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What is Expected:

LSM3241: Bioinformatics and Biocomputing Lecture 1: Introduction Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. What is Expected:. To learn the most-widely used bioinformatics tools

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What is Expected:

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  1. LSM3241: Bioinformatics and BiocomputingLecture 1: IntroductionProf. Chen Yu ZongTel: 6874-6877Email: csccyz@nus.edu.sghttp://xin.cz3.nus.edu.sgRoom 07-24, level 7, SOC1, National University of Singapore

  2. What is Expected: To learn the most-widely used bioinformatics tools • Basic understanding of the method in each tool (normally required in a college module) • Capable of explaining the algorithm to a layperson (so that you are perceived as an expert!) • Knowing the application range and limitation of each tool (now the real expert!) To learn through real-case studies, focused on applications and problem solving: • Lectures, labs, tutorials oriented toward real-case studies (we have an “open-lab” policy). • Study of real and recently-emerged biological problems, virus research, drug design, systems biology (to give you the experience to work for a life-science lab or a pharmaceutical company).

  3. Labs, Exams and Textbook: “Open-lab” policy: • Our lab assignments only uses internet tools and downloadable software (which means that you can do the projects “any-time, any-place”) • You do not have to show-up in your lab, as long as you submit lab-report on time. • Project-report submission system at: http://bidd.nus.edu.sg/lsm3241/upload.htm Exams: • 2 Projects (25% each), 1 Final (open-book, 50%). Textbook: • As most of the topics are not covered by existing textbooks, you are not required to have a textbook. The following are recommended reference books: • Introduction to Bioinformatics. Arthur M. Lesk. 2002. Oxford University Press; ISBN: 0199251967 • Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning). Pierre Baldi, Soren Brunak. 2001. The MIT Press; ISBN: 026202506X • Molecular modelling : principles and applications. Andrew R. Leach. Imprint Harlow, England; Singapore:

  4. Topics covered: Lecture 1: Introduction (week1): • Examples of bioinformatics tools applied to real-life biological and drug design problems • Identification of SARS pathogen. • How a protein substrate escape? • Computer aided drug design Lecture 2: Bioinformatics of viral genome (week2): • Viral genome database • Protein annotation. • Protein inhibitors. Note: Please do not just listen. Get familiar with the biology-side of the topics in advance

  5. Topics covered: Lecture 3: Molecular database development (week3): • Protein inhibitor search • Getting chem-info about inhibitors. • 2D and 3D structures. • Database construction Lecture 4: Sequence analysis (week4): • Sequence alignment methods revisited (pair-wise, BLAST, MSA, PSI-BLAST) • Identification of a novel coronavirus as the SARS pathogen. Project 1: Functional prediction of proteins in viral genomes by PSI-BLAST and SVM (25%) (week3-6) Note: Please do not just listen. Get familiar with the biology-side of the topics in advance

  6. Topics covered: Lecture 5: Support vector machines for protein function prediction (week5): • Support vector machines method for protein function prediction • Use of SVMProt for protein function prediction. Lecture 6: Fundamentals of molecular modeling (week6): • Structural organization of a molecule. • Basic interactions and models • Modeling methods (conformation search, energy minimization) Lecture 7: Modeling software (week7): • Learn to use a modeling software.

  7. Topics covered: Lecture 8: Gene Expression profiles and microarray data analysis (week8) Lecture 9: Clustering analysis of microarray data Project 2: Clustering analysis of microarray data from GEO database (25%) (week9-11). Lecture 10: Biological pathway simulation

  8. Bioinformatics in Life Science Research and Drug Discovery: Examples: • Identification of a novel coronavirus as the SARS pathogen. • How a metabolite escape from a protein? • Design of anti-HIV drugs Note: • Learn from these examples how bioinformatics tools can be used to solve biological and drug design problems, which tool to use. • Also pay attention to the biological nature of each problem.

  9. SARS Coronavirus A novel coronavirus Identified as the cause of severe respiratory syndrome (SARS )

  10. SARS Infection How SARS coronavirus enters a cell and reproduce itself?

  11. History of SARS Epidemics Big question in early stages: What is the cause of SARS ?

  12. The Search of SARS pathogen • Suspect groups: • A broad range of viral, bacterial, chlamydial,and rickettsial agents that likely to cause the SARS symptoms • Chief suspects: • Versinia, mycoplasma, chlamydia, legionella, coxiella burnetii • spotted fever and typhus group rickettsiae,influenzaviruses A and B, Paramyxovirinae and Pneumovirinae subfamily viruses (specifically, human respiratory syncytial virus and human metapneumovirus), Mastadenoviridae, Herpetoviridae,Picornaviridae, Old and New World hantaviruses, and Old World arenaviruses. • Consider yourself as a detective, how to solve a crime? • Identify Suspect and Come up with Search Strategies

  13. The Search of SARS pathogen • Traditional detection methods: • Virus isolation in suckling mice and cell culture • Electron microscopy • Histopathological examination • Serologic analysis • General and specialized bacterial culture techniques • Molecular detection methods: • Polymerase chain reaction (PCR) • Reverse-transcription PCR (RT-PCR) • Real-time PCR • Followed by sequence comparison with those of existing pathogens • New England Journal of Medicine 348, 1953-1966 (2003)

  14. The Search of SARS pathogen • Findings from molecular detection: • A 405-nucleotide segment of the coronavirus polymerase gene open reading frame 1b was amplified from the isolation material by RT-PCR with the broadly reactive primer set IN-2–IN-4. In contrast, this primer set produced no specific band against uninfected cells. • When compared with other human and animal coronaviruses, the nucleotide and deduced amino acid sequence from this region had similarity scores ranging from 0.56 to 0.63 and from 0.57 to 0.74, respectively. The highest sequence similarity was obtained with group II coronaviruses. • New England Journal of Medicine 348, 1953-1966 (2003)

  15. The Search of SARS pathogen • Sequence comparison identifies a novel coronavirus as the SARS pathogen

  16. Sequence Comparison of SARS coronavirus with others

  17. SARS Coronavirus Genome Get familiar with all the known genes (genome location, sequence, function. Where to get these info?

  18. How can an enzyme metabolite escape? The enzyme acetylcholinesterase generates a strong electrostatic field that can attract the cationic substrate acetylcholine to the active site. However, the long and narrow active site gorge seems inconsistent with the enzyme's high catalytic rate. E + S  E + P How does the metabolite P escape? Acetylcholinesterase (AChE) is the enzyme responsible for the termination of signaling in cholinergic synapses (such as the neuromuscular junction) by degrading the neurotransmitter acetylcholine. AChE has a gorge, 2 nm deep, leading to the catalytic site

  19. How can an enzyme metabolite escape? Metabolite unlikely escape through the entrance How can it escape?

  20. How can an enzyme metabolite escape? • How can it escape? • Can you tell which of the following possibilities is likely or unlikely, and why? • Protein unfolding • Condensation of ions on protein surface to counter-balance the force • Change of electric charge on metabolite • Alternative escape route

  21. How can an enzyme metabolite escape? Alternative route An “open back door” policy: Transient opening of a channel to allow the metabolite to escape

  22. Protein structureFrom static view to dynamic view Protein should not be viewed as a static structure Protein flexibility is an intrinsic feature of enormous biological significance

  23. Modeling of protein motion by molecular dynamics simulation Protein motion can be simulated by means of molecular dynamics simulations: Trajectory of atom movement is determined by Newton’s second law: F=ma x(t)=x(0)+vt+1/2 a t2 Typical MD software: AMBER, CHARM, TINKER TINKER is freely downloadable

  24. MD simulation of acetylcholinesterase MD simulation clearly reveals transient opening of a channel “back door” Science 263, 1276-1278 (1994) The open “back door”allows the metabolite Pto escape

  25. Design of anti-HIV drugs HIV virus structure

  26. Design of anti-HIV drugs HIV viral genome

  27. Design of anti-HIV drugs Recognition of HIV infected cell Vaccine-based drugs

  28. Design of anti-HIV drugs Pathways of HIV infection and reproduction and sites of drug action

  29. Design of anti-HIV drugs Pathways of HIV infection and reproduction and sites of drug action

  30. Design of anti-HIV drugs Selection of a target: HIV-1 protease

  31. Design of anti-HIV drugs HIV-1 protease structure and cavity

  32. Design of anti-HIV drugs Drug and protein: Lock and key mechanism, blocking=>stopping of protein function

  33. Design of anti-HIV drugs

  34. Design of anti-HIV drugs • Drug design: • Step 1: Finding the right target in the genome • A key protein involved in viral cycle (stop the disease process) • Different from human proteins (reduce side-effects) • Step 2: Finding or making a chemical agent to stop the protein • In majority of cases: protein inhibitors • Step 3: Test and clinical trials

  35. . Design of HIV-1 Protease Inhibitor

  36. . Design of HIV-1 Protease Inhibitor

  37. . Design of HIV-1 Protease Inhibitor

  38. . Design of HIV-1 Protease Inhibitor

  39. Success Stories: • HIV-1 Protease Inhibitors in the market: • Inverase (Hoffman-LaRoche, 1995) • Norvir (Abbot, 1996) • Crixivan (Merck, 1996) • Viracept (Agouron, 1997) Drug discovery today 2, 261-272 (1997)

  40. Design of anti-SARS drugs Pathways of SARS infection and reproduction and sites of drug action. Research works underway But the efforts have cooled down due to the “elimination” of this virus

  41. Summary of Today’s lecture • Bioinformatics tools in real-life biological research and drug design problems • Tools include: • Sequence analysis • Microarray data analysis (relatively new, not covered) • Molecular modeling • Computer-aided drug design

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