Sequence analysis

1. Sequence analysis • June 17, 2003 • Learning objectives-Review amino acids structures. Understand sliding window programs. Understand difference between identity, similarity and homology. Understand difference between global alignment and local alignment. • Workshop-Perform sliding window to compute %GC as a function of position in sequence.

2. Sliding window (1) This refers to the number of characters you look at, during one particular time. 4 GCATATGCGCATATCCCGTCAATACCA 5 GCATATGCGCATATCCCGTCAATACCA 6 GCATATGCGCATATCCCGTCAATACCA

3. Sliding window (2) A "window" can be defined as a span of a certain number of residues (nucleotides or amino acids). One calculates some value for the residues in that fragment. Once the calculation is completed, the program analyzes the next window of residues and this process repeats itself until the end of the sequence is reached. A simple example is to calculate the %GC content within a window. Then move the window one nucleotide and repeat the calculation.

4. Sliding window (3) If the window is too small it is difficult to detect the trend of the measurement. If too large you could miss meaningful data. Small window %GC Number in sequence Large window %GC Number in sequence

5. Sliding window (4)

6. Complex folding patterns- Interactions between polypeptides Four levels of protein structure Linear sequence-AGHIPLLQ 1) Primary 2) Secondary 3) Tertiary 4) Quaternary Initial folding patterns- AGHIPLLQ aaaTTTbb

7. Other classification schemes • Two major types: • Alpha Helical Regions • Beta Sheet Regions • Other classification schemes: • Turns • Transmembrane regions • Internal regions • External regions • Antigenic regions

8. Chou-Fasman Rules (Mathews, Van Holde, Ahern) Amino Acid -Helix -Sheet Turn Ala 1.29 0.90 0.78 Cys 1.11 0.74 0.80 Leu 1.30 1.02 0.59 Met 1.47 0.97 0.39 Glu 1.44 0.75 1.00 Gln 1.27 0.80 0.97 His 1.22 1.08 0.69 Lys 1.23 0.77 0.96 Val 0.91 1.49 0.47 Ile 0.97 1.45 0.51 Phe 1.07 1.32 0.58 Tyr 0.72 1.25 1.05 Trp 0.99 1.14 0.75 Thr 0.82 1.21 1.03 Gly 0.56 0.92 1.64 Ser 0.82 0.95 1.33 Asp 1.04 0.72 1.41 Asn 0.90 0.76 1.23 Pro 0.52 0.64 1.91 Arg 0.96 0.99 0.88 Favors -Helix Favors -Sheet Favors Turns

9. Chou-Fasman • First widely used procedure • If propensity in a window of six residues (for a helix) is above a certain threshold the helix is chosen as secondary structure. • If propensity in a window of five residues (for a beta strand) is above a certain threshold then beta strand is chosen. • Each classification is extended until the average propensity in a 4 residue window falls below a value. • Output-helix, strand or turn.

10. Chou&Fasman structure prediction Chou & Fasman [Biochemistry 13(2):222-245 (1974)]. By studying a number of proteins whose structures were known, they were able to determine stretches of amino acids that could serve to form an a-helix or a b-sheet. These amino acids are called helix formers or sheet formers and can have different strengths for forming their structures. Once these nucleation sites are determined, adjacent amino acids are examined to see if the structure can be extended in either or both directions. Values for some amino acids allow extension, other amino acids do not. Some amino acids are categorized as helix breakers, or sheet breakers. A string of these will terminate the current structure. This method is about 60-65% accurate.

11. 5 7 4 6 1 2 3 Kyte-Doolittle Hydropathy – Another sliding window routine [J. Mol. Biol. 157:105-132 (1982)]. They determine a "hydropathy scale" for each amino acid based on empirical observations.

14. Purpose of finding differences and similarities of amino acids in two proteins. • Infer structural information • Infer functional information • Infer evolutionary relationships

15. Evolutionary Basis of Sequence Alignment • Similarity: Quantity that relates how much • two amino acid sequences are alike. • 2. Identity: Quantity that describes how much • two sequences are alike in the strictest terms. • 3. Homology: a conclusion drawn from data • suggesting that two genes share a common • evolutionary history.

16. One is mouse trypsin and the other is crayfish trypsin. They are homologous proteins. The sequences share 41% identity.

17. Evolutionary Basis of Sequence Alignment (Cont. 1) Why are there regions of identity? 1) Conserved function-residues participate in reaction. 2) Structural (For example, conserved cysteine residues that form a disulfide linkage) 3) Historical-Residues that are conserved solely due to a common ancestor gene.

18. Evolutionary Basis of Sequence Alignment (Cont. 2) Note: it is possible that two proteins share a high degree of similarity but have two different functions. For example, human gamma-crystallin is a lens protein that has no known enzymatic activity. It shares a high percentage of identity with E. coli quinone oxidoreductase. These proteins likely had a common ancestor but their functions diverged. Analogous to railroad car and diner function.

20. Modular nature of proteins • The previous alignment was global. However, many proteins do not display global patterns of similarity. Instead, they possess local regions of similarity. • Proteins can be thought of as assemblies of modular domains. THINK OF MR. POTATOHEAD. It is thought that this may, in some cases, be due to a process known as exon shuffling.

21. Modular nature of proteins (cont. 1) Exon 1a Exon 2a Gene A Duplication of Exon 2a Exon 1a Gene A Exon 2a Exon 2a Exchange with Gene B Exon 1b Gene B Exon 2b Exon 2b Exon 3 (Exon 2b from Gene B) Exon 2a Exon 1a Gene A Exon 1b Exon 3 (Exon 2a from Gene A) Gene B Exon 2b

22. A T G C C T A G * * A T G C C T A G * * * * * * * * * * * * * * Dot Plots Window = 1 Note that 25% of the table will be filled due to random chance. 1 in 4 chance at each position

23. Dot Plots with window = 2 A T G C C T A G Window = 2 The larger the window the more noise can be filtered What is the percent chance that you will receive a match randomly? One in (four)2chance. 1/16 * 100 = 6.25% * A T G C C T A G { * { * { * { * { * { * {

24. Identity Matrix A 1 C 0 1 I 0 0 1 L 0 0 0 1 A C I L Simplest type of scoring matrix

25. Similarity It is easy to score if an amino acid is identical to another (the score is 1 if identical and 0 if not). However, it is not easy to give a score for amino acids that are somewhat similar. CO2- CO2- +NH3 +NH3 Isoleucine Leucine Should they get a 0 (non-identical) or a 1 (identical) or Something in between?

26. The Dotter Program • Program consists of three components: • Sliding window • A table that gives a score for each amino acid match • A graph that converts the score to a dot of certain density. • The higher the density the higher the score.

28. Two proteins that are similar in certain regions Tissue plasminogen activator (PLAT) Coagulation factor 12 (F12).

30. Single region on F12 is similar to two regions on PLAT Region of similarity