# Sequence analysis - PowerPoint PPT Presentation

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Sequence analysis. June 18, 2008 Learning objectives-Understand the concept of sliding window programs. Understand difference between identity, similarity and homology. Appreciate that proteins can be modular

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Sequence analysis

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### Sequence analysis

• June 18, 2008

• Learning objectives-Understand the concept of sliding window programs. Understand difference between identity, similarity and homology. Appreciate that proteins can be modular

• Workshop-Learn to recognize amino acid structures. Perform sliding window to compute %G+C as a function of position in sequence. Become familiar with the Dotter program.

4

GCATATGCGCATATCCCGTCAATACCA

5

GCATATGCGCATATCCCGTCAATACCA

6

GCATATGCGCATATCCCGTCAATACCA

### Sliding window

A sliding window-gathers information about properties of nucleotides or amino acids.

A simple example is to calculate the %G+C content within a window. Then move the window one nucleotide and repeat the calculation.

### Sliding window

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 size

%G+C

Sequence number

Large window size

%G+C

Sequence number

A T G C C T A G

*

*

A T G C C T A G

*

*

*

*

*

*

*

*

*

*

*

*

*

*

### Dot Plot with window = 1

Window = 1

Note that 25% of

the table will be

filled due to random

chance. 1 in 4 chance

at each position

### Dot Plot with window = 3

A T G C C T A G

Window = 3

The larger the window

the more noise can

be filtered

What is the

percent chance that

match randomly? One

in (four)3chance.

(¼)3 * 100 = 1.56%

{

A T G C C T A G

*

*

*

*

*

*

Amino acid characteristics

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

### Chou-Fasman Rules (Mathews, Van Holde, Ahern)

Amino Acid-Helix-SheetTurn

Ala 1.29 0.900.78

Cys 1.11 0.740.80

Leu 1.30 1.020.59

Met 1.47 0.970.39

Glu 1.44 0.751.00

Gln 1.27 0.800.97

His 1.22 1.080.69

Lys 1.23 0.770.96

Val 0.91 1.490.47

Ile 0.97 1.450.51

Phe 1.07 1.320.58

Tyr 0.72 1.251.05

Trp 0.99 1.140.75

Thr 0.82 1.211.03

Gly 0.56 0.921.64

Ser 0.82 0.951.33

Asp 1.04 0.721.41

Asn 0.90 0.761.23

Pro 0.52 0.641.91

Arg 0.96 0.990.88

Favors

-Helix

Favors

-Sheet

Favors

Turns

### 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.

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.

### Evolutionary Basis of Sequence Alignment

1. Identity: Quantity that describes how much

two sequences are alike in the strictest terms.

2. Similarity: Quantity that relates how much

two amino acid sequences are alike.

3. Homology: a conclusion drawn from data

suggesting that two genes share a common

evolutionary history.

### Purpose of finding differences and similarities of amino acids in two proteins.

• Infer structural information

• Infer functional information

• Infer evolutionary relationships

One is mouse trypsin and the other is crayfish trypsin.

They are homologous proteins. The sequences share 41% identity.

### Modular nature of proteins

• Proteins possess local regions of similarity.

• Proteins can be thought of as assemblies of modular domains.

### 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

### 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

### 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?

### Two proteins that are similar in certain regions

Tissue plasminogen activator (PLAT)

Coagulation factor 12 (F12).

### 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 dot density the higher the score)

Single region on F12

is similar to two regions

on PLAT

Region of

similarity