07. BLOSUM - BLOcks SUbstitution Matrix
An alternative to the PAM matrix is BLOSUM (BLocks SUbstitution Matrix), which was derived by Henikoff and Henikoff in 1992. NCBI uses BLOSUM62 as its the default matrix for protein BLAST.
Derivation of BLOSUM matrices
BLOSUM matrices are derived from comparisons of blocks of sequences from the Blocks database.
What are blocks and what is the blocks database?
A block is an ungapped multiple alignments of highly conserved, short regions. Here is what a sample block looks like:
The blocks database contains multiple alignments of conserved regions in protein families.
Derivation a BLOSUM matrix
The Henikoffs developed a database of "blocks" based on sequences with shared motifs. More than 2,000 blocks of aligned sequence segments were analyzed from more than 500 groups of related proteins. Within each block, they counted the relative frequencies of amino acids and their substitution probabilities
Why did they use Blocks?
The Henikoffs used blocks due to several reasons:
- Need to have multiple alignments and it's easier to align with similar sequences.
- They didn't want to complicate calculations with insertions/deletions.
- Wanted to focus on conserved regions for computing the scoring matrix.
What does a BLOSUM matrix tell us?
A BLOSUM tells us the likelihood of occurrence of each pairwise substitution, and we can use these values to score a pairwise comparison.
Each scoring matrix is constructed based on how identical the ungapped multiple sequence alignments are. For example, BLOSUM62 is derived from blocks containing at most 62% identity in the ungapped sequence aligments.
Calculating a BLOSUM
Here we'll show you how to calculate a BLOSUM.
Removing redundancies
Before we start constructing a matrix BLOSUM r, we have to eliminate the sequences that are more than r% identical. This solves us from the bias we get from databases over-representing certain classes of proteins. To do this, we have two options:
- Remove sequences from the block.
- Replace the similar sequences with a new sequence that represents the cluster.
ACD
DCE
DCE
DCE
BCE
BCD
ACBSince most databases today have an over-representation of proteins, the extraneous DCE sequences should be eliminated in order to make our database more representative.
Calculating possible pairwise combinations
Thus, after elminating redundancies, we look at the first vertical column in our block:
A
D
B
B
ALet's find out how many possible pairwise combinations we can see for each possible pair.
For the AA pair, we have 2 possible combinations, for AB or BA we have 4. For AD we have 2. We continue these calculations until the occurrence of all possible pairs are found.
| Pair | Column 1 score | Column 2 score | Column 3 score | Total |
|---|---|---|---|---|
| AA | 1 | 0 | 0 | 1 |
| AB or BA | 4 | 0 | 0 | 4 |
| AD or DA | 2 | 0 | 0 | 2 |
| BB | 1 | 0 | 0 | 1 |
| BD or DB | 2 | 0 | 2 | 4 |
| CC | 0 | 10 | 0 | 10 |
| DD | 0 | 0 | 1 | 1 |
| DE or ED | 0 | 0 | 4 | 4 |
| EE | 0 | 0 | 1 | 1 |
Note that the total sum is 26, which we can use to normalize our matrix.
| A | B | C | D | E | |
|---|---|---|---|---|---|
| A | 1 | ||||
| B | 4 | 1 | |||
| C | 0 | 0 | 10 | ||
| D | 2 | 4 | 0 | 1 | |
| E | 0 | 0 | 0 | 4 | 1 |
Calculate scores
To obtain integer values for our scoring matrix, we need to find the score per cell. We can do this with the following equation:
Where qij is observed frequency and eij is the expected frequency.
cij is the cell value as calculated above.
To calculate the Total T:
Where w is the number of columns and n is the number of sequences. With T, we can calculate qij, which is the rate of change of residue i to residue j.
In our case, T = 30, so let's divide all our cells by 30.
| A | B | C | D | E | |
|---|---|---|---|---|---|
| A | 0.0333 | ||||
| B | 0.133 | 0.0333 | |||
| C | 0 | 0 | 0.333 | ||
| D | 0.0667 | 0.133 | 0 | 0.0333 | |
| E | 0 | 0 | 0 | 0.133 | 0.0333 |
Now pi can be found with the following equation:
pB = ( 1 + 8/2 ) / 30 = 0.167
pC = 10 / 30
pD = ( 1 + 10/2 ) /30 = 0.200
pE = ( 1 + 4/2 ) / 30 = 0.0133
The expected frequencies:
eij = 2pipj (i ≠ j)
| A | B | C | D | E | |
|---|---|---|---|---|---|
| A | 0.0178 | ||||
| B | 0.0444 | 0.0278 | |||
| C | ? | ? | 0.111 | ||
| D | 0.0533 | 0.0667 | ? | 0.04 | |
| E | ? | ? | ? | 0.04 | 0.01 |
Notice how I didn't calculate cell values that had a value of 0 - you'll see that we don't need these values in the actual scoring matrix.
Now we have all we need! Just plug in values from the two matrices above into the equation below to obtain our scoring matrix.
To obtain scores, we multiple sij by two and round.
| A | B | C | D | E | |
|---|---|---|---|---|---|
| A | 0.9 | ||||
| B | 1.58 | 0.0278 | |||
| C | 0 | 0 | 0.111 | ||
| D | 0.0533 | 0.0667 | 0 | 0.04 | |
| E | 0 | 0 | 0 | 0.04 | 0.01 |
References
What is a blocks database?
BLOSUM Matrices Lecture. IA State.
Columbia CS Department.
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