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Help me know what people are not understanding
Slow down the slides
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((( )))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
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.
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
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.
Refinement:The best solution comes from a process
of repeatedly refining and inventing alternative solutions
What is Computational Biology?
“What is Computational Biology? - The
development and application of data-
analytical and theoretical methods,
mathematical modeling and
computational simulation techniques
to the study of biological, behavioral,
and social systems.”
(http://www.bisti.nih.gov/)
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
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 )
HIV particle and genome
envelopeenvelope gp120gp41 gp120gp41
matrixmatrix
p17p17
corecore p24p24
RTRT
RNARNA
MHCMHC
lipid bilayerlipid bilayer
HIV vaccines approaches
recombinant protein (gp120)
synthetic peptides (V3)
naked DNA
live-recombinant vectors(viral, bacterial)
whole-inactivated virus
live-attenuated virus
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
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?
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.
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.
Algorithm
Distance-Based method
Dynamic Programming
Time: 16:00 Wednesday September 28
Place: Department of Mathematics ST 516
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?
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
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.
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
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Two types of cells: Prokaryotes v.s.Eukaryotes
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.
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
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)
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.
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;
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.
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.
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
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.)
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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’
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
DNA, RNA, and the Flow of Information
TranslationTranscription
Replication
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
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.)
www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms
Section 2: What Molecule Codes For Genes?
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline For Section 2:
• Discovery of the Structure of DNA• Watson and Crick
• DNA Basics
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
DNA Components
Four nucleotide types:• Adenine• Guanine• Cytosine• Thymine
Hydrogen bonds:• A-T• C-G
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline For Section 3:• DNA Components
• Nitrogenous Base• Sugar• Phosphate
• Double Helix• DNA replication• Superstructure
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
The Double HelixS
ourc
e: A
lber
ts e
t al
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
DNA, continued
Phosphate
Base (A,T, C or G)
Sugar
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
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
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
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
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).
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
DNA OrganizationS
ourc
e: A
lber
ts e
t al
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Superstructure
Lodish et al. Molecular Biology of the Cell (5th ed.). W.H. Freeman & Co., 2003.
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
www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms
Section 4: What carries information between DNA to Proteins
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline For Section 4:• Central Dogma Of Biology• RNA• Transcription
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.
cbio course, spring 2005, Hebrew University
How Do Genes Code for Proteins?
Transcription
RNA
Translation
ProteinDNA
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
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
cbio course, spring 2005, Hebrew University
Transcription
RNA
Translation
ProteinDNA
The Central Dogma
Genes
Experiments
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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:
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms
Section 5: How Are Proteins Made?(Translation)
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline For Section 5:
• mRNA• tRNA• Translation• Protein Synthesis• Protein Folding
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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.
cbio course, spring 2005, Hebrew University
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
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