Professional Development Presented to ACS Student Group Oct 16, 2013


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Lessons learned in a scientific career presented to undergraduate students mostly majoring in chemistry at UCSD on October 16, 2013

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  • Tuberculosis, which is caused by the bacterial pathogen Mycobacterium tuberculosis, is a leading cause of mortality among the infectious diseases. It has been estimated by the World Health Organization (WHO) that almost one-third of the world's population, around 2 billion people, is infected with the disease.
    Every year, more than 8 million people develop an active form of the disease, which claims the lives of nearly 2 million. This translates to over 4,900 deaths per day, and more than 95% of these are in developing countries.
    Despite the current global situation, antitubercular drugs have remained largely unchanged over the last four decades. The widespread use of these agents has provided a strong selective pressure for M.tuberculosis, thus encouraging the emergence of resistant strains.
    Multidrug resistant (MDR) tuberculosis is defined as resistance to the first-line drugs isoniazid and rifampin. The effective treatment of MDR tuberculosis necessitates long-term use of second-line drug combinations, an unfortunate consequence of which is the emergence of further drug resistance.
    Enter extensively drug resistant (XDR) tuberculosis - M.tuberculosis strains that are resistant to both isoniazid plus rifampin, as well as key second-line drugs. Since the only remaining drug classes exhibit such low potency and high toxicity, XDR tuberculosis is extremely difficult to treat.
    The rise of XDR tuberculosis around the world imposes a great threat on human health, therefore reinforcing the development of new antitubercular agents as an urgent priority.
    Very few Mtb proteins explored as drug targets
  • Multi-target therapy may be more effective than single-target therapy to treat infectious diseases
    Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics.
    GSMN-TB: Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/
    849 reactions, 739 metabolites, 726 genes
    Can optimize the model for in vivo growth
    Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)
  • Professional Development Presented to ACS Student Group Oct 16, 2013

    1. 1. From B Student to Associate Vice Chancellor & Professor Philip E. Bourne 10/16/13 ACSSA 1
    2. 2. Lesson 1 Change is Good • My high school teacher Mr. Wilson said I would be a failure at chemistry • My PhD is in chemistry • The opportunity to live in different places shaped my life • Good friends are forever
    3. 3. Lesson 2 Nurture Friends & Colleagues 50 Years Later
    4. 4. PhD – The Molecular Basis of Cancer Treatment Lesson 3 See things for what they are • I thought I was at the center of the scientific universe • I later discovered I was actually in deep space
    5. 5. Lesson 4: Follow Your Heart • Your head will tell you stuff • Your heart will tell you something different • Follow your heart Circa 1974
    6. 6. Postdoctoral Work – The Molecular Basis of How the Body Works Lesson 5 Learn to live with regret • Regrets: never learnt another language
    7. 7. How I Got Excited 10/16/13 ACSSA 7
    8. 8. Some Things Stay with You Your Whole Life 10/16/13 ACSSA 8
    9. 9. Senior Scientist – Columbia University New York • Driven not by career but wanting to live in New York City
    10. 10. The Authoring Years
    11. 11. Lesson 6 Make the most of every day
    12. 12. Got Involved with the The Human Genome – Was Only Possible by Applying Computers to Problems in Biology • Took at least 10 years and ~$1Bn • Biology’s equivalent of landing on the moon • We now have thousands of genomes • $50 genome is upon us
    13. 13. Came to UCSD to Apply Computers to Big Biological Problems • Possibly the best place in the world to do computational biology
    14. 14. Fell in Love with the Data Problem 10/16/13 ACSSA 14
    15. 15. Number of released entries Proteomics Data Its Not Just About Numbers its About Complexity 10/16/13 The Omics Revolution ACSSA Year 15 Courtesy of the RCSB Protein Data Bank
    16. 16. 10/16/13 ACSSA 16
    17. 17. 2005 - Started a New Journal to Support My Field – Led to a Passion for Open Access
    18. 18. Josh Sommer and Chordoma Disease 10/16/13 ACSSA 18
    19. 19. Josh Sommer – A Remarkable Young Man Co-founder & Executive Director the Chordoma Foundation 10/16/13 ACSSA 19 Motivation
    20. 20. Chordoma • A rare form of brain cancer • No known drugs • Treatment – surgical resection followed by intense radiation therapy 10/16/13 ACSSA 20
    21. 21. 10/16/13 ACSSA 21
    22. 22. 10/16/13 ACSSA 22
    23. 23. 10/16/13 ACSSA 23
    24. 24. 10/16/13 ACSSA 24
    25. 25. 10/16/13 ACSSA 25
    26. 26. 10/16/13 ACSSA 26
    27. 27. Lesson 7 – Go After the Big Problems 1. 2. 3. 4. 5. August 14, 2009 Can we improve how science is disseminated and comprehended? What is the ancestry of the protein structure universe and what can we learn from it? Are there alternative ways to represent proteins from which we can learn something new? What really happens when we take a drug? Can we contribute to the treatment of neglected {tropical} diseases?
    28. 28. 2. Drug Discovery 10/16/13 ACSSA 28
    29. 29. The Worst of Times Source: 10/16/13 ACSSA 29
    30. 30. Here is One Reason Why • Tykerb – Breast cancer • Gleevac – Leukemia, GI cancers • Nexavar – Kidney and liver cancer • Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive 10/16/13 30 ACSSA Collins and Workman 2006 Nature Chemical Biology 2 689-700
    31. 31. Bioinformatics – Reverse Engineering Drug Discovery Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules … Dock molecules to both primary and off-targets Statistics analysis of docking score correlations 10/16/13 ACSSA 31 Xie and Bourne 2009 Bioinformatics 25(12) 305-312
    32. 32. The Problem with Tuberculosis • • • • One third of global population infected 1.7 million deaths per year 95% of deaths in developing countries Anti-TB drugs hardly changed in 40 years • MDR-TB and XDR-TB pose a threat to human health worldwide • Development of novel, effective and inexpensive drugs is an urgent priority 10/16/13 ACSSA 32
    33. 33. Map 2 onto 1 – The TB-Drugome Similarities between the binding sites of M.tb proteins (blue), 33 10/16/13 ACSSA and binding sites containing approved drugs (red).
    34. 34. From a Drug Repositioning Perspective • Similarities between drug binding sites and TB proteins are found for 61/268 drugs • 41 of these drugs could potentially inhibit more than one TB protein chenodiol testosterone ritonavir 10/16/13 conjugated estrogens & methotrexate raloxifene levothyroxine alitretinoin No. of potential TB targets ACSSA 34
    35. 35. Top 5 Most Highly Connected Drugs Drug Intended targets Indications levothyroxine transthyretin, thyroid hormone receptor α & β-1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor alitretinoin conjugated estrogens methotrexate raloxifene 10/16/13 retinoic acid receptor RXR-α, β & γ, retinoic acid receptor cutaneous lesions in patients α, β & γ-1&2, cellular with Kaposi's sarcoma retinoic acid-binding protein 1&2 estrogen receptor menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure dihydrofolate reductase, serum albumin gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis estrogen receptor, estrogen receptor β osteoporosis in postmenopausal women ACSSA No. of TB proteins connections 14 adenylyl cyclase, argR, bioD, CRP/FNR trans. reg., ethR, glbN, glbO, kasB, lrpA, nusA, prrA, secA1, thyX, trans. reg. protein 13 adenylyl cyclase, aroG, bioD, bpoC, CRP/FNR trans. reg., cyp125, embR, glbN, inhA, lppX, nusA, pknE, purN 10 acetylglutamate kinase, adenylyl cyclase, bphD, CRP/FNR trans. reg., cyp121, cysM, inhA, mscL, pknB, sigC 10 acetylglutamate kinase, aroF, cmaA2, CRP/FNR trans. reg., cyp121, cyp51, lpd, mmaA4, panC, usp 9 adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB, pknE, Rv1347c, secA1, sigC 35
    36. 36. Rule 8 – Give Back 10/16/13 ACSSA 36
    37. 37. What Would I Work On If Starting Today? • Neuroinformatics • Translational research – interdisciplinary, lab to market • Science advocacy • Anything big data 10/16/13 ACSSA 37
    38. 38. Questions? 10/16/13 ACSSA 38