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Generic substitution matrix based sequence comparison Q: M A T W L I. A: M A - W T V. Scr: 45 -?11 3 Scr: 45 -2 1 Q: M A T W L I. A: M A W T V A. Total:

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Presentation on theme: "Generic substitution matrix based sequence comparison Q: M A T W L I. A: M A - W T V. Scr: 45 -?11 3 Scr: 45 -2 1 Q: M A T W L I. A: M A W T V A. Total:"— Presentation transcript:

1 Generic substitution matrix based sequence comparison Q: M A T W L I. A: M A - W T V. Scr: 45 -?11 3 Scr: 45 -2 1 Q: M A T W L I. A: M A W T V A. Total: 5 Total = 22 - ? Blosum 62: Gap openning: -6 ~ -15 Gap Extension: -2 ~ -6

2 Blast outputoutput

3 Position –specific information about conserved domains is IGNORED in single sequence –initiated search BID_MOUSE SESQEEIIHN IARHLAQIGDEM DHNIQPTLVR BAD_MOUSE APPNLWAAQR YGRELRRMSDEF EGSFKGLPRP BAK_MOUSE PLEPNSILGQ VGRQLALIGDDI NRRYDTEFQN BAXB_HUMAN PVPQDASTKK LSECLKRIGDEL DSNMELQRMI BimS EPEDLRPEIR IAQELRRIGDEF NETYTRRVFA HRK_HUMAN LGLRSSAAQL TAARLKALGDEL HQRTMWRRRA Egl-1 DSEISSIGYE IGSKLAAMCDDF DAQMMSYSAH BID_MOUSE SESQEEIIHN IARHLAQIGDEM DHNIQPTLVR sequence X SESSSELLHN SAGHAAQLFDSM RLDIGSTAHR sequence Y PGLKSSAANI LSQQLKGIGDDL HQRMMSYSAH Why a BLAST match is refused by the family ?

4 Representation of positional information in specific motif M-C-N-S-S-C-[MV]-G-G-M-N-R-R. Binary patterns: Positional matrix: -2.499 -2.269 -5.001 -4.568 -2.418 -4.589 -3.879 1.971 -4.330 1.477 -1.241 -4.221 -4.590 -4.097 -4.293 -3.808 0.495 2.545 -3.648 -3.265 1.627 -2.453 -1.804 -1.746 -3.528 2.539 1.544 -3.362 -1.440 -3.391 -2.490 -1.435 -3.076 -1.571 0.501 0.201 -1.930 -2.707 -3.473 -3.024 -1.346 -2.872 -1.367 0.699 -2.938 -2.427 -0.936 -2.632 -0.095 1.147 -1.684 -1.111 -2.531 1.174 2.105 1.057 -1.400 -2.255 -2.899 -2.260 1.045 1.754 -1.169 -0.576 -2.756 -2.212 1.686 -2.576 0.951 -2.438 -1.544 0.857 -2.301 1.891 1.556 -1.097 -1.180 -2.155 -2.751 -2.060 -3.385 -2.965 -5.039 -4.313 -1.529 -5.006 -3.577 0.429 -4.094 3.154 -0.121 -4.440 -4.199 -3.292 -3.662 -4.198 -3.281 -1.505 -3.043 -3.120 2.368 -3.197 -2.285 -1.533 -3.721 -2.945 1.815 -3.235 0.067 -3.061 -2.259 -1.680 -3.231 1.195 2.287 -2.009 -2.044 -2.825 -3.324 -2.844 1.046 1.742 0.576 -0.734 -2.072 -2.234 -0.851 0.436 -0.548 -0.129 -0.974 -1.039 -2.318 2.368 0.667 -1.135 -1.076 -1.304 -2.398 -1.821 0.715 -1.778 -3.820 -3.359 -1.535 -3.463 -2.571 3.060 -3.008 0.262 1.566 -2.996 -3.575 0.450 -3.001 -2.571 -1.765 0.193 -2.389 -2.068 -2.053 0.965 -2.767 -3.509 -4.520 3.654 -3.242 -4.548 -3.338 -4.968 -3.789 -2.462 -4.048 -3.738 -3.266 -2.596 -3.573 -3.990 A C D E F G H I K L M N …. Pos. 123456789

5 DNA Pattern – Transcription factor binding site Practice: identify potential transcription factor binding sites on a promoter sequence. Using TESS : Transcription Element Search System http://www.cbil.upenn.edu/cgi-bin/tess/tess33?RQ=WELCOME

6 TESS result

7 Why are there many false positives for TF binding site scan?  Contextual dependency is not considered.  Stringency of the matrices.

8 DNA Pattern – Transcription factor binding site Pattern strings / Matrixes are extracted from known binding sequence. Core vs whole. Some short and/or ambiguous patterns will have many hits.

9 Stringency of the matrices ACGT Con sens u s 401323 N 203705G 553400R 09305C 538830W 150382T 001000G 0500 Y 068030C 1235348Y ACGT Co nse nsu s 40130G 50120G 15020A 01700C 000A 000 T 00 0G 01304C 01700C 0 00C 00 0G 00 0G 20150G 01700C 000A 000 T 00 0G 02015T 01304C 0727Y P53_01 P53_02 Consensus –10 bp Consensus –20 bp

10 How are the motif matrices derived? - example: Hidden Markov Model A specification on “how events will happen” so that statistical assessment can be readily made Used for Speech recognition and characterizing sequence patterns

11 Hidden Markov Model Position 1Position 2 Deletion Insertion Position 3 1.) possible states of events or “routes” --Transition Probability

12 Hidden Markov Model Position 1Position 2 2.) possible AA at a given position -- emission probability ACDEFGHIACDEFGHI

13 Hidden Markov Model 3.) That’s all. Let’s give it a try and you will know what it is about

14 Hidden Markov Model How to make a HMM for my motif ? Collect related sequences MEME : http://meme.sdsc.edu/meme4_3_0/cgi- bin/meme.cgi *Selection of sequences determines the model*

15 Identifying motifs using MEME -Multiple EM for Motif Elicitation EM: Expectation maximization (P173- 177). Identifies statistically significant motif(s) in a set of sequences. Outline the occurrence of the motifs at the end of the report

16 Practice: Identify conserved motifs using MEME 1.) Input your own email address. 2.) Load the file of multiple Fasta format sequences. 3.) You can change other options based on your needs.

17 Two search examples  The outcome of the search is dependent on the inputting set of sequences.  Compose the inputting set based on your research needs. Set1: Mammalian P53 plus mosquito hits Set2: Diverse set of P53 plus mosquito hits

18 Two search examples Set1: Mammalian P53 plus mosquito hits Set2: Diverse set of P53 plus mosquito hits

19 Secondary structure prediction Predict the likelihood of amino acid x to be in each of the three (four) types of secondary structure configuration Helix Sheet Turn Coil Coiled-coil is two helices tangled together

20 Secondary structure prediction - different strategies and algorithms Chou-Fasman / Garnier Method -- based on AA composition Nearest Neighbor / Levin Method -- based on sequence similarity Neural Network / PHD SOPM, DPM, DSC, etc.

21 Results are given at single amino acid level

22 Secondary structure prediction --Interpretation of result Seq: - D G S L A D E R K Pre: - B B B H H B B T T What is the likelihood of helix formation here ?

23 Secondary structure prediction -Accuracy At the amino acid level -- ~ 75% based on testing set IF: Seq: - D G I L A V A S M I V Pre: - B B H H H H H H H H H Length > 9 > 90% chance a helix formation around this region

24 Secondary structure prediction -Programs There are over a dozen web sites provide 2 nd structure predication service – (tools) AntheWin has a good sample of different approaches and has other associated tools

25 Practice: using AnathePro to analyze protein secondary structure Open the sequence file. Chose and run a secondary structure predication method from the “Methods” menu LEFT click the left boundary of an alpha helix and then RIGHT the right boundary, perform “helical Wheel”

26 Helix wheel to discern helix subtype HydrophilicHydrophobicOthers AmphipathicHydrophobicHydrophilic

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