BCB 444544

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BCB 444/544

• Lecture 18
• More details HMMs
• Protein Motifs Domain Prediction
• Maybe Protein Structure - The Basics
• 18_Oct03

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Required Reading (before lecture)

• vMon Oct 1 - Lecture 17
• Protein Motifs Domain Prediction
• Chp 7 - pp 85-96
• Wed Oct 3 - Lecture 18
• Protein Structure The Basics (Note chg in
  lecture Schedule!)
• Chp 12 - pp 173-186
• Thurs Oct 4 - Lab 6
• Protein Structure Databases Visualization
• Fri Oct 5 - Lecture 19
• Protein Structure Classification Comparison
• Chp 13 - pp 187-199

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Assignments Announcements

• HW544Extra 1 -
• vDue Task 1.1 - Mon Oct 1 (today) by noon
• Task 1.2 Task 2 - Mon Oct 8 by 5 PM
• HomeWork 3 - posted online
• Due 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/ab
  s/nature05113.html
• doi10.1038/nature05113
• PDF available on class website - under Required
  Reading Link

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A few Online Resources for Cell Molecular
Biology

• NCBI Science Primer What is a cell?
• http//www.ncbi.nlm.nih.gov/About/primer/genetics_
  cell.html
• NCBI Science Primer What is a genome?
• http//www.ncbi.nlm.nih.gov/About/primer/genetics_
  genome.html
• BioTechs Life Science Dictionary
• http//biotech.icmb.utexas.edu/search/dict-search.
  html
• NCBI Bookshelf
• http//www.ncbi.nlm.nih.gov/sites/entrez?dbbooks

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Statistics References

• Statistical Inference (Hardcover)
• George Casella, Roger L. Berger

StatWeb A Guide to Basic Statistics for
Biologists http//www.dur.ac.uk/stat.we
b/ Basic Statistics http//www.statsoft.com/tex
tbook/stbasic.html (correlations, tests,
frequencies, etc.) Electronic Statistics
Textbook StatSoft http//www.statsoft.com/textb
ook/stathome.html (from basic statistics to
ANOVA to discriminant analysis, clustering,
regression, data mining, machine learning,
etc.)
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Extra Credit Questions 2-6

• What is the size of the dystrophin gene (in kb)?
• Is it still the largest known human
  protein?
• What is the largest protein encoded in human
  genome (i.e., longest single polypeptide
  chain)?
• What is the largest protein complex for which a
  structure is known (for any organism)?
• What is the most abundant protein (naturally
  occurring) on earth?
• Which state in the US has the largest number of
  mobile genetic elements (transposons) in its
  living population?

• For 1 pt total (0.2 pt each) Answer all
  questions correctly
• submit by to terrible_at_iastate.edu
• For 2 pts total Prepare a PPT slide with all
  correct answers
• submit to ddobbs_at_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
  MH7), 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 FH5)

• For 0.6 pts total (0.2 pt each) Answer all
  questions correctly
• submit by to terrible_at_iastate.edu
• For 1 pts total Prepare a PPT slide with all
  correct answers
• submit to ddobbs_at_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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Answers?
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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
• Profiles
• Markov Models Hidden Markov Models

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Statistical Models for Representing Biological
Sequences

• 3 types of probabilistic models, all of which
• Are based on MSA
• Capture both observed frequencies predicted
  frequencies of unobserved characters
• In order of "sensitivity"
• PSSM - scoring table derived from an ungapped
  MSA stores frequencies (log odds scores) for
  each amino acid in each position of a protein
  sequence,
• Profile - A PSSM with gaps based on gapped MSA
  with penalties for insertions delations
• HMM - hidden Markov Model - more complex
  mathematical model (than PSSMs or Profiles)
  because it also differentiates between insertions
  and deletions

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HMMs for Biological Sequences?

• HMMs originally developed for speech recognition
• Now widely used in bioinformatics
• Many applications (motif/domain detection,
  sequence alignment, phylogenetic
• HMMs are "machine learning" algorithms - must be
  "trained" to obtain optimal statistical
  parameters
• For Biological sequences
• each character of a sequence is considered a
  state in a Markov process

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But, What is a Markov Model?

• Markov Model (or Markov chain)
• mathematical model used to describe a sequence
  of events that occur one after another in a chain
• a process that moves in one direction from one
  state to the next with a certain transition
  probability
• For biological sequences
• each letter state
• linked together by transition probabilities

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Different Types of Markov Models

• Zero-order Markov Model probability of current
  state is independent of previous state(s)
• e.g., random sequence, each residue with equal
  frequency
• First-order MM probability of current state is
  determined by the previous state
• e.g., frequencies of two linked residues
  (dimer) occurring simultaneously
• Second-order MM describes situation in which
  probability of current state is determined by
  the previous two states
• e.g., frequencies of thee linked residues
  (trimers) - occurring simultaneously, as in a
  codon
• Higher orders? Also possible, later

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So, What is a hidden Markov Model?

• Hidden Markov Model (HMM)
• a more sophisticated model in which some of
  states are hidden
• some "unobserved" factors influence the state
  transition probabilities
• MM which combines 2 or more Markov chains
• only 1 chain is made up of observed states
• other chains are made up of unobserved or
  "hidden" states

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Hidden Markov Models - HMMs

• Goal Find most likely explanation for observed
  variables
• Components
• States - composed of a number of elements or
  "symbols" (e.g., A,C,G,T)
• Observed variables - sequence (or outcome) we can
  "see"
• Hidden variables - insertions/deletions/transition
  probabilities that can't be "seen"
• Emission probability - probability value
  associated with each "symbol" in each state
• Transition probability - probability of going
  from one state to another
• Special graphical representation used to
  illustrate relationships

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An HMM for CpG Islands?
Emission probabilities are 0 or 1 e.g., eG-(G)
1, eG-(T) 0
See Durbin et al., Biological Sequence Analysis,
Cambridge, 1998
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HMM example from Eddy HMM paper Toy HMM for
Splice Site Prediction
This is a new slide
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An HMM for Occasionally Dishonest Casino

• Transition probabilities
• Prob(Fair ? Loaded) 0.01
• Prob(Loaded ? Fair) 0.2

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Calculating Different Paths to an Observed
Sequence
This slide has been changed
Calculations such as those shown below are used
to fill a matrix with probability values for
every state at every position
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Calculating the Most Probable Path, using
Viterbi algorithm (using traceback as in DP)
This slide has been changed
Path within HMM that matches query sequence
with highest probability
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Calculating the Total Probability
This slide has been changed
Note This not the same as matrix on previous
slide! Here, last column contains sums for each
row
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Estimating the Probabilities or Training
the HMM
This slide has been changed

• Calculate frequencies in each column of MSA built
  from set of related sequences
• Use frequency values to fill the emission and
  transition probabilities in the model (use two
  matrices for this)
• Viterbi training
• Derive probable paths for training data using
  Viterbi algorithm
• Re-estimate transition probabilities based on
  Viterbi path
• Iterate until paths stop changing
• Other algorithms can be used
• e.g., "forward" "backward" algorithms
• (see text - or see Wikipedia re HMMs)

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Profile HMMs

• Used to model a family of related sequences
• (or motif or domain)
• Derived from a MSA of family members
• Transition emission probabilities are
  position-specific
• Set parameters of model so that total probability
  peaks at members of family
• Sequences can be tested for family membership
  using Viterbi algorithm to evaluate match
  against profile

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Profile HMM represents a gapped MSA
This slide has been changed
Character in alignment can be in one of 3
states Match - observed Insert -
hidden Delete - hidden
Hidden chains
Observed chain
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Example Pfam Protein Families http//pfam.san
ger.ac.uk/

• A comprehensive collection of protein domains
  and families, with a range of well-established
  uses including genome annotation.
• Pfam clans, web tools and services R.D. Finn,
  A. Bateman (2006) Nucleic Acids Res Database
  Issue 34D247-D5
• Each family is represented by
• 2 MSAs
• 2 Hidden Markov Models (profile-HMMs)
• cf. Superfamily - from Lab 5
• similar collection of curated MSAs HMMs,
  focuses on superfamily level

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A few more Details re Profiles HMMs

• Smoothing or "Regularization" - method used to
  avoid "over-fitting"
• Common problem in machine learning (data-driven)
  approaches
• Limited training sample size causes
  over-representation of observed characters while
  "ignoring" unobserved characters
• Result? Miss members of family not yet sampled
• (too many false negative hits)
• Pseudocounts - adding artificial values for
  'extra' amino acid(s) not observed in the
  training set
• Treated as a 'real' values in calculating
  probabilities
• Improve predictive power of profiles HMMs
• Dirichlet mixture - commonly used mathematical
  model to simulate the aa distribution in a
  sequence alignment
• To "correct" problems in an observed alignment
  based on limited number of sequences

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Applications (of PSSMs, Profiles, HMMs)

• HMMer - for building using HMMs
• developed by Sean Eddy's group
• Not a web-based server must download the
  software
• 9 related programs
• but check out the site - it's fun!
• Psi-BLAST - you've heard enough about this!
• Uses Profiles (not actually PSSMs) - iteratively
• In previous lab used SuperFam (HMMs)
• http//supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY/
• Prosite - includes patterns (regular expressions)
  profiles for motifs domains
• http//ca.expasy.org/prosite
• Pfam (MSAs HMMs)
• http//pfam.sanger.ac.uk/ (new URL)
• Many others

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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 MSAs
• Motif Domain Databases Using Regular
  Expressions
• Motif Domain Databases Using Statistical Models
• Protein Family Databases
• Motif Discovery in Unaligned Sequences
• vSequence Logos

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Motifs Domains

• Motif - short conserved sequence pattern
• Associated with distinct function in protein or
  DNA
• Avg 10 residues (usually 6-20 residues)
• e.g., zinc finger motif - in protein
• e.g., TATA box - in DNA
• Domain - "longer" conserved sequence pattern,
  defined as a independent functional and/or
  structural unit
• Avg 100 residues (range from 40-700 in
  proteins)
• e.g., kinase domain or transmembrane domain - in
  protein
• Domains may (or may not) include motifs

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2 Approaches for Representing "Consensus"
Information in Motifs Domains

• Regular expression - reduce information from MSA
• e.g., protein phosphorylation site motif
  S,T- X- R,K
• Symbols represent specific or unspecified
  residues, spaces, etc.
• 2 mechanisms for matching
• Exact
• "Fuzzy" (inexact, approximate) - flexible, more
  permissive to detect "near matches"
• Statistical model - includes probability
  information derived from MSA
• e.g., PSSM, Profile or HMM

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Motif Domain Databases

• Based on regular expressions
• Prosite (Interpro)
• Emofit
• Limitation these don't take probability info
  into account
• Based on statistical models
• PRINTS
• BLOCKS
• ProDom
• Pfam
• SMART
• CDART
• Reverse PsiBLAST

• READ your textbook try some of these at home
  there are distinct advantages/disadvantages
  associated with each
• TAKE HOME LESSON
• Always try several methods!
• (not just one!)

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Chp 12 - Protein Structure Basics

• SECTION V STRUCTURAL BIOINFORMATICS
• Xiong Chp 12
• Protein Structure Basics
• Introduction to the Protein DataBank - PDB
• NEXT lecture!