Bioinformatica 15-12-2011-t9-t10-bio cheminformatics


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  • I have the pleasure to update you on our RNAi HTC strategy. First I will give an overview of the different cloning techniques we tried and finally the one we came up with to clone the whole C. elegans genome. Ann Lissens will focus on the RNAi screen.
  • Bioinformatica 15-12-2011-t9-t10-bio cheminformatics

    1. 2. FBW 15-12-2011 Wim Van Criekinge
    2. 3. Inhoud Lessen: Bioinformatica <ul><li>don 30-09-2010: 1* Bioinformatics (practicum 9.00-11.30) </li></ul><ul><li>don 07-10-2010: 2* Biological Databases (practicum 9.00-11.30) </li></ul><ul><li>don 21-10-2010: 3 Sequence Similarity (Scoring Matrices) </li></ul><ul><li>don 28-10-2010: 4 Sequence Alignments </li></ul><ul><li>don 04-11-2010: 5 Database Searching Fasta/Blast </li></ul><ul><li>don 25-11-2010: 6 Phylogenetics </li></ul><ul><li>don 02-12-2010: 7 Protein Structure </li></ul><ul><li>don 09-12-2010: 8 Gene Prediction, Gene Ontologies & HMM </li></ul><ul><li>don 16-12-2010: 9 ncRNA, Chip Data Analysis, AI </li></ul><ul><li>don 23-12-2010: 10 Bio- & Cheminformatics in Drug Discovery (inhaalweek) </li></ul><ul><li>Opgelet: Geen les op don 14-10-2010 en don 18-11-2010 </li></ul>
    3. 4. Examen <ul><li><html> </li></ul><ul><li><title>Examen Bioinformatica</title> </li></ul><ul><li><center> </li></ul><ul><li><head> </li></ul><ul><li><script> </li></ul><ul><li> Date(); </li></ul><ul><li>; </li></ul><ul><li>function rnd() { </li></ul><ul><li>rnd.seed = (rnd.seed*9301+49297) % 233280; </li></ul><ul><li>return rnd.seed/(233280.0); </li></ul><ul><li>}; </li></ul><ul><li>function rand(number) { </li></ul><ul><li>return Math.ceil(rnd()*number); </li></ul><ul><li>}; </li></ul><ul><li></SCRIPT> </li></ul><ul><li></head> </li></ul><ul><li><body bgcolor=&quot;#FFFFFF&quot; text=&quot;#00FF00&quot; link=&quot;#00FF00&quot;> </li></ul><ul><li><script language=&quot;JavaScript&quot;> </li></ul><ul><li>document.write('<table>'); </li></ul><ul><li>document.write('<tr>'); </li></ul><ul><li>document.write('<td><a href=&quot;index.html&quot; ><img border=0 src=&quot;' + rand(713) + '.jpg&quot; width=&quot;520&quot; height=&quot;360&quot;></a></td>'); </li></ul><ul><li>rand(98); </li></ul><ul><li>document.write('<td><a href=&quot;index.html&quot; ><img border=0 src=&quot;' + rand(713) + '.jpg&quot; width=&quot;520&quot; height=&quot;360&quot;></a></td>'); </li></ul><ul><li>rand(98); </li></ul><ul><li>document.write('<td><a href=&quot;index.html&quot; ><img border=0 src=&quot;' + rand(713) + '.jpg&quot; width=&quot;520&quot; height=&quot;360&quot;></a></td>'); </li></ul><ul><li>rand(98); </li></ul><ul><li>document.write('<td><a href=&quot;index.html&quot; ><img border=0 src=&quot;' + rand(713) + '.jpg&quot; width=&quot;520&quot; height=&quot;360&quot;></a></td>'); </li></ul><ul><li>rand(98); </li></ul><ul><li>document.write('</tr>'); </li></ul>
    4. 5. <ul><li>The keywords can be </li></ul><ul><ul><li>genome structure </li></ul></ul><ul><ul><li>gene-organisation </li></ul></ul><ul><ul><li>known promoter regions </li></ul></ul><ul><ul><li>known critical amino acid residues. </li></ul></ul><ul><li>Com bination of functional modelorganism knowledge </li></ul><ul><li>Structure-function </li></ul><ul><li>Identify similar areas of biol o gy </li></ul><ul><li>Identify orthologous pathways (might have different endpoints) </li></ul>Comparative Genomics: The biological Rosetta
    5. 7. Example: Agro Known “lethal” genes from worm, drosphila Sequence Genome Filter for drugability”, tractibility & novelty
    6. 8. Example: Extremophiles Known lipases Filter for “workable”lipases at 90 º C Sequence Genome Functional Foods Convert Highly Energetic Monosaccharides to Dextrane Look for species with interesting phenotypes Clone and produce in large quantities Washing Powder additives
    7. 10. 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
    8. 11. <ul><li>Drugs are generally defined as molecules which affect biological processes. </li></ul><ul><li>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. </li></ul><ul><li>Additionally, the molecule must be safe -- that is, metabolized and eliminated from the body without causing injury. </li></ul><ul><li>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 … </li></ul>Drug Discovery: What is a drug ?
    9. 16. <ul><li>Taxol a drug which is an unmodified natural compound, is the exception </li></ul><ul><li>Most drugs require “work” -> need for target driven pipeline </li></ul><ul><li>Humane genome is available so all target are identified </li></ul><ul><li>How to validate (within a given disease area) ? </li></ul>
    10. 17. <ul><li>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. </li></ul><ul><li>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 molecule's activity for therapeutic purposes. </li></ul><ul><li>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. </li></ul>Drug Discovery: What is a target ?
    11. 18. Phenotypic Gap <ul><li>Proposal to prioritize hypothetical protein without annotation, nice for bioinformatics and biologist </li></ul># genes with known function Total # genes Number of genes 1980 1990 2000 2010 Functional Genomics ? More than running chip experiments !
    12. 20. “ Optimal” drug target Predict side effect Where is optimal drug target ? How to correct disease state Side effects ?
    13. 27. G enome-wide RNA i RNAI vector bacteria producing ds RNA for each of the 20.000 genes proprietary nematode responding to RNA i 20.000 responses 20.000 genes insert library
    14. 30. Normal insulin signaling Reduced insulin signaling fat storage LOW fat storage HIGH Type-II Diabetes
    15. 31. <ul><li>proprietary C.elegans strains </li></ul><ul><ul><li>sensitized to silencing </li></ul></ul><ul><ul><li>sensitized to relevant pathway </li></ul></ul>Industrialized knock-downs 20,000 bacteria each containing selected C. elegans gene select genes with desired phenotypes
    16. 32. Pharma is conservative
    17. 34. Molecular functions of 26 383 human genes Structural Genomics
    18. 36. Lipinsky for the target ? Database of all “drugable” human genes
    19. 37. Drug Discovery: Design new drugs by computer ?
    20. 38. screening - the automated examination and testing of libraries of synthetic and/or organic compounds and extracts to identify potential drug leads, based on the compound's 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 Drug Discovery: Screening definitions
    21. 39. <ul><li>At the beginning of the 1990s, when the term &quot;high-throughput screening&quot; 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. </li></ul>Drug Discovery: Screening Throughput
    22. 40. Drug Discovery: HTS – The Wet Lab Roboti c arm Read-out Fluorescence / luminescence D istribution 96 / 384 wells Optical Bank for stability
    23. 41. <ul><li>Available molecules collections from pharma, chemical and agro industry, also from academics (Eastern Europe) </li></ul><ul><li>Natural products from fungi, algae, exotic plants, Chinese and ethnobotanic medicines </li></ul><ul><li>Combinatorial chemistry : it is the generation of large numbers of diverse chemical compounds (a library) for use in screening assays against disease target molecules. </li></ul><ul><li>Computer drug design (from model substrates or X-ray structure) </li></ul>Drug Discovery: Chemistry Sources
    24. 42. Drug Discovery HIT LEAD
    25. 43. <ul><li>• initial screen established </li></ul><ul><li>• Compounds screened </li></ul><ul><li>• IC 50 s established </li></ul><ul><li>• Structures verified </li></ul><ul><li>• Minimum of three independent chemical series to evaluate </li></ul><ul><li>• Positive in silico PK data </li></ul>Drug Discovery: HIT
    26. 44. <ul><li>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. </li></ul><ul><li>Such a model can be used as a template to select the most promising candidates from the library. </li></ul>Drug Discovery: Hit/lead computational approaches
    27. 45. <ul><li>lead compound - a potential drug candidate emerging from a screening process of a large library of compounds. </li></ul><ul><li>It basically affects specifically a biological process. Mechanism of activity ( reversible/ irreversible, kinetics) established </li></ul><ul><li>Its is effective at a low concentration: usually nanomolar activity </li></ul><ul><li>It is not toxic to live cells </li></ul><ul><li>It has been shown to have some in vivo activity </li></ul><ul><li>It is chemically feasible. Specificity of key compound(s) from each lead series against selected number of receptors/enzymes </li></ul><ul><li>Preliminary PK in vivo (rodent) to establish benchmark for in vitro SAR </li></ul><ul><li>In vitro PK data good predictor for in vivo activity </li></ul><ul><li>Its is of course New and Original. </li></ul>Drug Discovery: Lead ?
    28. 46. Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, Paul J. Feeney &quot;Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings&quot;: <ul><li>&quot;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 &quot;rule of 5&quot; states that: poor absorption or permeation is more likely when: </li></ul><ul><li>There are less than 5 H-bond donors (expressed as the sum of OHs and NHs); </li></ul><ul><li>The MWT is less than 500; </li></ul><ul><li>The Log P is less than 5 (or MLogP is < 4.15); </li></ul><ul><li>There are less than 10 H-bond acceptors (expressed as the sum of Ns and Os). </li></ul><ul><li>Compound classes that are substrates for biological transporters are exceptions to the rule.&quot; </li></ul>Lipinski: « rule of 5 »
    29. 47. <ul><li>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. </li></ul><ul><li>Free alternative (Chemsketch / PreADME) </li></ul>
    30. 48. Drug-like-ness (Celebrex) Methyl in this position makes it a weaker cox-2 inhibitor, but site of metabolic oxidation and ensures an acceptable clearance
    31. 49. To assist combinatorial chemistry, buy specific compunds
    32. 51. <ul><li>Structural Descriptors : (15 descriptors) </li></ul><ul><li>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 bonds </li></ul><ul><li>Topological Descriptors :(350 descriptors) </li></ul><ul><li>Topological descriptors on the adjustancy and distance matrix </li></ul><ul><li>Count descriptors </li></ul><ul><li>Kier & Hall molecular connectivity Indices </li></ul><ul><li>Kier Shape Indices </li></ul><ul><li>Galvez topological charge Indices </li></ul><ul><li>Narumi topological index </li></ul><ul><li>Autocorrelation descriptor of atomic masses, atomic polarizability, Pauling electronegativity and van der Waals radius </li></ul><ul><li>Information content descriptors </li></ul><ul><li>Electrotopological state index (E-state) </li></ul><ul><li>Atomic-Level-Based AI topological descriptors </li></ul><ul><li>Physicochemical Descriptor :(10 descriptors) </li></ul><ul><li>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 Energy </li></ul><ul><li>Geometrical Descriptor :(9 descriptors) </li></ul><ul><li>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 </li></ul>
    33. 52. <ul><li>What can you do with these descriptors ? </li></ul><ul><li>Cluster entire chemical library </li></ul><ul><ul><li>Diversity set </li></ul></ul><ul><ul><li>Focused set </li></ul></ul>Drug Discovery: Hit/lead computational approaches
    34. 53. <ul><li>Structure is known, virtual screening -> docking </li></ul><ul><li>Many different approaches </li></ul><ul><ul><li>DOCK </li></ul></ul><ul><ul><li>FlexX </li></ul></ul><ul><ul><li>Glide </li></ul></ul><ul><ul><li>GOLD </li></ul></ul><ul><li>Including conformational sampling of the ligand </li></ul><ul><li>Problem: </li></ul><ul><ul><li>host flexibility </li></ul></ul><ul><ul><li>solvatation </li></ul></ul><ul><li>Example: Bissantz et al. </li></ul><ul><ul><li>Hit rate of 10% for single scoring function </li></ul></ul><ul><ul><li>Up to 70% with triple scoring (bagging) </li></ul></ul>Drug Discovery: Docking
    35. 54. <ul><li>Given the target site: </li></ul><ul><li>Docking + structure generator </li></ul><ul><li>Specialized approach: growing substituent on a core </li></ul><ul><ul><li>LUDI </li></ul></ul><ul><ul><li>SPROUT </li></ul></ul><ul><ul><li>BOMB (biochemical and organic model builder) </li></ul></ul><ul><ul><li>SYNOPSIS </li></ul></ul><ul><li>Problem is the scoring function which is different for every protein class </li></ul>Drug Discovery: De novo design / rational drug design
    36. 55. Drug Discovery: Novel strategies using bio/cheminformatics <ul><li>HTS ? Chemical space is big (10 41 ) </li></ul><ul><li>Biased sets/focussed libraries -> bioinformatics !!! </li></ul><ul><li>How ? Use phylogenetics and known structures to define accesible (conserved) functional implicated residues to define small molecule pharmacophores (minimal requirements) </li></ul><ul><li>Desciptor search (cheminformatics) to construct/select biased compound set </li></ul><ul><li>ensure serendipity by iterative screening of these predesigned sets </li></ul>
    37. 56. Drug Discovery Toxigenomics Metabogenomics
    38. 58. <ul><li>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 </li></ul>Drug Discovery: Clinical studies
    39. 61. Drug Discovery & Development: IND filing
    40. 62. Hapmap
    41. 63. Pharmacogenomics Predictive/preventive – systems biology
    42. 64. Sneak preview Bioinformatics (re)loaded
    43. 65. Sneak preview Bioinformatics (re)loaded <ul><li>Relational datamodels </li></ul><ul><ul><li>BioSQL ( MySQL ) </li></ul></ul><ul><li>Data Visualisation </li></ul><ul><ul><li>Interface </li></ul></ul><ul><ul><ul><li>Apache </li></ul></ul></ul><ul><ul><ul><li>PHP </li></ul></ul></ul><ul><li>Large Scale Statistics </li></ul><ul><ul><li>Using R </li></ul></ul>