comp. sci. 4322 algorithms in bioinformatics 生物資訊演算法

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Comp. Sci. 4322 Algorithms in Bioinformatics 生生生生生生生 Arthur W. Chou 周周周

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Comp. Sci. 4322 Algorithms in Bioinformatics 生物資訊演算法. Arthur W. Chou 周維中. Please feel free to ask questions!. Help me know what people are not understanding Slow down the slides (We do have a lot of material). ((( ))). Stationary Hand. Wiggling Hand. - PowerPoint PPT Presentation

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Page 1: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Comp. Sci. 4322

Algorithms in Bioinformatics

生物資訊演算法

Arthur W. Chou 周維中

Page 2: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Please feel free to ask questions!

Help me know what people are not understanding

Slow down the slides

(We do have a lot of material)

Page 3: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

((( )))Stationary Hand Wiggling Hand

You have a question or comment of any

nature

You have something relevant to say to

what is being spoken spoken nownow

Page 4: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Useful Learning Techniques

Page 5: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Explaining

• We are going to test you on your ability to explain the material.

• Hence, the best way of studying is to explain the material over and over again out loud to yourself, to each other, and to your stuffed bear.

Page 6: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Day Dream

Mathematics is not all linear thinking.

Allow the essence of the material to seep into your

subconscious

Pursue ideas that percolate up and flashes of

inspiration that appear.

While going along with your day

Page 7: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Be Creative

•Ask questions.

• Why is it done this way and not that way?

Page 8: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Guesses and Counter Examples

• Guess at potential algorithms for solving a problem.

• Look for input instances for which your algorithm gives the wrong answer.

• Treat it as a game between these two players.

Page 9: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Refinement:The best solution comes from a process

of repeatedly refining and inventing alternative solutions

Page 10: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

What is Bioinformatics?

“the research, development, or application

of computational tools and approaches for

expanding the use of biological, medical,

behavioral or health data, including those to

acquire, store, organize, archive, analyze, or

visualize such data.”

(http://www.bisti.nih.gov/)

Page 11: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Why Bioinformatics?

Page 12: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

There once lived a manwho learned how to slay dragonsand give all he possessedto mastering the art.

After three yearhe was fully prepared but,alas, he found no opportunityto practice his skills.

- Zhuan Zhi

Page 13: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

As a result he began to teach how to slay dragons.

- Rene Thom

Page 14: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法
Page 15: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Sequence Analysis of gp120 on HIV-1 Virus

Highly variable levels of N-linked glycosylation on the V1 loop of

HIV-1 envelope glycoproteins and their relationship to the

immunogenicity of HIV Env of primary viral isolates

by

Arthur W. Chou and Laboratory of Nucleic Acid Vaccines at UMASS Medical School

( submitted to Journal of Virology )

Page 16: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

HIV particle and genome

envelopeenvelope gp120gp41 gp120gp41

matrixmatrix

p17p17

corecore p24p24

RTRT

RNARNA

MHCMHC

lipid bilayerlipid bilayer

Page 17: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

HIV vaccines approaches

recombinant protein (gp120)

synthetic peptides (V3)

naked DNA

live-recombinant vectors(viral, bacterial)

whole-inactivated virus

live-attenuated virus

Page 18: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

HIV env genetic subtypes

Adapted from J. MullinsAdapted from J. Mullins

Page 19: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Estimated prevalence of HIV-1 env subtypes by region

Page 20: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

HIV-1 Envelope Glycoprotein Domains

HIV sequences

Page 21: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Tertiary structure of HIV-1 gp120 protein

Page 22: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Observation

The lengths of the V1 loop vary with the number of N-glycosylation sites; i.e, the longer the V1 loop, the more the number of sites.

V1 loop:

Length No. of Glycosylation sites

15 - 25 2 - 4

26 - 45 4 - 7

Page 23: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Questions

Does this correlation between the length of the

sequences and the number of glycosylation sites only

occur in V1?

How are the glycosylation sites distributed throughout

the sequences?

Page 24: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Results of Sequence Analysis

This high correlation rarely occurs at other places: the

average number of glycosylation sites for any stretch

of length 45 is ~ 2.5 .

Analysis of the distribution of Glycosylation sites show

that they are mostly concentrated on the V1 loop.

Page 25: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Results of the paper

Mutated Env antigen with selected removal of N-

glycosylation sites at the tip of V1 loop on one of the

primary HIV-1 Env, 92US715, induced higher antibody

responses against the autologous HIV virus.

[ Contribution ] These data improve our understanding on

the structure-function relationship of HIV-1 Env

antigens, and facilitate better design of HIV vaccines

with the aim of inducing broad neutralizing antibody

responses.

Page 26: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Algorithm

Distance-Based method

Dynamic Programming

Time: 16:00 Wednesday September 28

Place: Department of Mathematics ST 516

Page 27: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

Interactions

Protein Amino Acid Sequence

DNA Sequence (Gene)

Protein 3D Structure

Protein Function

Page 28: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms

Molecular Biology Primer

Page 29: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Outline:

• 1. What Is Life Made Of?• 2. What Molecule Code For Genes?• 3. What Is the Structure Of DNA?• 4. What Carries Information between DNA

and Proteins?• 5. How are Proteins Made?

Page 30: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Section1: What is Life made of?

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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Outline For Section 1:

• All living things are made of Cells • Prokaryote, Eukaryote

• Cell Signaling• What is Inside the cell: From DNA, to RNA, to

Proteins

Page 32: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Cells

• Fundamental working units of every living system. • Every organism is composed of one of two

radically different types of cells:

prokaryotic cells or

eukaryotic cells.• Prokaryotes and Eukaryotes are descended from the

same primitive cell. • All extant prokaryotic and eukaryotic cells are the

result of a total of 3.5 billion years of evolution.

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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Cells• Chemical composition-by weight

• 70% water• 7% small molecules

• salts• Lipids• amino acids• nucleotides

• 23% macromolecules• Proteins• Polysaccharides• lipids

• biochemical (metabolic) pathways• translation of mRNA into proteins

Page 34: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Life begins with Cell

• A cell is a smallest structural unit of an organism that is capable of independent functioning

• All cells have some common features

Page 35: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Two types of cells: Prokaryotes v.s.Eukaryotes

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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Prokaryotes and Eukaryotes

•According to the most recent evidence, there are three main branches to the tree of life. •Prokaryotes include Archaea (“ancient ones”) and bacteria.•Eukaryotes are kingdom Eukarya and includes plants, animals, fungi and certain algae.

Page 37: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Prokaryotes and Eukaryotes, continued

Prokaryotes Eukaryotes

Single cell Single or multi cell

No nucleus Nucleus

No organelles Organelles

One piece of circular DNA Chromosomes

No mRNA post transcriptional modification

Exons/Introns splicing

Page 38: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Cells Information and Machinery• Cells store all information to replicate itself

• Human genome is around 3 billions base pair long• Almost every cell in human body contains same

set of genes• But not all genes are used or expressed by those

cells• Machinery:

• Collect and manufacture components• Carry out replication• Kick-start its new offspring(A cell is like a car factory)

Page 39: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Overview of organizations of life• Nucleus = library• Chromosomes = bookshelves• Genes = books• Almost every cell in an organism contains the

same libraries and the same sets of books.• Books represent all the information (DNA)

that every cell in the body needs so it can grow and carry out its vaious functions.

Page 40: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Some Terminology

• Genome: an organism’s genetic material

• Gene: a discrete units of hereditary information located on the chromosomes and consisting of DNA.

• Genotype: The genetic makeup of an organism

• Phenotype: the physical expressed traits of an organism

• Nucleic acid: Biological molecules(RNA and DNA) that allow organisms to reproduce;

Page 41: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA the Genetics Makeup• Genes are inherited and are

expressed• genotype (genetic makeup)• phenotype (physical

expression)

• On the left, is the eye’s phenotypes of green and black eye genes.

Page 42: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

More Terminology

• The genome is an organism’s complete set of DNA.• a bacteria contains about 600,000 DNA base pairs• human and mouse genomes have some 3 billion.

• human genome has 24 distinct chromosomes.• Each chromosome contains many genes.

• Gene • basic physical and functional units of heredity. • specific sequences of DNA bases that encode

instructions on how to make proteins. • Proteins

• Make up the cellular structure• large, complex molecules made up of smaller subunits

called amino acids.

Page 43: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Genome Sizes• E.Coli (bacteria) 4.6 x 106 bases• Yeast (simple fungi) 15 x 106 bases• Smallest human chromosome 50 x 106 bases• Entire human genome 3 x 109 bases

Page 44: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

All Life depends on 3 critical molecules

• DNAs• Hold information on how cell works

• RNAs• Act to transfer short pieces of information to different parts

of cell• Provide templates to synthesize into protein

• Proteins• Form enzymes that send signals to other cells and regulate

gene activity• Form body’s major components (e.g. hair, skin, etc.)

Page 45: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA: The Code of Life

• The structure and the four genomic letters code for all living organisms

• Adenine, Guanine, Thymine, and Cytosine which pair A-T and C-G on complimentary strands.

Page 46: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

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DNA, continued• DNA has a double helix

structure which composed of • sugar molecule• phosphate group• and a base (A,C,G,T)

• DNA always reads from 5’ end to 3’ end for transcription replication 5’ ATTTAGGCC 3’3’ TAAATCCGG 5’

Page 47: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA, RNA, and the Flow of Information

TranslationTranscription

Replication

Page 48: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Overview of DNA to RNA to Protein

• A gene is expressed in two steps1) Transcription: RNA synthesis2) Translation: Protein synthesis

Page 49: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Cell Information: Instruction book of Life

• DNA, RNA, and Proteins are examples of strings written in either the four-letter nucleotide of DNA and RNA (A C G T/U)

• or the twenty-letter amino acid of proteins. Each amino acid is coded by 3 nucleotides called codon. (Leu, Arg, Met, etc.)

Page 50: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms

Section 2: What Molecule Codes For Genes?

Page 51: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Outline For Section 2:

• Discovery of the Structure of DNA• Watson and Crick

• DNA Basics

Page 52: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Discovery of DNA• DNA Sequences

• Chargaff and Vischer, 1949• DNA consisting of A, T, G, C

• Adenine, Guanine, Cytosine, Thymine• Chargaff Rule

• Noticing #A#T and #G#C • A “strange but possibly meaningless”

phenomenon.• Wow!! A Double Helix

• Watson and Crick, Nature, April 25, 1953•

• Rich, 1973• Structural biologist at MIT.• DNA’s structure in atomic resolution. Crick Watson

1 Biologist1 Physics Ph.D. Student900 wordsNobel Prize

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Watson & Crick – “…the secret of life”

• Watson: a zoologist, Crick: a physicist

• “In 1947 Crick knew no biology and practically no organic chemistry or crystallography..” – www.nobel.se

• Applying Chagraff’s rules and the X-ray image from Rosalind Franklin, they constructed a “tinkertoy” model showing the double helix

• Their 1953 Nature paper: “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.”

Watson & Crick with DNA model

Rosalind Franklin with X-ray image of DNA

Page 54: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA: The Basis of Life• Deoxyribonucleic Acid (DNA)

• Double stranded with complementary strands A-T, C-G

• DNA is a polymer• Sugar-Phosphate-Base• Bases held together by H bonding to the opposite strand

Page 55: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA Components

Four nucleotide types:• Adenine• Guanine• Cytosine• Thymine

Hydrogen bonds:• A-T• C-G

Page 56: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Double helix of DNA

• James Watson and Francis Crick proposed a model for the structure of DNA.

• Utilizing X-ray diffraction data, obtained from crystals of DNA) • This model predicted that DNA

• as a helix of two complementary anti-parallel strands, • wound around each other in a rightward direction • stabilized by H-bonding between bases in adjacent strands. • The bases are in the interior of the helix

• Purine bases form hydrogen bonds with pyrimidine.

Page 57: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms

Section 3: The Structure of DNA

Page 58: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Outline For Section 3:• DNA Components

• Nitrogenous Base• Sugar• Phosphate

• Double Helix• DNA replication• Superstructure

Page 59: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA• Stores all information of life• 4 “letters” base pairs. AGTC (adenine, guanine,

thymine, cytosine ) which pair A-T and C-G on complimentary strands.

http://www.lbl.gov/Education/HGP-images/dna-medium.gif

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The Double HelixS

ourc

e: A

lber

ts e

t al

Page 61: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA, continued

Phosphate

Base (A,T, C or G)

Sugar

Page 62: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA, continued• DNA has a double helix structure. However,

it is not symmetric. It has a “forward” and “backward” direction. The ends are labeled 5’ and 3’ after the Carbon atoms in the sugar component.

5’ AATCGCAAT 3’

3’ TTAGCGTTA 5’

DNA always reads 5’ to 3’ for transcription replication

Page 63: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA Components• Nitrogenous Base:

N is important for hydrogen bonding between bases A – adenine with T – thymine (double H-bond)C – cytosine with G – guanine (triple H-bond)

• Sugar: Ribose (5 carbon)Base covalently bonds with 1’ carbonPhosphate covalently bonds with 5’ carbonNormal ribose (OH on 2’ carbon) – RNAdeoxyribose (H on 2’ carbon) – DNA dideoxyribose (H on 2’ & 3’ carbon) – used in DNA sequencing

• Phosphate:negatively charged

Page 64: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

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Basic Structure

Phosphate

Sugar

Page 65: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Basic Structure Implications• DNA is (-) charged due to phosphate:

gel electrophoresis, DNA sequencing (Sanger method)

• H-bonds form between specific bases: hybridization – replication, transcription, translation

DNA microarrays, hybridization blots, PCRC-G bound tighter than A-T due to triple H-bond

• DNA-protein interactions (via major & minor grooves): transcriptional regulation

• DNA polymerization: 5’ to 3’ – phosphodiester bond formed between 5’

phosphate and 3’ OH

Page 66: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

The Purines The Pyrimidines

Page 67: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Double helix of DNA

• The double helix of DNA has these features:• Concentration of adenine (A) is equal to thymine (T) • Concentration of cytidine (C) is equal to guanine (G). • Watson-Crick base-pairing A will only base-pair with T, and C with G

• base-pairs of G and C contain three H-bonds, • Base-pairs of A and T contain two H-bonds. • G-C base-pairs are more stable than A-T base-pairs

• Two polynucleotide strands wound around each other.• The backbone of each consists of alternating deoxyribose and

phosphate groups

Page 68: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Double helix of DNA

Page 69: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Double helix of DNA

• The DNA strands are assembled in the 5' to 3' direction• by convention, we "read" them the same way.

• The phosphate group bonded to the 5' carbon atom of one deoxyribose is covalently bonded to the 3' carbon of the next.

• The purine or pyrimidine attached to each deoxyribose projects in toward the axis of the helix.

• Each base forms hydrogen bonds with the one directly opposite it, forming base pairs (also called nucleotide pairs).

Page 70: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

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DNA - replication• DNA can replicate by

splitting, and rebuilding each strand.

• Note that the rebuilding of each strand uses slightly different mechanisms due to the 5’ 3’ asymmetry, but each daughter strand is an exact replica of the original strand.

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/D/DNAReplication.html

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DNA Replication

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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

DNA OrganizationS

ourc

e: A

lber

ts e

t al

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An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Superstructure

Lodish et al. Molecular Biology of the Cell (5th ed.). W.H. Freeman & Co., 2003.

Page 74: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Superstructure Implications• DNA in a living cell is in a highly compacted and

structured state

• Transcription factors and RNA polymerase need ACCESS to do their work

• Transcription is dependent on the structural state – SEQUENCE alone does not tell the whole story

Page 75: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms

Section 4: What carries information between DNA to Proteins

Page 76: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Outline For Section 4:• Central Dogma Of Biology• RNA• Transcription

Page 77: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

• Central Dogma (DNARNAprotein)

The paradigm that DNA directs its transcription to RNA, which is then translated into a protein.

• Transcription(DNARNA) The process which transfers genetic information from the DNA to the RNA.

• Translation(RNAprotein) The process of transforming RNA to protein as specified by the genetic code.

Page 78: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

How Do Genes Code for Proteins?

Transcription

RNA

Translation

ProteinDNA

Page 79: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Transcription

Coding sequences can be transcribed to RNA

RNA nucleotides: Similar to DNA, slightly different backbone Uracil (U) instead of Thymine (T)

Sou

rce:

Mat

hew

s &

van

Hol

de

Page 80: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

RNA Editing

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cbio course, spring 2005, Hebrew University

Translation

Page 82: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

The ribosome attaches to the mRNA at a translation initiation site

Then ribosome moves along the mRNA sequence and in the process constructs a poly-peptide

When the ribosome encounters a stop signal, it releases the mRNA. The construct poly-peptide is released, and folds into a protein.

Translation is mediated by the ribosomeRibosome is a complex of protein & rRNA molecules

Page 83: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

Sou

rce:

Alb

erts

et

al

Page 84: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

Sou

rce:

Alb

erts

et

al

Page 85: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

Sou

rce:

Alb

erts

et

al

Page 86: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

Sou

rce:

Alb

erts

et

al

Page 87: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Translation

Sou

rce:

Alb

erts

et

al

Page 88: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Genetic Code

Page 89: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

cbio course, spring 2005, Hebrew University

Transcription

RNA

Translation

ProteinDNA

The Central Dogma

Genes

Experiments

Page 90: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

An Introduction to Bioinformatics Algorithms www.bioalgorithms.info

Central Dogma of Biology

The information for making proteins is stored in DNA. There is a process (transcription and translation) by which DNA is converted to protein. By understanding this process and how it is regulated we can make predictions and models of cells.

Sequence analysis

Gene Finding

Protein Sequence Analysis

Assembly

Page 91: Comp. Sci. 4322  Algorithms in Bioinformatics 生物資訊演算法

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RNA• RNA is similar to DNA chemically. It is usually only

a single strand. T(hyamine) is replaced by U(racil)• Some forms of RNA can form secondary structures

by “pairing up” with itself. This can have change its

properties dramatically.

DNA and RNA

can pair with

each other.

http://www.cgl.ucsf.edu/home/glasfeld/tutorial/trna/trna.giftRNA linear and 3D view:

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RNA, continued

• Several types exist, classified by function• mRNA – this is what is usually being referred

to when a Bioinformatician says “RNA”. This is used to carry a gene’s message out of the nucleus.

• tRNA – transfers genetic information from mRNA to an amino acid sequence

• rRNA – ribosomal RNA. Part of the ribosome which is involved in translation.

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Transcription• The process of making

RNA from DNA• Catalyzed by

“transcriptase” enzyme• Needs a promoter

region to begin transcription.

• ~50 base pairs/second in bacteria, but multiple transcriptions can occur simultaneously

http://ghs.gresham.k12.or.us/science/ps/sci/ibbio/chem/nucleic/chpt15/transcription.gif

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DNA RNA: Transcription• DNA gets transcribed by a

protein known as RNA-polymerase

• This process builds a chain of bases that will become mRNA

• RNA and DNA are similar, except that RNA is single stranded and thus less stable than DNA• Also, in RNA, the base uracil (U) is

used instead of thymine (T), the DNA counterpart

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Transcription, continued• Transcription is highly regulated. Most DNA is in a

dense form where it cannot be transcribed. • To begin transcription requires a promoter, a small

specific sequence of DNA to which polymerase can bind (~40 base pairs “upstream” of gene)

• Finding these promoter regions is a partially solved problem that is related to motif finding.

• There can also be repressors and inhibitors acting in various ways to stop transcription. This makes regulation of gene transcription complex to understand.

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Definition of a Gene

• Regulatory regions: up to 50 kb upstream of +1 site

• Exons: protein coding and untranslated regions (UTR)1 to 178 exons per gene (mean 8.8)8 bp to 17 kb per exon (mean 145 bp)

• Introns: splice acceptor and donor sites, junk DNAaverage 1 kb – 50 kb per intron

• Gene size: Largest – 2.4 Mb (Dystrophin). Mean – 27 kb.

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Splicing

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Splicing and other RNA processing• In Eukaryotic cells, RNA is processed

between transcription and translation.• This complicates the relationship between a

DNA gene and the protein it codes for.• Sometimes alternate RNA processing can

lead to an alternate protein as a result. This is true in the immune system.

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Splicing (Eukaryotes)• Unprocessed RNA is

composed of Introns and Extrons. Introns are removed before the rest is expressed and converted to protein.

• Sometimes alternate splicings can create different valid proteins.

• A typical Eukaryotic gene has 4-20 introns. Locating them by analytical means is not easy.

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Section 5: How Are Proteins Made?(Translation)

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Outline For Section 5:

• mRNA• tRNA• Translation• Protein Synthesis• Protein Folding

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Terminology for Ribosome• Codon: The sequence of 3 nucleotides in DNA/RNA that

encodes for a specific amino acid. • mRNA (messenger RNA): A ribonucleic acid whose

sequence is complementary to that of a protein-coding gene in DNA.

• Ribosome: The organelle that synthesizes polypeptides under the direction of mRNA

• rRNA (ribosomal RNA):The RNA molecules that constitute the bulk of the ribosome and provides structural scaffolding for the ribosome and catalyzes peptide bond formation.

• tRNA (transfer RNA): The small L-shaped RNAs that deliver specific amino acids to ribosomes according to the sequence of a bound mRNA.

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mRNA Ribosome• mRNA leaves the nucleus via nuclear

pores.• Ribosome has 3 binding sites for tRNAs:

• A-site: position that aminoacyl-tRNA molecule binds to vacant site

• P-site: site where the new peptide bond is formed.

• E-site: the exit site• Two subunits join together on a mRNA

molecule near the 5’ end. • The ribosome will read the codons until

AUG is reached and then the initiator tRNA binds to the P-site of the ribosome.

• Stop codons have tRNA that recognize a signal to stop translation. Release factors bind to the ribosome which cause the peptidyl transferase to catalyze the addition of water to free the molecule and releases the polypeptide.

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Terminology for tRNA and proteins• Anticodon: The sequence of 3 nucleotides in

tRNA that recognizes an mRNA codon through complementary base pairing.

• C-terminal: The end of the protein with the free COOH.

• N-terminal: The end of the protein with the free NH3.

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Purpose of tRNA

• The proper tRNA is chosen by having the corresponding anticodon for the mRNA’s codon.

• The tRNA then transfers its aminoacyl group to the growing peptide chain.

• For example, the tRNA with the anticodon UAC corresponds with the codon AUG and attaches methionine amino acid onto the peptide chain.

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Translation: tRNA

• Carboxyl end of the protein is released from the tRNA at the Psite and joined to the free amino group from the amino acid attached to the tRNA at the A-site; new peptide bond formed catalyzed by peptide transferase.

• Conformational changes occur which shift the two tRNAs into the E-site and the P-site from the P-site and A-site respectively. The mRNA also shifts 3 nucleotides over to reveal the next codon.

• The tRNA in the E-site is released• GTP hydrolysis provides the energy to drive this reaction.

mRNA is translated in 5’ to 3’ direction and the from N-terminal to C-terminus of the polypeptide.Elongation process (assuming polypeptide already began):

tRNA with the next amino acid in the chain binds to the A-site by forming base pairs with the codon from mRNA

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Uncovering the code

• Scientists conjectured that proteins came from DNA; but how did DNA code for proteins?

• If one nucleotide codes for one amino acid, then there’d be 41 amino acids

• However, there are 20 amino acids, so at least 3 bases codes for one amino acid, since 42 = 16 and 43 = 64• This triplet of bases is called a “codon”• 64 different codons and only 20 amino acids means that

the coding is degenerate: more than one codon sequence code for the same amino acid

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Revisiting the Central Dogma• In going from DNA to proteins,

there is an intermediate step where mRNA is made from DNA, which then makes protein• This known as The Central

Dogma• Why the intermediate step?

• DNA is kept in the nucleus, while protein sythesis happens in the cytoplasm, with the help of ribosomes

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The Central Dogma (cont’d)

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RNA Protein: Translation• Ribosomes and transfer-RNAs (tRNA) run along the

length of the newly synthesized mRNA, decoding one codon at a time to build a growing chain of amino acids (“peptide”)• The tRNAs have anti-codons, which complimentarily match

the codons of mRNA to know what protein gets added next• But first, in eukaryotes, a phenomenon called

splicing occurs• Introns are non-protein coding regions of the mRNA; exons

are the coding regions• Introns are removed from the mRNA during splicing so that

a functional, valid protein can form

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Translation• The process of going

from RNA to polypeptide.

• Three base pairs of RNA (called a codon) correspond to one amino acid based on a fixed table.

• Always starts with Methionine and ends with a stop codon

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Translation, continued• Catalyzed by Ribosome• Using two different

sites, the Ribosome continually binds tRNA, joins the amino acids together and moves to the next location along the mRNA

• ~10 codons/second, but multiple translations can occur simultaneously

http://wong.scripps.edu/PIX/ribosome.jpg

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Protein Synthesis: Summary• There are twenty amino

acids, each coded by three- base-sequences in DNA, called “codons”• This code is degenerate

• The central dogma describes how proteins derive from DNA• DNA mRNA (splicing?)

protein• The protein adopts a 3D

structure specific to it’s amino acid arrangement and function

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Proteins

• Complex organic molecules made up of amino acid subunits

• 20* different kinds of amino acids. Each has a 1 and 3 letter abbreviation.

• http://www.indstate.edu/thcme/mwking/amino-acids.html for complete list of chemical structures and abbreviations.

• Proteins are often enzymes that catalyze reactions.• Also called “poly-peptides”

*Some other amino acids exist but not in humans.

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Protein Folding• Proteins tend to fold into the lowest

free energy conformation.• Proteins begin to fold while the

peptide is still being translated.• Proteins bury most of its hydrophobic

residues in an interior core to form an α helix.

• Most proteins take the form of secondary structures α helices and β sheets.

• Molecular chaperones, hsp60 and hsp 70, work with other proteins to help fold newly synthesized proteins.

• Much of the protein modifications and folding occurs in the endoplasmic reticulum and mitochondria.

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Protein Structure

Proteins are poly-peptides of 70-3000 amino-acids

This structure is (mostly) determined by the sequence of amino-acids that make up the protein

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Protein Structure

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Protein Folding

• Proteins are not linear structures, though they are built that way

• The amino acids have very different chemical properties; they interact with each other after the protein is built• This causes the protein to start fold and adopting it’s

functional structure• Proteins may fold in reaction to some ions, and several

separate chains of peptides may join together through their hydrophobic and hydrophilic amino acids to form a polymer

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Protein Folding (cont’d)

• The structure that a protein adopts is vital to it’s chemistry

• Its structure determines which of its amino acids are exposed carry out the protein’s function

• Its structure also determines what substrates it can react with