged Groups, The Number of (-) Charged Groups, No. single, double, triple, aromatic bondsTopological Descriptors:(350 descriptors)• Topological descriptors on the adjustancy and distance matrix• Count descriptors• Kier & Hall molecular connectivity Indices• Kier Shape Indices• Galvez topological charge Indices• Narumi topological index• Autocorrelation descriptor of atomic masses, atomic polarizability, Pauling electronegativity and van der Waals radius• Information content descriptors• Electrotopological state index (E-state)• Atomic-Level-Based AI topological descriptorsPhysicochemical Descriptor:(10 descriptors)AlogP98 (calculated logP), SKlogP (calculated logP), SKlogS in pure water (calculated water solubility), SKlogS in buffer system (calculated water solubility),SK vap (calculated vapor pressure), SK bp (calculated boiling point), SK mp (calculated meling point), AMR (calculated molecular refractivity), APOL(calculated polarizability), Water Solvation Free EnergyGeometrical Descriptor:(9 descriptors)Topological Polar Surface Area, 2D van der Waals Volume, 2D van der Waals Surface Area, 2D van der Waals Hydrophobic Surface Area, 2D van der Waals Polar Surface Area, 2D van der Waals H-bond Acceptor Surface Area, 2D van der Waals H-bond Donor Surface Area, 2D van der Waals (+) Charged Groups Surface Area, 2D van der Waals (-) Charged Groups Surface Area
  34. 34. Drug Discovery: Hit/lead computational approaches • What can you do with these descriptors ? • Cluster entire chemical library – Diversity set – Focused set
  35. 35. Drug Discovery: Docking • Structure is known, virtual screening -> docking • Many different approaches – DOCK – FlexX – Glide – GOLD • Including conformational sampling of the ligand • Problem: – host flexibility – solvatation • Example: Bissantz et al. – Hit rate of 10% for single scoring function – Up to 70% with triple scoring (bagging)
  36. 36. Drug Discovery: De novo design / rational drug design • Given the target site: • Docking + structure generator • Specialized approach: growing substituent on a core – LUDI – SPROUT – BOMB (biochemical and organic model builder) – SYNOPSIS • Problem is the scoring function which is different for every protein class
  37. 37. Drug Discovery: Novel strategies using bio/cheminformatics - HTS ? Chemical space is big (1041) - Biased sets/focussed libraries ->bioinformatics !!! - How ? Use phylogenetics and known structures to define accesible (conserved) functional implicated residues to define small molecule pharmacophores (minimal requirements) - Desciptor search (cheminformatics) to construct/select biased compound set - ensure serendipity by iterative screening of these predesigned sets
  38. 38. Drug Discovery Toxigenomics Metabogenomics
  39. 39. Drug Discovery: Clinical studies • Preclinical - An early phase of development including initial safety assessment Phase I - Evaluation of clinical pharmacology, usually conducted in volunteers Phase II - Determination of dose and initial evaluation of efficacy, conducted in a small number of patients Phase III - Large comparative study (compound versus placebo and/or established treatment) in patients to establish clinical benefit and safety Phase IV - Post marketing study
  40. 40. Drug Discovery & Development: IND filing
  41. 41. Hapmap
  42. 42. Pharmacogenomics Predictive/preventive – systems biology
  43. 43. Sneak previewBioinformatics (re)loaded
  44. 44. Sneak preview Bioinformatics (re)loaded• Relational datamodels – BioSQL (MySQL)• Data Visualisation – Interface • Apache • PHP• Large Scale Statistics – Using R