BCB 444/544
Lecture 15
RNA, Proteins,
Promoters, TFs
More Review:
Next time: Profiles &
Hidden Markov Models (HMMs)
#15_Sept26
BCB 444/544 F07 ISU Dobbs #15 - RNA, Proteins, Promoters, TFs
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Required Reading
(before lecture)
Mon Sept 24 - Lecture 14
Review: Nucleus, Chromosomes, Genes, RNAs, Proteins
Surprise lecture: No assigned reading
Wed Sept 26 - Lecture 15
Profiles & Hidden Markov Models
• Chp 6 - pp 79-84
• Eddy: What is a hidden Markov Model?
2004 Nature Biotechnol 22:1315
http://www.nature.com/nbt/journal/v22/n10/abs/nbt1004-1315.html
Thurs Sept 27 - Lab 4 & Fri Sept 28 - Lecture 16
Protein Families, Domains, and Motifs
• Chp 7 - pp 85-96
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Assignments & Announcements
Wed Sept 26
• Exam 1
• HW#2
•
•
- Graded & returned in class
- Graded & returned in class
Answer KEYs posted on website
Grades posted on WebCT
• HomeWork #3 - posted online
Due: Mon Oct 8 by 5 PM
• HW544Extra #1 - posted online
Due: Task 1.1 - Mon Oct 1 by noon
Task 1.2 & Task 2 - Mon Oct 8 by 5 PM
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BCB 544 - Extra Required Reading
Mon Sept 24
BCB 544 Extra Required Reading Assignment:
• Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS, Katzman S, King
B, Onodera C, Siepel A, Kern AD, Dehay C, Igel H, Ares M Jr, Vanderhaeghen P, Haussler
D. (2006) An RNA gene expressed during cortical development evolved rapidly in humans.
Nature 443: 167-172.
• http://www.nature.com/nature/journal/v443/n7108/abs/nature05113.html
• doi:10.1038/nature05113
• PDF available on class website - under Required Reading Link
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Cell & Molecular Biology: the Basics
Slide Credits: Terribilini, 06; &
some adapted from Erin Garland
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Eukaryotic Cell
• Enclosed & subdivided by membranes
• Several compartments called
organelles
• Multiple linear chromosomes in
nucleus
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Prokaryotic Cell
•
•
•
•
Enclosed by membrane & cell wall
No real organelles
Single circular chromosome (usually)
Has nucleoid but no true nucleus
Wrong! DNA is never naked
(inside cells)!
X
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Translation:
mRNA to protein, by ribosomes
protein
tRNA
Amino
acids
ribosome
tRNA
mRNA = messenger RNA
Codon = 3 nucleotides
encode an amino acid
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Genetic Code: Universal (almost!)
Stop Codons
Start Codon
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Mutations
• Nonsense = STOP codon in wrong place!
• Missense = mutation that results in an amino acid
change in the protein
• Synonymous = mutation in DNA that does not
result in an amino acid change in protein
• Non-synonymous = mutation in DNA that does
result in an amino acid change in protein
Question: Can a "synonymous" mutation alter expression of a
protein - even though DNA change is "silent" (because it
does not change encoded amino acid)? YES! How?
This was last Slide covered in Class on Mon 9/24
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Extra Credit Questions #2-6:
2. What is the size of the dystrophin gene (in kb)?
(Is it still the largest known human protein?)
3. What is the largest protein encoded in human genome (i.e.,
longest single polypeptide chain)?
4. What is the largest protein complex for which a structure is
known (for any organism)?
5. What is the most abundant protein (naturally occurring) on
earth?
6. Which state in the US has the largest number of mobile
genetic elements (transposons) in its living (plant and
animal) population?
For 1 pt total (0.2 pt each): Answer all questions correctly
& submit to terrible@iastate.edu
For 2 pts total: Prepare a PPT slide with all correct answers
& submit to ddobbs@iastate.edu before 9 AM on Mon Oct 1
• Choose one option - you can't earn 3 pts!
• Partial credit for incorrect answers? only if they are truly amusing!
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Extra Credit Questions #7 & #8:
Given that each male attending our BCB 444/544 class on a typical
day is healthy (let's assume MH=7), and is generating sperm at a
rate equal to the average normal rate for reproductively
competent males (dSp/dT = ? per minute):
7a. How many rounds of meiosis will occur during our 50 minute class
period?
7b. How many total sperm will be produced by our BCB 444/544 class
during that class period?
8. How many rounds of meiosis will occur in the reproductively
competent females in our class? (assume FH=5)
For 0.6 pts total (0.2 pt each): Answer all questions correctly
& submit to terrible@iastate.edu
For 1 pts total: Prepare a PPT slide with all correct answers
& submit to ddobbs@iastate.edu before 9 AM on Mon Oct 1
• Choose one option - you can't earn more than 1 pt for this!
• Partial credit for incorrect answers? only if they are truly amusing!
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Protein Function
• Proteins are primary molecules responsible
for carrying out cellular functions
Proteins are the
workhorses of the
cell
• Most "enzymes" that catalyze chemical reactions are
proteins (but some are RNAs!)
• Proteins have complex structures that are critical for
their functions
Protein structure for dystrophin:
encoded by the largest known gene
in humans
(but, dystropin is not the largest
known protein in humans)
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Protein Structure: 4 levels of organization
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Key Aspects of Protein Function:
Localization & Interactions
Protein localization - function
depends on proteins being in right
place at right time!
Protein interactions - function
depends on proteins interacting
with correct partners inside cells!
Both of these are "hot" areas of Bioinformatics research:
later, you will use machine learning to "predict" these!
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Protein Sequence-Structure-Function
• Amino acid sequence determines protein structure
• But some proteins need help folding ("chaperones") in vivo
• Proteins fold to a single "native" structure (under a specific set of
conditions)
• Protein structure determines function
• But level, timing & location of expression are important
• Interactions with other proteins, DNA, RNA, & small ligands are also
very important!!
PROBLEMS:
• We don't know the "folding code" that determines how proteins fold!
• We don't know the "recognition code" that determines how proteins
find and bind their correct partners!
These are "hot" areas of Computational Biology research:
soon, you will try to predict protein structures &
protein binding sites!
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Modeling Protein Interaction Networks
Is this an engineering problem?
This is a "hot" area of Systems Biology research:
later, we will try out "Retinal Workbench" for analyzing
networks involved in retinal development
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Modeling Metabolic Pathways? see MetNet
http://metnet.vrac.iastate.edu/MetNet_overview.htm
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Genes
Genes in chromatin are not just “beads on a string”
they have complex structures that we don't yet
fully understand
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Eukaryotic gene structure
• Recall: Eukaryotic genes are fragmented, containing
introns between functional exons
• In human, on average genes that encode proteins include
~2000-3000 bp coding sequences, but can have >10,000 bp
between exons!!
• Gene sizes can vary by up to 4 orders of magnitude!
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RNA Processing - Splicing
Introns are removed to generate a mature mRNA
DNA
Transcribed
RNA
One gene
Introns removed
by splicing
mRNA
One protein
Different combinations of
exons can be used to make
different proteins
(alternative splicing)
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Gene regulation
• Transcriptional regulation is primarily mediated by
proteins that interact with cis-acting DNA elements
associated with genes:
• DNA level (sequence-specific) regulatory signals
• Promoters, terminators
• Enhancers, repressors, silencers
• Chromatin level (global) regulation
• Heterochromatin (inactive)
•e.g., X-inactivation in female mammals
• In eukaryotes, genes are often regulated at other levels:
• Post-transcriptional (RNA transport, splicing, stability)
• Post-translational (protein localization, folding, stability)
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Promoter = DNA sequences required for
initiation of transcription; contain TF binding
sites, usually "close" to start site
• Transcription factors (TFs) - proteins that regulate transcription
• (In eukaryotes) RNA polymerase binds by recognizing a complex of
TFs bound at promotor
First, TFs must
bind TF binding
sites (TFBSs) within
promoters; then
RNA polymerase can
bind and initiate
transcription of
RNA
~200 bp
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Pre-mRNA
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Enhancers & repressors = DNA sequences
that regulate initiation of transcription;
contain TF binding sites,can be far from start site!
RNAP = RNA polymerase II
Promoter
Enhancer
Repressor
10-50,000 bp
Enhancers "enhance"
transcription
Repressors or silencers
"repress" transcription
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Gene
Enhancer binding
proteins (TFs)
interact with RNAP
Repressor binding
proteins (TFs) block
transcription
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Transcription factors (TFs) &
their binding sites (TFBSs)
• Transcription factors - proteins that either activate or repress
transcription, usually by binding DNA (via a DNA binding domain) &
interacting with RNA polymerase (via a "trans-activating domain) to
affect rate of transcription initiation
• Promotors, enhancers, and repressors - all contain binding sites
for transcription factors
• Promoters - usually located close to start site;
• Enhancers/Silencers/Repressor sequences - can be close or very
far away: located upstream, downstream or even within the coding
sequence of genes !!
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"Non-coding" DNA? Many genes encode
RNA that is not translated
4 Major Classes of RNA:
1. mRNA = messenger RNA
2. tRNA = transfer RNA
3. rRNA = ribosomal RNA
4. "Other" - Lots of these, diverse structures & functions:
"Natural" RNAs:
• siRNA, miRNA, piRNA, snRNA, snoRNA, …
• ribozymes
• Artificial RNAs:
• RNAi
• antisense RNA
•
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Web Resources for more information:
• BioTech’s Life Science Dictionary
• Online textbooks – NCBI bookshelf
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Algorithms & Software for MSA? #3
(NOT covered on Exam1)
Heuristic Methods - continued
• Progressive alignments (Star Alignment, Clustal)
• Others: T-Coffee, DbClustal -see text: can be better than Clustal
• Match closely-related sequences first using a guide tree
• Partial order alignments (POA)
• Doesn't rely on guide tree; adds sequences in order given
• PRALINE
• Preprocesses input sequences by building profiles for each
• Iterative methods
• Idea: optimal solution can be found by repeatedly modifying existing
suboptimal solutions (eg: PRRN)
• Block-based Alignment
• Multiple re-building attempts to find best alignment
(eg: DIALIGN2 & Match-Box)
• Local alignments
• Profiles, Blocks, Patterns - more on these soon!
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Chp 6 - Profiles & Hidden Markov Models
SECTION II
SEQUENCE ALIGNMENT
Xiong: Chp 6
Profiles & HMMs
• √Position Specific Scoring Matrices (PSSMs)
• √PSI-BLAST
Thurs & Fri:
• Profiles
• Markov Models & Hidden Markov Models
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Chp 7 - Protein Motifs & Domain Prediction
SECTION II
SEQUENCE ALIGNMENT
Xiong: Chp 7
Protein Motifs and Domain Prediction
• Identification of Motifs & Domains in Multple Sequence
Alignment
• Motif & Domain Databases Using Regular Expressions
• Motif & Domain Databases Using Statistical Models
• Protein Family Databases
• Motif Discovery in Unaligned Sequences
• Sequence Logos
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