1 / 33

BCB 444/544

BCB 444/544. Lecture 18 More details: HMMs Protein Motifs & Domain Prediction Maybe: Protein Structure - The Basics #18_Oct03. Required Reading ( before lecture). √ Mon Oct 1 - Lecture 17 Protein Motifs & Domain Prediction Chp 7 - pp 85-96 Wed Oct 3 - Lecture 18

pavel
Download Presentation

BCB 444/544

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BCB 444/544 Lecture 18 More details: HMMs Protein Motifs & Domain Prediction Maybe: Protein Structure - The Basics #18_Oct03 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  2. Required Reading (before lecture) √MonOct 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  3. Assignments & Announcements • HW544Extra #1 - √Due: 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  4. 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. Nature443: 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  5. 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 • BioTech’s Life Science Dictionary • http://biotech.icmb.utexas.edu/search/dict-search.html • NCBI Bookshelf • http://www.ncbi.nlm.nih.gov/sites/entrez?db=books BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  6. Statistics References Statistical Inference (Hardcover) George Casella, Roger L. Berger StatWeb: A Guide to Basic Statistics for Biologists http://www.dur.ac.uk/stat.web/ Basic Statistics: http://www.statsoft.com/textbook/stbasic.html (correlations, tests, frequencies, etc.) Electronic Statistics Textbook: StatSoft http://www.statsoft.com/textbook/stathome.html (from basic statistics to ANOVA to discriminant analysis, clustering, regression, data mining, machine learning, etc.) BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  7. 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@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! BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  8. 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 by 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! BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  9. Answers? BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  10. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  11. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  12. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  13. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  14. 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… BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  15. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  16. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  17. An HMM for CpG Islands? Emission probabilities are0 or 1e.g.,eG-(G) = 1, eG-(T) = 0 See Durbin et al., Biological Sequence Analysis, Cambridge, 1998 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  18. HMM example from Eddy HMM paper: Toy HMM for Splice Site Prediction This is a new slide BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  19. An HMM for Occasionally Dishonest Casino Transition probabilities • Prob(Fair  Loaded) = 0.01 • Prob(Loaded Fair) = 0.2 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  20. Calculating Different Paths to an Observed Sequence This slide has been changed transition probability emission probability Calculations such as those shown below are used to fill a matrix with probability values for every state at every position BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  21. Calculating the Most Probable Path*, using Viterbi algorithm (using traceback as in DP) This slide has been changed x 2 6  6 0 0 B 1 0 (1/6)max{(1/12)0.99, (1/4)0.2} = 0.01375 (1/6)max{0.013750.99, 0.020.2} = 0.00226875 (1/6)(1/2) = 1/12 0 F  (1/2)max{0.013750.01, 0.020.8} = 0.08 (1/10)max{(1/12)0.01, (1/4)0.8} = 0.02 (1/2)(1/2) = 1/4 0 L * Path within HMM that matches query sequence with highest probability BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  22. Calculating the Total Probability: This slide has been changed x 2 6  6 0 0 B 1 0 (1/6)sum{(1/12)0.99, (1/4)0.2} = 0.022083 (1/6)sum{0.0220830.99, 0.0200830.2} = 0.004313 (1/6)(1/2) = 1/12 0 F  (1/2)sum{0.0220830.01, 0.0200830.8} = 0.008144 (1/10)sum{(1/12)0.01, (1/4)0.8} = 0.020083 (1/2)(1/2) = 1/4 0 L Total probability = = 0 + 0.004313 + 0.008144 = 0.012 Note: This not the same as matrix on previous slide! Here, last column contains sums for each row BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  23. 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) BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  24. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  25. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  26. Example: Pfam: Protein Familieshttp://pfam.sanger.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 34:D247-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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  27. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  28. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  29. 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 • √Sequence Logos BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  30. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  31. 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 BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  32. 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!) BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

  33. Chp 12 - Protein Structure Basics SECTION V STRUCTURAL BIOINFORMATICS Xiong: Chp 12 Protein Structure Basics • Introduction to the Protein DataBank - PDB • NEXT lecture! BCB 444/544 F07 ISU Dobbs #18- Protein Motifs & Domains

More Related