what is expected:

LSM3241: Bioinformatics and BiocomputingLSM3241: Bioinformatics and Biocomputing

Lecture 1: IntroductionLecture 1: Introduction

Prof. Chen Yu ZongProf. Chen Yu Zong

Tel: 6874-6877Tel: 6874-6877Email: Email: csccyz@nus.edu.sgcsccyz@nus.edu.sghttp://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg

Room 07-24, level 7, SOC1, Room 07-24, level 7, SOC1, National University of SingaporeNational University of Singapore

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What is Expected: 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).

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Labs, Exams and Textbook: 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:

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Topics covered: 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

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Topics covered: 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

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Topics covered: Topics covered: Lecture 5: Support vector machines for protein functionprediction (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.

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Topics covered: 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

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Bioinformatics in Life Science Research Bioinformatics in Life Science Research and Drug Discovery: 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.

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SARS CoronavirusSARS CoronavirusA novel coronavirusIdentified as the cause ofsevere respiratorysyndrome (SARS )

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SARS InfectionSARS Infection

How SARS coronavirus enters a cell and reproduce itself?

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History of SARS EpidemicsHistory of SARS EpidemicsBig question in early stages:

What is the cause ofSARS ?

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The Search of SARS pathogenThe 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

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The Search of SARS pathogenThe 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 PCRFollowed by sequence comparison with those of existing pathogens

New England Journal of Medicine 348, 1953-1966 (2003)

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The Search of SARS pathogenThe 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)

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The Search The Search of SARS pathogenof SARS pathogen

• Sequence comparison identifies a novel coronavirus as the SARS pathogen

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Sequence Comparison of Sequence Comparison of SARS coronavirus with othersSARS coronavirus with others

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SARS Coronavirus GenomeSARS Coronavirus Genome

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

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How can an enzyme metabolite escape?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

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How can an enzyme metabolite escape?How can an enzyme metabolite escape?

Metabolite unlikely escape through the entrance

How can it escape?

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How can an enzyme metabolite escape?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

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How can an enzyme metabolite escape?How can an enzyme metabolite escape?

Alternative route

An “open back door” policy:

Transient opening of a channel to allow the metabolite to escape

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Protein structureProtein structureFrom static view to dynamic viewFrom static view to dynamic view

Protein should not be viewed as a static structure

Protein flexibility is an intrinsic feature of enormous biological significance

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Modeling of protein motion Modeling of protein motion by molecular dynamics simulationby 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

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MD simulation of acetylcholinesterase 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

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Design of anti-HIV drugs Design of anti-HIV drugs

HIV virus structure

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Design of anti-HIV drugs Design of anti-HIV drugs

HIV viral genome

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Design of anti-HIV drugs Design of anti-HIV drugs

Recognition

of HIV infected cell

Vaccine-based drugs

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

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Design of anti-HIV drugs Design of anti-HIV drugs

Pathways of HIV infection and reproduction and sites of drug action

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Design of anti-HIV drugs Design of anti-HIV drugs

Selection of a target: HIV-1 protease

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Design of anti-HIV drugs Design of anti-HIV drugs

HIV-1 protease structure and cavity

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Design of anti-HIV drugs Design of anti-HIV drugs

Drug and protein:

Lock and key mechanism, blocking=>stopping of protein function

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Design of anti-HIV drugsDesign of anti-HIV drugs

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Design of anti-HIV drugsDesign 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

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..Design of HIV-1 Protease InhibitorDesign of HIV-1 Protease Inhibitor

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..Design of HIV-1 Protease InhibitorDesign of HIV-1 Protease Inhibitor

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..Design of HIV-1 Protease InhibitorDesign of HIV-1 Protease Inhibitor

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..Design of HIV-1 Protease InhibitorDesign of HIV-1 Protease Inhibitor

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Success Stories: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)

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Design of anti-SARS drugsDesign 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

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Summary of Today’s lectureSummary 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